Semiconductor_Wafer_Defect_Classification.ipynb

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        "# Classification of Semiconductor Wafer Defects\n",
        "\n",
        "Lukas Y. Peng, Iago Leal de Freitas\n",
        "\n",
        "Professor David E. Bernal Neira\n",
        "\n",
        "November 18, 2024\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Introduction\n",
        "\n",
        "The semiconductor industry is developing rapidly. Data science and machine learning techniques are being utilized to detect defetcs and to better control their processes in semiconductor manufacturing. In this project, a convolutional neural network model is rewritten in Python to detect and classify defects on wafers."
      ],
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        "### 1.1. Background\n",
        "\n",
        "In semiconductor manufacturing, a die is a small piece sliced from a silicon wafer. After circuit manufacturing processes such as CVD, PVD, lithography, etc., semiconductor wafers are diced into pieces containing each circuit. These small pieces are called **dies**.\n",
        "\n",
        "However, in the semiconductor manufacturing process, even the smallest imperfections and impurities may interfere with the processes and cause die failure. Equipment malfunctions, chemical stains, or human failures etc. can all cause different failures. For example, scratches may be caused by machine handling, while center and edge-ring defects may be caused by etching problems and chemical deposition. These **failures** need to be identified to streamline the manufacturing process for efficiency.\n",
        "\n",
        "To do so, wafers are tested and a **wafer bin map (WBM)** is produced to provide information on the quality and functionality of each die. A WBM displays which dies on a wafer has failed, while showing which ones are functional. In wafer map dataset experimented with in this project, a ternary numeral system represents 0 - non wafer pixel, 1 - functional wafer, and 2 - faulty wafer. In industrial practice, scientists and process engineers identify defects on wafer bin maps to manually sort defects on certain wafers and which defect classification (nine classes--further specified in *2.2 - Data Attributes*: no failure, near-full, edge-ring, loc, edge-loc, scratch, center, random, and donut). However, manually sorting defects is both inefficient and costly.\n",
        "\n",
        "To recognize wafer defects more efficiently, various machine learning-based (ML) approaches have been developed. A github tutorial approaches the data set using ML-based models including **(1)-Support vector machine (SVM), (2)-Logistic regression (LR), and (3)-Random forest declassifier**. **Convolutional neural networks (CNNs)** have also been used to approach a more efficient classification process."
      ],
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        "### 1.2. Objective\n",
        "\n",
        "This project follows a CNN model as seen in MATLAB, which utilized MATLAB's image processing and deep learning toolboxes. This project follows the same overall steps to construct a CNN; however, the TensorFlow neural network model will be used in Python.\n",
        "\n",
        "The process follows the following overall main steps:\n",
        "1. Dataset Description\n",
        "  - Source and Processing\n",
        "  - Attributes\n",
        "2. Preprocessing and Augmenting Data\n",
        "  - Oversampling\n",
        "  - Partitioning into Training and Testing\n",
        "  - Resizing\n",
        "3. Setting up the Network\n",
        "  - Network layers\n",
        "  - Network options\n",
        "4. Analyzing the model\n",
        "  - Confusion Matrix\n",
        "  - Precision, Recall, and F1 scores\n",
        "  - Precision-Recall curves\n",
        "\n",
        "This project will analyze tensor flow results and compare them to the results ran by MATLAB scripts using its respective toolboxes. Additionally, results can show the differences between the ML-based models found by Xi Chen and Wu et. al.\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## 2. Dataset Description\n"
      ],
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        "\n",
        "### 2.1. Data Source\n",
        "\n",
        "The experiment is conducted using the real-time wafer map dataset WM-811K. The data is available on Kaggle: https://www.kaggle.com/qingyi/wm811k-wafer-map. This is a public dataset from the Multimedia Information Retrieval (MIR) lab (http://mirlab.org/dataset/public/).\n",
        "\n",
        "This dataset contains 811,457 wafer maps collected from 46,393 lots in real-world fabrication. The dataset includes nine regular wafer map failure pattern types: Center, Donut, Local, Edge-Local, Edge-Ring, Scratch, Random, Near-Full, and None.\n"
      ],
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        "### 2.2. Data Attributes\n",
        "\n",
        "The data set comes as a dataframe with 811457 entries and 6 total columns. These aspects of each WBM include: *waferMap* - a pixel by pixel 2D array that represents the WBM; *dieSize* - irrelevant measurement of how big the WBM is; *lotName* - lot containing said WBM; *waferIndex* - a WBM's entry in its respective lot; *trainTestLabel* - labels WBM as either training or testing data; and *failureType* - none for no failure and either of the other 8 failure classifications."
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            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 811457 entries, 0 to 811456\n",
            "Data columns (total 6 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   waferMap        811457 non-null  object \n",
            " 1   dieSize         811457 non-null  float64\n",
            " 2   lotName         811457 non-null  object \n",
            " 3   waferIndex      811457 non-null  float64\n",
            " 4   trianTestLabel  811457 non-null  object \n",
            " 5   failureType     811457 non-null  object \n",
            "dtypes: float64(2), object(4)\n",
            "memory usage: 37.1+ MB\n"
          ]
        }
      ],
      "source": [
        "# Read data file\n",
        "\n",
        "import pandas as pd\n",
        "#{Following code block is for Google Colab use\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "file_path = '/content/drive/My Drive/Colab Notebooks/LSWMD.pkl'\n",
        "df_init = pd.read_pickle(file_path)\n",
        "df_init.info()\n",
        "#}\n",
        "\n",
        "#If dataframe is saved locally:\n",
        "#import pandas as pd\n",
        "#df = pd.read_pickle(\"./data/LSWMD.pkl\")\n",
        "#df.info()\n"
      ]
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              "                                            waferMap  dieSize lotName  \\\n",
              "0  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...   1683.0    lot1   \n",
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              "3  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...   1683.0    lot1   \n",
              "4  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...   1683.0    lot1   \n",
              "\n",
              "   waferIndex trianTestLabel failureType  \n",
              "0         1.0   [[Training]]    [[none]]  \n",
              "1         2.0   [[Training]]    [[none]]  \n",
              "2         3.0   [[Training]]    [[none]]  \n",
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      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import random\n",
        "\n",
        "# display dataframe format and categories\n",
        "df_init.head()"
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "The waferMap column contains the wafer bin map, pixel by pixel. For simplicity, each die on the WBM is considered failed if it fails for at least one test, and otherwise it is considered functional. If a die on a wafer fails, it is marked as 2 or it is marked 1 if it passes. Therefore, each pixels has a categorical variable that expresses\n",
        "- 0 : not wafer\n",
        "- 1 : normal\n",
        "- 2 : faulty\n",
        "\n",
        "Identifying wafer defects requires only the waferMap and its failureType. Thus, we can drop the other four WBM attributes without affecting the data analysis."
      ]
    },
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              "                                            waferMap failureType\n",
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      "source": [
        "# Correct columns & remove unnecessary information\n",
        "df = df_init.copy()\n",
        "df = df.drop(['waferIndex'], axis = 1)\n",
        "df = df.drop(['lotName'], axis = 1)\n",
        "df = df.drop(['dieSize'], axis = 1)\n",
        "df = df.drop(['trianTestLabel'], axis = 1)\n",
        "df.head()"
      ]
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        "For the WBMs that have no failureType label, we can replace failureType for those with an empty value, later removing them from the dataset. Now, we can see the data distribution of our failureTypes as shown below. Making the dataframe categorical is easier to separate and show data distribution."
      ]
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        {
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          "data": {
            "text/plain": [
              "failureType\n",
              "none         147431\n",
              "Edge-Ring      9680\n",
              "Edge-Loc       5189\n",
              "Center         4294\n",
              "Loc            3593\n",
              "Scratch        1193\n",
              "Random          866\n",
              "Donut           555\n",
              "Near-full       149\n",
              "Name: count, dtype: int64"
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>failureType</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>Edge-Ring</th>\n",
              "      <td>9680</td>\n",
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              "    <tr>\n",
              "      <th>Edge-Loc</th>\n",
              "      <td>5189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Center</th>\n",
              "      <td>4294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loc</th>\n",
              "      <td>3593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scratch</th>\n",
              "      <td>1193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random</th>\n",
              "      <td>866</td>\n",
              "    </tr>\n",
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              "      <th>Donut</th>\n",
              "      <td>555</td>\n",
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              "      <th>Near-full</th>\n",
              "      <td>149</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Create a copy of dataset\n",
        "\n",
        "df2 = df.copy()\n",
        "df2.failureType = df2.failureType.apply(lambda x: x[0][0] if len(x) > 0 else float(\"NaN\"))\n",
        "df2['failureType'] = df2['failureType'].astype('category')\n",
        "\n",
        "# Check falure type distribution\n",
        "# Check failure type distribution of wafers with non-null labels\n",
        "\n",
        "df2['failureType'].value_counts()\n",
        "#df2['failureType'].value_counts(dropna = False, normalize = True).plot.pie()\n",
        "#df2['failureType'].value_counts().plot.bar()\n",
        "#df2['failureType'].value_counts().sum()\n",
        "# There are 172950 (sum)\n",
        "# df2.shape[0] -> 811457"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5sgGqdg0Jkt"
      },
      "source": [
        "We can also see that out of the entire dataset, only 172,950 images with manual labels (in total 9 labels: none, Edge-Ring, Edge-Loc, Center, Loc, Scratch, Random, Donut, and Near-full). 78.7% wafers have no label based on the failureType attribute. Among labeled wafers, only ~15% wafers (25,519 wafers) have real failure patterns while 147,431 wafers were still labeled none.\n",
        "\n",
        "As the MATLAB distribution does not remove any data to cancel out any potential noise or bias from the analysis, we won't as well. Below, the empty failureTypes which make up a large portion of the dataframe are removed. Additionally, a numeric failureNum is mapped to the preexisting failureType column to fit tensor flow categorical requirements.\n",
        "\n",
        "**Note**: It should be noted that the Python tutorial using SVM, LR, and RFC by Xi Chen continues to remove near-full failureTypes as it does not make up more than 1% of the data set. Additionally, smaller data sets not making up larger than 5,5 dimensions are removed (this is only about 2 WBMs)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "i0nRhkyZ0Jku",
        "outputId": "121c64aa-667b-4913-cfa6-4857af78749d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-487df7ad23ed>:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df2 = df2.replace({'failureNum':mapping_type}).infer_objects(copy=False)\n",
            "<ipython-input-9-487df7ad23ed>:6: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.\n",
            "  df2 = df2.replace({'failureNum':mapping_type}).infer_objects(copy=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "172950"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Delete unlabeled data (and \"near-full\" type)\n",
        "\n",
        "df2['failureNum'] = df2.failureType\n",
        "mapping_type = {'Center':0,'Donut':1,'Edge-Loc':2,'Edge-Ring':3,'Loc':4,'Random':5,'Scratch':6,'Near-full':7,'none':8}\n",
        "\n",
        "df2 = df2.replace({'failureNum':mapping_type}).infer_objects(copy=False)\n",
        "df2['failureNum'] = pd.to_numeric(df2['failureNum'], errors='coerce')\n",
        "\n",
        "df2 = df2[(df2['failureNum']>=0) & (df2['failureNum']<=8)]\n",
        "\n",
        "# removes 149 occasions. shown above ^\n",
        "#df2 = df2[(df2['failureType']!='Near-full')]\n",
        "\n",
        "df2['failureType'] = df2['failureType'].cat.remove_unused_categories()\n",
        "\n",
        "# Filter out filter maps that are too small\n",
        "# size_filter = [all(np.greater(df2['waferMapDim'].iloc[i], (5,5))) for i in range(len(df2))]\n",
        "# df2 = df2[size_filter]\n",
        "df2.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oagFAIY40Jkv"
      },
      "source": [
        "We will visualize the some wafer bin maps to see what the failure patterns look like. The dark purple region is the region that is not a wafer, the green color in the figure denotes that the die on the wafer has passed, while yellow denotes that the die has failed. This gives us a visual idea of what each failureType may look like on their WBMs.\n",
        "\n",
        "**Center** - There is a small, dense bloc in the middle of the wafer that contains failing dies.\n",
        "\n",
        "**Donut** - A ring, either thin like a small line or thicker like the shape of a donut consisting of failing dies surrounds the middle of the wafer.\n",
        "\n",
        "**Edge-Loc** - A region of faulty dies lying on the edge of the wafer, either spread out like a crescent or collected like a bloc.\n",
        "\n",
        "**Edge-Ring**: A small thin etched line forming an outer ring of faulty dies.\n",
        "\n",
        "**Loc**: An area of connected faulty dies, neither on the edge or the center of the wafer.\n",
        "\n",
        "**Near-full**: Almost all dies are faulty\n",
        "\n",
        "**Random**: No clear pattern of faulty dies; however, faulty dies still make up a majority of the WBM.\n",
        "\n",
        "**Scratch**: A thin line running from one edge to edge like a thin, hair-sized line of faulty dies.\n",
        "\n",
        "**none**: Mostly performing correctly with no regional pattern of faulty dies."
      ]
    },
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          "data": {
            "text/plain": [
              "<Figure size 3000x3000 with 45 Axes>"
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qVbelOs6adtppp3jyySerLevXr1/a9efOnRsVFRW1lq9duzYi1v/jzKcRI0bE888/H5WVldWe3PrHP/4RHTt2rNV2KyLiwQcfjKVLl6ZsZxixvkXW+++/H5988klstdVWVctnz54dEVGrHRYA/yUHN10Ojlh/EfQ///M/8fTTT8fvf//7GDNmTK11Zs2aFWvXro299tqr1mu//e1v47e//W3cf//9cfjhh6fdz4a2jRtyYHl5eeywww7xyiuv1Fr3H//4R2y11VZVU9GNHDkyIiI++eSTauutWbMm5s+fX23MESNGxMsvvxxr1qypVvUvBwPUTw5u2hycyoan3BYvXlznekmSxMyZM2PnnXeuWjZixIiIiHjllVfiK1/5StXyV155JSorK6tezyavZ5qDAWgcObj4cnA216yZ5uB0UuX1jd11112RJEnae9ENGROA9eTg0r4X3ZAcvHr16rjvvvti3333zagwNJ1Zs2ZFRMQRRxxR67VPPvkkBg0aFJMmTYpvfvObDd5HIZXXvwqZ+MpXvhLr1q2Lm266qWpZRUVFXH/99dXW69+/fwwfPjx++9vfVs11HLG+Lc+bb75Zbd2jjz46Kioq4kc/+lGt/a1bty4WLVoUERHDhg2LAw44oOq/DTd56nLJJZfEggUL4tZbb83mMNMaN25cfPLJJ/Hggw9WLVu1alVG43fv3r1a/AcccEDVPJepbL311jF37txq1XsR6z9MR0TePxiPHz8+5s6dG3/84x+rls2fPz/+8Ic/xCGHHJJyDq4777wzOnbsGF/96ldTjrlhyp1f//rXVcsqKytj8uTJ0aNHj4zeU4CWSg5uuhwcEXHuuefGPffcEzfeeGPKD8gREcccc0zcf//9tf6LWP9+3X///bH77rtHRMS8efNqbb906dK49tpro1evXtXO6fjx4+Pll1+udhPv3XffjWeeeSaOOuqoqmX77rtv1dMBG9oYR0TcfvvtUVFREWPHjq1aNmHChKioqKj2lMCqVatiypQpMWzYsEZdSAA0d3Jw0+XgVPkyYv01ZFlZWeyyyy51rnvTTTfFvHnz4sADD6xa9sUvfjF69OhR7f3bsG7Hjh3joIMOiojs8no2ORiAhpODizMHZ3rNmmkOTrf/VHl9Y3feeWcMGDAgvvCFL2R8TPWNCcB6cnBp34vOJgdv8Mgjj8SiRYuyKuZM5Ytf/GLKOHv37h277rpr3H///XHIIYc0ah+FpNNJhh599NF45513ai3fc889Y6uttopDDjkk9tprr7j00ktj5syZMWzYsPjjH/+Y8mmjH//4x3HYYYfFXnvtFaecckp8/vnnccMNN8Tw4cOr/eEZM2ZMnHHGGXH11VfH1KlT40tf+lK0adMm3n///fjDH/4Q1113XYwfP75Bx/PlL385hg8fHj//+c/j7LPPzqrNXipnnHFG3HDDDXHsscfGxIkTY9NNN40pU6ZU/bGoObdXY5xzzjkxefLkOOSQQ+Lcc8+NgQMHxl/+8pe46667YuzYsVV/OCLWzy12xx13RERUfdi/8sorI2J9Nd6JJ55Yte5DDz0Ur7/+ekSs75ryxhtvVK176KGHxo477hgR6y8eRo8eHaecckr85z//iV69esWNN94YFRUV8YMf/KBWvAsXLoxHH300jjzyyFpzvG1w2GGHxf777x9XX311zJ8/P3baaad44IEH4m9/+1vccsstKQtZAFoKObhuTZmDr7322rjxxhtjjz32iI4dO8bvfve7aq9/9atfjU6dOsW2224b2267bcoxBg0aVK3DyS9/+ct44IEH4pBDDokBAwbEnDlz4v/9v/8Xs2bNijvuuKNa95Gzzjorbr311jjooIPioosuijZt2sTPf/7z6Nu3b1x44YVV67Vr1y5++tOfxkknnRT77LNPnHjiiTFr1qy47rrrYu+99652gXLGGWfEbbfdFmeffXa89957MWDAgLjjjjviww8/jIceeihHZw6gNMnBdWvKHHzVVVfFCy+8EAceeGAMGDAgFi5cGPfdd1+8/PLLce6551abP3vgwIExYcKE2GGHHaJ9+/bxt7/9Le6+++4YMWJEnHHGGVXrdejQIX70ox/F2WefHUcddVSMGzcunn/++fjd734XV111VdXc4Nnk9Wxy8OLFi6tuzL7wwgsREXHDDTdEt27dolu3bnHOOefk7PwBlBo5uG7FmoMzvWbNNAdHZJ7XN3jrrbfijTfeiEsvvTTtech2TICWRA6uW6nfi84mB28wZcqUaNeuXRx55JFpY83kO+YBAwaknMb9m9/8ZvTt27fOruAlIaFOkydPTiIi7X+TJ0+uWnfBggXJiSeemHTt2jXZZJNNkhNPPDF57bXXaq2XJEly9913J9tuu23Srl27ZPjw4cmDDz6YHHnkkcm2225bK4Zf/epXyciRI5MOHTokXbp0SXbYYYfk4osvTmbPnl1v/AMHDkwOOuiglK/dfvvt1WJ79tlnk4hInn322ap1xowZk2y//fa1tj3ppJOSgQMHVlv2wQcfJAcddFDSoUOHpHfv3smFF16Y3HfffUlEJC+99FK9sWbjnXfeScaPH59sscUWSZs2bZKBAwcmF110UbJ8+fJq6204plT/jRkzptYxZfI+J0mSLFy4MDnttNOSnj17Jh07dkzGjBmTvPzyyyljvfnmm5OISB588ME6j2np0qXJxIkTk379+iVt27ZNdthhh+R3v/td1ucGoLmQg4svB9eVKyMimTFjRp3bR0Ry9tlnV1v2xBNPJGPHjk369euXtGnTJunWrVvypS99KXn66adTjvHRRx8l48ePT7p27Zp07tw5Ofjgg5P3338/5bp33XVXstNOOyXt2rVL+vbtm5xzzjnJkiVLaq03d+7c5KSTTkp69OiRtGvXLtl9992Txx57LLOTAtAMycHFl4OfeOKJ5OCDD0769++ftGnTJunSpUuy1157JZMnT04qKyurrfu1r30tGTZsWNKlS5ekTZs2yZAhQ5JLLrkkZQ5MkvXneptttknatm2bDB48OJk0aVKtMVNJldc3yCQHz5gxI+3vWM3zDNBSyMGlnYOTJLtr1kxycLZ5/dJLL00iInnjjTfSHlO2YwK0BHJw8eXgfNyL3iDT6+DFixcn7du3T4444ogGx1rzd6Kmut67UlKWJElSX2EKTWPEiBHRu3fvWvNZlbJrr702zj///Pj4449js802K3Q4AJCSHAwAhSEHA0BhyMEAUBhyMM1ReaEDaInWrl0b69atq7bsueeei9dffz323XffwgSVAytXrqz286pVq+KWW26JoUOH+gMDQFGQgwGgMORgACgMORgACkMOpiVpXegAWqJPPvkkDjjggDjhhBOif//+8c4778TNN98c/fr1izPPPLPQ4TXYEUccEQMGDIgRI0bE4sWL43e/+1288847MWXKlEKHBgARIQcDQKHIwQBQGHIwABSGHExLouikALp37x4jR46M2267LebNmxedOnWKgw46KK655pro2bNnocNrsHHjxsVtt90WU6ZMiYqKihg2bFjcfffdMWHChEKHBgARIQcDQKHIwQBQGHIwABSGHExLUpYkSVLoIAAAAAAAAAAAKC3lhQ4AAAAAAAAAAIDSo+gEAAAAAAAAAICsKToBAAAAAAAAACBrrZt6h2PLj2rqXbZo0yaNbvQY0yfcXO86g+85s9H7aawh579U6BCo4cnKPxQ6BKCR5O38eXz21EKH0GTG9R9R6BBKmnwKbCAv50ep5GT5ND/kWWh55NOmkeyxUywb0KHasrZLK6LDJ8vWv966PBb9aHWcO/jZlNtf9eaXY/VHndev231tXLr7o/HYvO3j9VcHxzYjZsXx/f8REREzVveOX7+wT5Sv2uj52rKIfUe/FV/s/nZERPxj6Vbx5xdGRlll+njbD1galw9/rM5j+u6T42ObW5fEys06x5ouraq91uXDFREvvVHn9uSG3A2lTy4urFK5Bt6Y6+HCyiT3NnnRCQAAAAAA0HzNPC9i6t6/qLbs2oU7xP97cr+IZP3PyXtd4rvvp/7iceMCkbLP28Q1TxwaZf//du++PiC++8aA9a8lEeVJjY2TiOdeGh7Plg2vWqes5jo1rJrVJb77cd1fgpZVRrz/P5vE17/0dJzb/d/VXtvxL2fEYM9EAgAtlKITAAAAAAAgc2VlseC00bFkyPof2ywpi57/XhdlFet/XruiMs6fPabaJv9euGkkGzUkiQyKQap2t3GXkky2y2LsiIik55rYdasPY+rHm0XFnI7p1yuPeOiTHeKDlb2qLa9Y2SpWHTwqFg1pHSv7rd9x28/LYosb34zKpUszDwQAoAQpOgEAAACajWxbBRfDdLGZmD67/qlvN6b9MAA5VVb23/+fJBFl5dFlwux4Zfs/RUTEwyvax7mPnhRl69avV76kPJ56YadCRNogm/ZZFL/f6uk4onJsvD5ncJ3rznm7T8yJPtWWlUfEx/tHHLvf83FlnzcjIuJXi/vHn+7cZX3Rycbnb4Mki6oYAIAipugEAAAAAABIqfXALeK9q3tG902WR0TEgnd7Rrd3ymLhR2tjj3VHRkTE0pXto6wiRWFFiZj9SY/YIzky5n62STTmKO75z8h4es7WERGxaFnHaH9Yl1jVc0B0GTWv2noLF3eKbS6ZH+s++rgRewOgJcv2gYtS1tBj9TBG01F0AgAAAAAARHn79lHevdv6H5IkklWroqJPt/jpbvfGnu3nRkTEed0OiZfLto7yxa3js8W9CxdsDpUvaR2fLendqIKTiIjKue3js7ntq35eM7gyNhn8efx5x9urrffiqr7xy4Hjo+26dRERkSxfERVLljRy7wAAhaHoBACgHi2pajwf8nX+VKoDAADk1oIJO8eJFz8SERGrktZx07MHRLsFreL8v02I8taVERFRubxNlBcyyBKz6KNuMXrRuVFW9t/pdFq1qYhjf/ly9Gq9LCIirn/oKzHo0r8XKkQAgEZRdFJA0yaNLnQIGWmK+a2nT6h/bur64miK8znk/Jfyvg8AAAD+K9vixaa4hi2EbI9r+uz6r7M3ppgToGUqa906KnfbPpK25dFm9uJY2acshrVfP+XLqqRNlPdcHWvWtY/yRW2qtlFwkp2yNWURC9pWW7a2Q2Vs1vbzGNx2ffeYtT3WRashg9a/WF4eazftGmVrKqPsH29FVFY0dcgAAFlRdAIAAAAAAC1Qeffusf0Nb8ZunWfEtx+ZEGWVlfH1R79W9XrZusZOOEMq5SvL45qnD/nvz2vL4v2v94uIiMp2SVz9lbvixaVD470vbRIVCxYWKkwAmkBTd9kuxQc1MmlekEpDz62HMrKn6AQAAAAAAFqIZM+dYs6enWKTmRXRemVlvLGofcxb0zmS1knEurIoW6vQpCnUPM+VbddPv5O0SuLPC3aKFevaxoenbx3la9e/vukLy6Ps7683dZgAAPVSdAIAAAAAAC3ErHGd4p2v3xhfeOOI+Og/fSLe2Cw+iM1CqUlxKFtXFi+8NCzK+qyOl8/6WXRv1TEiIrbrfFYM+HuBgwMASEHRCQAAAFAQmbS6zXXr35pteUuxtXAmsj2u6bOrnxfthAGaj1a9e8c7398qIono/XJ5VG6z7L8vqjQpWhVL28T4d4+JNuUVERHRdsTnMX3KzhERUbmgbWz7w2lRMX9BIUMEAIgIRScAAAAAANCslLVpG2Xt20WyZk1Ej03ix2N/HxVRHt+tPCqG9ZsXKyrXREWi4qSYla8sj5lv9q/6+ctfeC1+sunzERFx//JNY8otB0T56jUREZGsWh3J2jUFiRMAQNEJAAAAAAA0Ix9fuGvsc+S/4om/7hzt5pfHt18+PCKJKKuM+Pd7m8cun50Wqxd2iPJCB0rGHn5zh3hqxtYREdGmTUWM+vWs6NBqbURE/OUPu0b/n7xYyPAASCGT7p4N1Vy7dmaq5vHX7Gqaap1069WUyfumO2h1ik4AgBYhnx/wm4u6LlTq+zDemG0bqq731Id+AACgJSlr1y7KthkUZesqo+zzJbFi84o4rddf49kBQ2NVeaeIee2q1i1f3irWLu+k4KTElC9uHWsXr/9KZ3WXdXHEDq/EFq0XRUTEw4N3jLKdt4+IiLKKikjenRHJ6tWFChUAaGEUnQAAAABNombBYCZPZtUsXmzs01wNfRqqLtnGmOtjyoVa52V2/UWjijwBikfZdoPjq3c9F1OXDYinn9g5ytYmMf7xc6JsTVmYRKf5KV/WOs556n+qft552Iy49I+PRETEv1ZuGX86dp9IXn+7UOEBAC2MopMGyuRp6WK4aVQqmupcNfpJ6wmNj8FNOQAAAAAgJ8rKotV2Q2PJkC4xfVWfmL+6U6zrmERERPlKvUyaraT6+zt72Sbxr5VbRkTEx2t6xMdf6h7dtxwVHR/+VyTr1hUoSACgpVB0AgAAAAAAJaisbdv46KBesWLTyrj3udGFDocCmfdur/jpe4dERERlh4q496xrY/L8vWP6Mx0iWbq0wNEBNE8Nnc69uTQtaMoOnpmMnen+G9JxNZVM3v+W1IhA0QkAAAAAAJSIdfuPjBmHtY5N3msVHedWxNouSZhDh1jf4CbK1pbHd2ceHh8t6hZdvtI1VvQpjyVDK2LQA+ui9TOvFjZGAKBZ0l8PAAAAAABKQXmr+HRUu3j/yJui11c/ik/3LFtfdNISlYVimxTK1pbFu1MHxIqZXWPu6Ii+h8+K94+8KT7btV1EeatChwcANEM6nQAAAAB5UbPdbEPa7ea79XAuxs92jFJop5xRy+HZ1VsOt6TWwQCFULbr8Fh65YroWTYndnv12Ph8XpcW+1Rp0iqJr+wxNXq2XRa/fWGvKF/dUs9E/d6b2S92W35sdN9/Tiz74sDoclmHSF77d6HDAgCaEUUnAECz0dB5NEtNvr6oqmuuysbss6Hb1jd3Zl3j1vwSLBu+MAMAAIpFq759oqxD+0iWLY9lm3WMSdtMjt/O/0I8+redW2zByQY7dv4oBrRZEL8t36vQoRS18iWtY/GS7vGFvV+N43q8FJds9o3o9FHPKOvcKZKVq6Ji7meFDhEAKHGKTgAAAAAAoNiUlcW7P9ssvrnLM3HtE1+OVivLYsIz34hYW97iC07KKsri6r8dFFGWRPmqln42MvPQKzvHQ212irZ7tIp1+w+Nb37p0Zj06v4x9KR5EUkLnaIJII1cPdyY6qG5mg/alUInzFRKNe5MZNT5s54HJiNq/x4154cdFZ0AAAAAAEARabXNkFi2XY/o02tBdCxfHZVd10VE6yhf4pb+BuXLWhU6hJJSvrI8YmV5rOuYRGXXddGl1cro02tJrDx0t+j8zsKoeHdaoUMEAEqUT6hpTJs0up41pjZFGEUhk0qtUqlmK4Y4G9Puf4PmXAkHAACUrppP8dS8BmtO15ekeH9rXO+6dgVouGkn9Y4XT/xZfOWNk+OqJw+L8oqyQodEM1K+pHX88ImvRp8hC+KPN0yKvX5zUWz5HUUnAEDD6DsHAAAAAABFoNWwreOj7+4ZyZYr4+rP9o6FizpHmYIT8qCsoizmL+ocV3+2d1RstTI++s6e0Wq7oYUOCwAoQTqdAAAAAABAEfh8RI947czr4rxP9okH/jqq0OHQzCVz28cDc0fFAXu9HpP2+Usc8P550eXt9wsdFkCTqtmxM51cdeXU3bN5yOR9rNntNdPftVLsGqroBAAAAAAACqCsTduY/qORkWyxKrq80CFWd4/Y/80J8enCroUOjRbk6WnbxIGf94uVxy2KOeN2jWHfnR3rPpld6LAAgBKh6AQAKBn1VQLXVV1cs6o4G/moPq8vnmKreM9XvHWNm69zUN+402enj6kUq8wB8qlmbq7vb2yx5Tdyq+b7WzOnyqMA1ZV37Bjl3bvFbnu/E+dt+lQcu/zMKF/eKua83afQodHCJHPbx8fz2sfFYx+KfsMWx02bfTVarVq1/rWVq6JyxYoCRwgAFDNFJwAAAAAA0MRmXLpTTDjsr/HQh8Pj+A++FmUrWxU6JFqwssqI//3HgdG+85r4yk3/jq6t1xed3PXAvjHw+y8WODoAoJiVFzoAAAAAAABoKVr17RPJXiNi7aBVsV/nt2PZ8vYR89tFWWXu91W5ybpIeq6JxDcBZKB8UZtYtbB97NL5wxjX5c0Y1+XNWDNoVSR7jYhWvXsXOjwAoEjpdAIAAAAAAE1k7mGD43eX/198a+aRccrjX4uyteVRlo8dlUVcNPrxGN1heox/+qwoW+rrAOpXvqJVfOfpI6t+3n7YR/HTKffFiVdeGD1vm1fAyAByo74p3CNMDVtsUk3RXgrvUSYxpjq2mr+jpTBVrU+ZAAAAQEZq3vgohZs8LUXNG1XF8N7UjGH67OoxlsKNM4BcatW3T8w9bHB8vmNl/HnZDvHRom5Rvjq/LUj+snDrmNWpZ8Q6rU7I3Ma/l58s3iT+vGyHWLhTZcTX94g+D0yLinmKTwCA/2qxRSfTJo1u1PbFcPOmqbSkY20KuTifNW/U1eTGHQAAAAAUl3VD+sdd3/5Z/GnpjnHzk2Pzv8Mk4tWXh8arMTSUnNBQSz7oFjd/MDbOGftEjDvk33H+v8+MMkUnAMBGWmzRCQAAAAAA5Ft5x47xwWU7xbotV8UFM8bHR4u6FTokyNod00fFM5tsE9O/UR5tDtwjBl3zelSuWFHosACAIqDoBAAoKnXNqVlft6hU8x9mIl9dvRoaT2PGbcyx1LVtvo4lX+c+X8dS1++nTmMAAEBNZa1bR3n3bnHcoX+JMZ3fiVMe/1qjptRJyiPKkohIchdjySiLSMqK/Pg3xFhZ6EByb8kH3WJR+67xm3G/iid22CFeu2mzSNasiWTdukKHBpBWXffyNkh1HzHV/cNa04dmsA650dLPa6rf42K7F63oBAAAAEip5o2N+m6yZXsjyE263CmF81br96fG1LHFdtMMIBemX7lb7Lb3O/GnD3eIKct3i/I1jSg46bkmrhj1YDzw2c7x+quDcxhlaei85eL43rCH45r3DoyF7/codDgp7bTL9Di8z2txxT8PjbIFbQsdTs6VrSmPr/3jpOjcaVVse+9n8cpfd4tBl/290GEBAAVmKkcAAAAAAMihVj17RKthW0eHbRfFN/s/EYsXd4yKTzs0qkNHeevK2L7d7OjdflnuAq0haZNEZdd1kbQtvlYiHdutiR3azYlObdcUOpS0erdfFju2+yTKWzfDViexvoNLxacdYvGSjvHN/k9Eu20XR6thW0ernsVZBAQANA1FJwAAAAAAkEMfnbJtfPPB+6Nr+9Ux4cmzIlnYrtFjVs5rH+OfOjueeHWHHESYWq9BC+OZA66NQdvMyds+Gmru9F4x7olvxqz3+hY6lLSeeHWH+OpT50TlvPaFDiWvkgXtYsKTZ0X3jivjmw/eHx+ftG2hQwIACsj0OgAAAAAAkAOt+/WNRfsMimWD18Wn6zaJRSs6RPnyVjkZu6wyomxZbsZKZ8XqtvH8yi1j0criK5ooW1sWZWvze/yNVb6qZTznW1YZUba8VXy+okPMq+ga5esKHREAUEiKTgAAAICIiHh89tRqPw++58y87q8h40+fcHOdY9T3OsWr5u/fuP4jChIHQGMs221g3POzn8XP5u0bVzw+PspKbJaVlR92iSs+Kr24KYwVM7vGd2cdFQPfLd4pj4CWpeY1RUNlch2ZyTo1r08z3Y6Wo6G/R8V2/dwsi06mTRpd6BAgr+r7AzR9du0/PjUV+o8PAAAAADQ3bRetjYs/OiTeWdAnyiojNhn8eWzdc15ERCxY1Smm/7t/lFWUFTjKuik4aZ66brUoBveYH6++PShn3Xfaf1Ye3d+viA4zPo+KnIwIAJSiZll0AgAAAAAATa3NguXxzxe3jaR1EhER47ecGpf3ejciIl5YVRknvHtm0Red0DwduuWbcUHPV2K3j8+MiuUdGjfY+l/v6DQniU73/kPBCQC0cIpOAIAm19A2h6nayG0sX60J69tvOo2Jp659FqIFo2Op35B4KS/jAgAAxa+8U6f4/Ks7xMre5RHl/20Vcvu/R8cDXXeMiIiVa9pE2dryQoVIgfXb7rM4ZotX47rX94tkbvsm3/+d/94t/txl+1i7qF009rew7aLy2PyZFdHm08UKTgAARScAAADQUtUsBM22MK8YiwebIqZCFcICUJxa9ewR0atHLBpaHmu7Vp+bpuLTDrHg0/92ldDjpHCS1kkkbZIoW1NWkG4zA7t8Hl/t8u+4pf0XYmU0fdFJ5dz2sWhu+0YXnERElK+NaPPuJ1G5ZEkORgPIn0Jfm+Vz/6muSwt9vORHqve1oQ/K5ouyagAAAAAAaIjyVvHe9QNi99//J1b3W1voaKhD/6Hz4t5xN0TPrT4vyP7//ubQ2Oe582LF7M4F2X8uremexMwzhsayQ0YUOhQAoAjodAIAAAAAAFlqNXSrWDm4ZwzsMzcGtfusRT/imbRNonWPVbF2RZsoX1KcXztUJGWxoKJTrKsozBtVvrI8YmXbguw71yrbJLG6ZxKru5RHp0IHAwAUXAv+GAwAAAAAAA3z3hl94je/mhSfr+gQVzx5ZJQva1XokAqmdc+V8bcv3BiHjZha6FDSmvte7zjzsVNj8cxuhQ4FAKBZKc6SYwAAACCnHp89tdYy8z03TK7PW825mFvK+1LzOGueh5q/s+P6j8hzRADZabegPH61cI9YvKRjlK0ry8mYlV3WxVZbfhYfzeseFZ92yMmYTWHtyjbxf/P3ilfmDSh0KGmVVUZEZW7eJ9Zb1bMsKvbbJdpN+yzWffRxocMBAAqkJItOpk0aXegQWpSaN31SaSk3xIpFfe9JJu/H9Nl1j+FmHgAAAACk1+Pdirjr2b1yOmbXXsvjie0eiLO67hVPfbpTTsfOp/JFbeLe59y3b2lWbFYZM/q3jYGP9Ys2ik6AJtbSH6zI6LvAFN8ntqRz1JKk+vfQlN/1lmTRCQBAKnUV5TXmw3QhPogXYp/N6fwVQqoP9hso5gQAgOajcszOMeP0iIrllVG+PLdT6iyZ3ykOfOew+HBe95yOC3lTFjF/h7bRsffo6PnMzFg359NCRwQANLHyQgcAAAAAAABFr6wsyjt1ioXbtI9Xx9wYu28/Pee7KF/aOj54Y7OomNMx52NDvizfvDLmjyiLyt7doqxduyjv0uW//3XsGGVt2kZ5p05R3qlTRJkpjgCgudHpBAAAAKCA8tEVrGYHs+bQeazQ7YIBWm09OFr/alkMKn8/Rr1wRqxZ0s5TnfD/S1olMevgHlExvnuMGvvv6N12WUREPPD2TtHxjQ7Rad/PolfH5VH29b5R8f4HBY4WAMglRScAAAAAAFCPpF3rOLjPGzF9VZ94bfYQBSewsbKIVb0ro7LLupjQ+5+xWavFERHx7uZ94+35A+Obg/4W27f7JH7Q8YQCBwqUqrqmtt4gV8X3qaYhL8VC/lKMmczUfG9T/c7W/DeTz4c2FJ0AAAAAAEA9yhcujZ88cmhUdKhUcAJplC9rHec89T9VP5etKwsT6gBA86boBAAAAAAA0ilvFSsPHRnLNm0VURlRvlrJCaSVRJSvrP1v5MmFw2J6xz7x8Ze6R/ctR0WHh16NqKwoQIAAQK75dAwAAAAAAGmUd+oYw7/9Rtx68XVR0cWX5NAQL/9z67jrn7vHHWdNiu2+/WaUd2hf6JAAgBzR6QQAyItM5thMJV/zTKaa0zDf+8yXfB1LIc5DXcdSn2L7Xanrtemz6z7OfM6nCbRcNXNxqeW7DXI1JzfFryHvbc0cK6cCebF2bTz27+3jgwE9o2xd7YlCNhs2N/p3Xhz/eGNIlK/ynCfNX2Xnithr+/fjvc97x4L3ema8Xdna8rh85ldj5bo28eFP+kXvl8qj2x1/z2OkAEBTaJZFJ5l8eVEMN6lKJc5iiCGi/vNVLHHWJxfve6kcKwAAAACUtLL1RSad3moX73++RdQqOSmLOGnA3+OLHafFF9/9ZkS2RSdlEZHkIE5oQq06rY1rt3g4bu48Mia/t2/G25WtLYt3pw6INpstj7cOuz6Gdzkzut2RvziB0pXJA435/K7M93CQnWZZdAIAAAAAAI21duzIWLhN21jdM01lSBJxzWsHxqT2a6Nseausxt5qx0/imP4vx9X/+nIkn7XLQbTQNCoWtY39Xvl6rFju9xYAiNDrDwAAAAAANlLepUu03mrLWDyoTSzdqjIq2qdvR1I5t32s/LBLyql36rJ5p0Wxd8fp0brNusaGC02qfHV5rJjZNWJew4pOKivK48N166K8VRKtt9oyyrt0yXGEAEBT0ukEAAAAKBo1pyTNtq1xY7dvCk0RYzEeN0ApmXv88Pj2BVPix+8eGDGtR1728dxr28Vz/94myla0qj1tDzRj6+a1j688fV5sNfCzOPWx5+Lqnx4fPW/9e6HDAgAaSKcTAAAAAACooVVZZV7HL19VHuVLWmfdIaWlSdom0Xbz5ZH0WFPoUNKq7FQR7bZYFpWdKgodSkkoqyiL8iWto12rdXFk5yWxtrN/AwBQynQ6AQAAAACAjXR/b01c8OjxUVbpy/BCa9V9dfxl9M3xiwWj465n9yp0OClttdXceGS7++Lgd46ID97YrNDhAECzVwpdTvMpk+Ovuc7js6fWWmdc/xE5iUfRCQBQVGp+ENpYYz44NmbbfMXUUKX2Abqu81eXQh1nQ/fb0OMEAACKR+stB8SM4zePdZ2SKKtIosPApbFlj4Xx7/c2j/LlrQodXou0bkXruOLT/eP1Bf0LHUpaHy/oFpd8ukfMmt+90KFkJem+NoYP+iTemd03Kj7t0OT7nzG/Z3yz666xZMfVse7yPWPLKR/Fug8/avI4AIDGUXQCAAAAzUDNJ1ZKrUhxg1KNOxst4RibSs1zOX129SLQXD21BbQcqwb3jifO+Enc+vnu8btn9o5DB70Z3+n9Suzy2WmxdnmnQofXIpUvbR2P/21EocOo07rZHeOh2bsWOoys9emzOB4c+lgc0/qL8fKnWzf5/td83Cke+njXOG3/5+J/9n8lTv37edFK0QkAlJyiKzqZNml0o8dw86Z5ai7va7EcR31x1LxRl4qbdwAAAAA0J20XrIxDp54Wy1e2i4iIe98dEX+ZOyRWL+wQ5QWODXJt7pxu8YXyI2LOvE0KHQoAUMKKrugEAAAAAACaVFlZtOrWLSrLymLp2z2ism0SEREVczrGp3M6KjihWSpf0jrmLOlT6DBiRUXbWFTZOtZ2ah1tu20SFYsWFzokoInV7NyZSrE81E1xaOm/D5kcf63OoHmcHt5nZQAAAAAAWrRWPXvEsru7xaY3zYqKDpWFDgdalLteHRXj/35G7PD912PJ3T2jVc8ehQ4JAMiCTicAAABAs1GKTzvVfNqoFI8hH5wXoCmVtWkTR23+rxjW/uP4S/mwiIpCRwQtR/nS1rFudasY3+PlGNJxbjzReutChwQAZEGnEwAAAAAAAAAAsqbTCQAAAAAALVqydm3c98nO8XHv7jFg67lRmZRFRMTHc3pE2edtChwdudS6/4ro0XV5fDqzZ5Sv8lxu0aiIuGPenvH+4t7Red3iQkcDAGRB0QkA0CCPz55a5+t1tT+v2Sr9/2PvvuPkKuvF8X9md5NNT0gICQECKRApYkBAepESQERBCFWKWGiKsaBy9aJXRQUuAfEr4L38QLlBQcBrQ0MREZAQWkJvgZCQkN57snN+f3CzsruzOzO7Mztl3+/XKy/YM2ee8zlnzu4z55zP83lyfV+ptDemStvPtrS1L9kUa187ElNb2htve895AACg9JL1G2LBw8Pid7v1jycPujH61/SMiIiju30sXl+6XYmjo5DO3vmJuGjgtPjw0gsiWdej1OHwf1INqfj747tFj4U10XvdglKHAwBdQvPnPOOGjW1XO5JOAAAAgC6jeSJgLsmG7XlPPiotGbWztOezAWiPeV/eP1bstiFSq9ORrOgep73+qehe0xAREa/P3arE0VFov3lzz3h08ahoWNE91DkpP2uHpOOV63aOfs93j6HX/bPU4QAAOZB0UkJuKgEAAAAAlFAqFfVHLowndr819vv7xZFaVB+vThte6qgootVv9Y9Xo7+EkzKVGrg+ph76szhm6LkR15U6GgAgF75XAQAAAAAAAACQN5VOAAAAAADoslatrY9XNvaOpKEmUqUOhoJI92qIqE0itbouUulSR0M+koaaeHVjz1i5pj4GljoYoGTMFtFUpmlF2zsdqWPbdeQyVXChpqyVdAIAAAAVaPLcaU1+duMoN+05To5t+cr22cyY2/QG2rhhY4sYDVCRkiS6/6NfnL34c5FaV1vqaCiEVMT5+/89Duz9apz50OcjtcJjkIqyvFucef8Xov8L3UodCQCQI9+2AAAAAADocmp2+0As231AbOgfUbNGwknVSCJeWT00uqUaIhrUrqk0qXREak1tbBgQseL0fWPAc8si/cIrpQ4LAGiDpBMAoNN1ldHC1bSf5bgvpYipveUG8x2FDQAAFN+bp24Rz55zXRw6/YxY/9qgUodDAf1jyq7xj9g1apJSR0J79dtvQTz4hdtjj1suiR1eKHU0AEBbJJ0AAAAAANDlJDURvWq6R21NutShUGiSTSpeKpVEr5ruETWljgQAyEbSCQAAAFAUzStElWPlLKpLe6uSAV1XQ5KOJDEFC5SjhiQtgQiq0OS500odQkm15zq5Uq+lM12fVeq+VKL2HOv2/n5KOgEAAAAAoMsZ8GrEvs+eGove7a+YApSZeXO2iH0bTo0Br5U6EgAgm05POnlj4r6dvckuLZcRPjLKyCSX82J0TOmESAAAAACg8Hot2hSzXx8Yqe5JpHumo2ZdjaoK8H+SmoikPh2pTalIbez8akA1K+tiycqBMXRdEnXbDIuGRYsjWb++0+MAALKTwA0AAAAAQJd1wr5Pxk1H3BLp/htLHQqUjW5D18Qd4/5f7DX2jZLG0f1z78aR970U6z66e0njAABaZ3odAAAAoCgKUVm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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot each failure type as examples\n",
        "sample_size = 5\n",
        "failure_types = list(df2['failureType'].cat.categories)\n",
        "num_cat = len(failure_types)\n",
        "fig, axs = plt.subplots(num_cat, sample_size, figsize = (30,30))\n",
        "for i_cat in range(num_cat):\n",
        "    cat = failure_types[i_cat]\n",
        "    random.seed(10)\n",
        "    sample = df2.loc[df2['failureType'] == cat].sample(sample_size)\n",
        "    for i in range(len(sample)):\n",
        "        index = sample.index[i]\n",
        "        axs[i_cat, i].axis('off')\n",
        "        axs[i_cat, i].imshow(sample['waferMap'][index])\n",
        "        axs[i_cat, i].set_title(f'{cat} - {index}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKAC3mm50Jkv"
      },
      "source": [
        "The figure above shows 5 random examples of each of the eight wafer map failure types."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Preprocess and Augment Data\n"
      ],
      "metadata": {
        "id": "-cVLUmRmIh1D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Balance Data by Oversampling\n",
        "\n",
        "The data distribution, as seen in 2.1 is heavily unbalanced. There are significantly fewer images in defect classes as there are with no defects. A CNN model may then overbias towards no defects. An effort to combat this is to oversample data so that there are a relatively larger number of each defect class image.\n",
        "\n",
        "To improve class balancing, an oversample function is written to cycle through every defect class (not being none) and is duplicated and appended to the existing dataframe. The oversample function appends the dataframe with 5 modified copies of each defect image with the following modifications: horizontal reflection, vertical reflection, or rotation by a multiple of 90 degrees."
      ],
      "metadata": {
        "id": "xb54s566mjV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "df3 = df2.copy()\n",
        "\n",
        "def oversample(df):\n",
        "    # Create an empty list to store the augmented data\n",
        "    augmented_data = []\n",
        "    valid_df = df[df['failureType'] != 'none']\n",
        "    sample = random.choice(valid_df.index)\n",
        "\n",
        "    for index, row in valid_df.iterrows():\n",
        "        wafer_map = row['waferMap']\n",
        "        rotated_90 = np.rot90(wafer_map)\n",
        "        rotated_180 = np.rot90(wafer_map, 2)\n",
        "        rotated_270 = np.rot90(wafer_map, 3)\n",
        "        flipped_horizontal = np.fliplr(wafer_map)\n",
        "        flipped_vertical = np.flipud(wafer_map)\n",
        "\n",
        "        augmented_data.append({'waferMap': rotated_90, 'failureType': row['failureType'], 'failureNum': row['failureNum']})\n",
        "        augmented_data.append({'waferMap': rotated_180, 'failureType': row['failureType'], 'failureNum': row['failureNum']})\n",
        "        augmented_data.append({'waferMap': rotated_270, 'failureType': row['failureType'], 'failureNum': row['failureNum']})\n",
        "        augmented_data.append({'waferMap': flipped_horizontal, 'failureType': row['failureType'], 'failureNum': row['failureNum']})\n",
        "        augmented_data.append({'waferMap': flipped_vertical, 'failureType': row['failureType'], 'failureNum': row['failureNum']})\n",
        "\n",
        "        if index==sample:\n",
        "            fig, axs = plt.subplots(1, 6, figsize=(30,30))\n",
        "            axs[0].imshow(wafer_map, cmap='plasma')\n",
        "            axs[0].set_title(\"Original\")\n",
        "            axs[1].imshow(rotated_90, cmap='plasma')\n",
        "            axs[1].set_title(\"Rotated 90°\")\n",
        "            axs[2].imshow(rotated_180, cmap='plasma')\n",
        "            axs[2].set_title(\"Rotated 180°\")\n",
        "            axs[3].imshow(rotated_270, cmap='plasma')\n",
        "            axs[3].set_title(\"Rotated 270°\")\n",
        "            axs[4].imshow(flipped_horizontal, cmap='plasma')\n",
        "            axs[4].set_title(\"Flipped Horizontal\")\n",
        "            axs[5].imshow(flipped_vertical, cmap='plasma')\n",
        "            axs[5].set_title(\"Flipped Vertical\")\n",
        "            for ax in axs:\n",
        "                ax.axis('off')\n",
        "            plt.show()\n",
        "\n",
        "    # Add augmented sample to original df\n",
        "    augmented_df = pd.DataFrame(augmented_data)\n",
        "    oversampled_df = pd.concat([df, augmented_df], ignore_index=True)\n",
        "\n",
        "    return oversampled_df\n",
        "\n",
        "df3 = oversample(df3)\n",
        "print(df3.failureType.value_counts())"
      ],
      "metadata": {
        "id": "56KmxpPQJevc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "1472df6c-b3a5-4d03-f7dc-53c2fa5823da"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x3000 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "failureType\n",
            "none         147431\n",
            "Edge-Ring     58080\n",
            "Edge-Loc      31134\n",
            "Center        25764\n",
            "Loc           21558\n",
            "Scratch        7158\n",
            "Random         5196\n",
            "Donut          3330\n",
            "Near-full       894\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A random defect class is gathered and displayed as to how the oversample function modifies it into 5 modified copies.\n",
        "\n",
        "Additionally from the value_counts() summary showing the distribution of each failureType, we can see that each defect class has been increased by 6 times its original. Although they are still relatively small compared to the no defect class, the percentage of no defects goes from 85.2% \"none\" failureType distribution to 49.1%, reducing the class biasing to \"none\" by a large factor."
      ],
      "metadata": {
        "id": "aizT0j1Sa3U9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Partition Data into Testing, Validation, and Training\n",
        "\n",
        "Split the oversampled data set into training, validation, and test sets using the splitlabels function. Approximately 90% of the data is used for training, 5% is used for validation, and 5% is used for testing."
      ],
      "metadata": {
        "id": "LQfJZmnAmk-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df3['waferMap'].values\n",
        "y = df3['failureNum'].values\n",
        "\n",
        "from collections import  Counter\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
        "print('Training target statistics: {}'.format(Counter(y_train)))\n",
        "print('Testing target statistics: {}'.format(Counter(y_test)))\n",
        "\n",
        "RANDOM_STATE =42"
      ],
      "metadata": {
        "id": "-DQam1dkmzGE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79cb13e7-a7c3-4a6e-8011-2e3563e54ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training target statistics: Counter({8.0: 110497, 3.0: 43488, 2.0: 23384, 0.0: 19343, 4.0: 16210, 6.0: 5415, 5.0: 3920, 1.0: 2492, 7.0: 659})\n",
            "Testing target statistics: Counter({8.0: 36934, 3.0: 14592, 2.0: 7750, 0.0: 6421, 4.0: 5348, 6.0: 1743, 5.0: 1276, 1.0: 838, 7.0: 235})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Resize Training Data\n",
        "\n",
        "Resize training data to fit so there is no overfitting bias when CNN is applied."
      ],
      "metadata": {
        "id": "jpFmPv2enEjl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "def resize_images(images):\n",
        "    resized_images = []\n",
        "    for image in images:\n",
        "        resized_image = cv2.resize(image, (48, 48))\n",
        "        resized_images.append(resized_image)\n",
        "    return np.array(resized_images)\n",
        "\n",
        "X_train_resized = resize_images(X_train)\n",
        "X_test_resized = resize_images(X_test)"
      ],
      "metadata": {
        "id": "7zgNRmcicEMC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Create Network\n",
        "\n",
        "Convolutional neural networks, put simply, use kernels, matrices of smaller values and sizes, to iterate through an image to detect patterns for certain classifications. This is most commonly applicable to image processing and image detection as a CNN can detect patterns and features in its area around it.\n",
        "\n",
        "Each CNN layer is consisted of convolutional layer, a nonlinearity layer, in this case a ReLU, and lastly a max pooling.\n",
        "\n"
      ],
      "metadata": {
        "id": "wZGQCgnjkt5S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Create Network Architecture\n",
        "\n",
        "Tensorflow is a popular machine learning model to construct neural network models. For image processing, convolutional neural networks are especially useful training a model that can accurately predict wafer defects.\n",
        "\n",
        "Below, a tensorflow model with a CNN architecture based on the MATLAB model is produced. Three layers are applied to the wafer maps."
      ],
      "metadata": {
        "id": "qWM3xSzUdd1r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "\n",
        "# Define input shape and number of classes\n",
        "num_classes = 9  # Replace with the actual number of classes\n",
        "\n",
        "# Build the model\n",
        "model = models.Sequential([\n",
        "    # Input layer with normalization\n",
        "    layers.Rescaling(1/2, input_shape=(48, 48, 1)),  # Normalization from 0 to 2\n",
        "\n",
        "    # First convolutional block\n",
        "    layers.Conv2D(8, (3, 3), padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.ReLU(),\n",
        "    layers.MaxPooling2D((2, 2), strides=2),\n",
        "\n",
        "    # Second convolutional block\n",
        "    layers.Conv2D(16, (3, 3), padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.ReLU(),\n",
        "    layers.MaxPooling2D((2, 2), strides=2),\n",
        "\n",
        "    # Third convolutional block\n",
        "    layers.Conv2D(32, (3, 3), padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.ReLU(),\n",
        "    layers.MaxPooling2D((2, 2), strides=2),\n",
        "\n",
        "    # Fourth convolutional block\n",
        "    layers.Conv2D(64, (3, 3), padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.ReLU(),\n",
        "\n",
        "    # Dropout layer\n",
        "    layers.Dropout(0.5),  # Adjust dropout rate if needed\n",
        "\n",
        "    # Fully connected layer\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(num_classes),\n",
        "    layers.Softmax()\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Display model summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "id": "cHgFb5tEyvFa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        },
        "outputId": "4b6e00f0-b5f5-4355-ca16-75d6a849cee6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/preprocessing/tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ rescaling (\u001b[38;5;33mRescaling\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │              \u001b[38;5;34m80\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │              \u001b[38;5;34m32\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu (\u001b[38;5;33mReLU\u001b[0m)                         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m4,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_2 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_3 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)                   │          \u001b[38;5;34m20,745\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ softmax (\u001b[38;5;33mSoftmax\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ rescaling (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">20,745</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ softmax (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m45,609\u001b[0m (178.16 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,609</span> (178.16 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m45,369\u001b[0m (177.22 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,369</span> (177.22 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m240\u001b[0m (960.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">240</span> (960.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Define Training Options\n",
        "\n",
        "Based on the MATLAB model, we will use Adam optimization and training the network for 30 epochs."
      ],
      "metadata": {
        "id": "E50Xx2JjdaPl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "X_train_tf = tf.convert_to_tensor(X_train_resized, dtype=tf.float32)\n",
        "X_test_tf = tf.convert_to_tensor(X_test_resized, dtype=tf.float32)\n",
        "\n",
        "history = model.fit(\n",
        "    X_train_tf, y_train,\n",
        "    epochs=30,\n",
        "    batch_size=128,\n",
        "    validation_data=(X_test_tf, y_test),\n",
        "    shuffle=True,\n",
        "    verbose=0  # Set to 1 for detailed output, 0 to suppress output\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvO-railfXdz",
        "outputId": "77a80003-7d33-49bf-9052-bf0fa216a77c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/nn.py:635: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Quantifying Network Performance\n"
      ],
      "metadata": {
        "id": "WWZbYPzKAPZS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Validation and Training Loss\n",
        "\n",
        "The following code shows validation and training loss per epoch of training this network. Both training and validation begin to converge to a higher accuracy."
      ],
      "metadata": {
        "id": "GO7RZEkyAcaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss', linestyle='--')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "LrKdmblnYiAh",
        "outputId": "d44b9c77-97df-4410-e2c6-9f4adf631bd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2 Confusion Matrix\n",
        "\n",
        "A confusion matrix can help analyze what features are harder for this model to predict as well as compare how well each defect class is being identified."
      ],
      "metadata": {
        "id": "cAjrK45tAlht"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(model.predict(X_test_tf), axis=1)\n",
        "\n",
        "# Calculate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Create a figure with two subplots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(20, 10))  # 1 row, 2 columns\n",
        "\n",
        "# Plot the standard confusion matrix on the first subplot\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=failure_types)\n",
        "disp.plot(cmap=plt.cm.Blues, values_format='d', ax=axes[0])  # Plot on the first subplot (axes[0])\n",
        "axes[0].set_title('Confusion Matrix', fontsize=16)\n",
        "axes[0].tick_params(axis='x', rotation=45, labelsize=12)\n",
        "axes[0].tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Plot the normalized confusion matrix on the second subplot\n",
        "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "disp_normalized = ConfusionMatrixDisplay(confusion_matrix=cm_normalized, display_labels=failure_types)\n",
        "disp_normalized.plot(include_values=True, cmap=plt.cm.Blues, values_format='.2f', ax=axes[1], xticks_rotation='vertical')  # Plot on the second subplot (axes[1])\n",
        "axes[1].set_title('Normalized Confusion Matrix', fontsize=16)\n",
        "axes[1].tick_params(axis='x', labelsize=12)\n",
        "axes[1].tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Adjust layout and show the figure\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "HSr_b4xCjTCS",
        "outputId": "24d4ab19-4f47-4b44-fd3d-7164915a93b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2349/2349\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the confusion matrices produced we can deduce a couple things. As seen in *2.2 - Data Attributes*, a couple of the defects are more difficult, even for manual identification, to classify. The lower accuracy classes as shown in the normalized confusion matrix, include loc, random, and donut defect classes. Viewing the sample defect WBMs showin *2.2*, we see that its hard to differentiate. However, it is interesting that the random defect class is harder to identify using this tensorflow model.\n",
        "\n",
        "In the MATLAB model mentioned earlier, a confusion matrix shows that some other defect classes such as the scratch are far less accurate; however, in this model, they seem to predict those a little better.\n",
        "\n",
        "**Note**: It is also shown in the model using SVM in Xi Chen's previous Python tutorial, that the identification of loc and edge-loc defect classes are also lower relative to other defect classes."
      ],
      "metadata": {
        "id": "2r41cDMuhRgg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3 Precision-Recall Curves"
      ],
      "metadata": {
        "id": "wc94kmQhAt5B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also evaluate network performance using precision, recall, and F1 scores.\n",
        "\n",
        "Definitions:\n",
        "- **True positive**: images predicted as a class and actually belonging to that class\n",
        "- **False positive**: those predicted to belonging to one class while it actually belongs to another\n",
        "- **False negative**: fails to predict a wafer is defective when it is actually defective\n",
        "\n",
        "Precision is the proportion of images that are correctly predicted to belong to a class, and gives the ratio of true positive to total positive predictions made by the model:\n",
        "\\begin{align}\n",
        "        \\text{Precision: } = \\frac{TP}{TP+FP}.\n",
        "    \\end{align}\n",
        "\n",
        "Recall is the ratio of true positives to the sum of true positives false negatives. It represents the proporiton of true positives out of all actual positive cases.\n",
        "\\begin{align}\n",
        "        \\text{Recall: } = \\frac{TP}{TP+FN}.\n",
        "    \\end{align}\n",
        "\n",
        "F1 scores are the harmonic mean of the precision and recall values as given by:\n",
        "\\begin{align}\n",
        "        \\text{F1: } = \\frac{2\\times\\text{precision}\\times\\text{recall}}{(\\text{precision+recall})}.\n",
        "    \\end{align}"
      ],
      "metadata": {
        "id": "ioY5_9ttjLXG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Initialize parameters\n",
        "pr_table = pd.DataFrame(index=failure_types, columns=[\"Recall\", \"Precision\", \"F1\"])\n",
        "\n",
        "# Calculate Precision, Recall, and F1 score for each class\n",
        "for idx in range(num_classes):\n",
        "    # True Positives (TP), False Positives (FP), and False Negatives (FN)\n",
        "    num_tp = cm[idx, idx]\n",
        "    num_fp = sum(cm[:, idx]) - num_tp\n",
        "    num_fn = sum(cm[idx, :]) - num_tp\n",
        "\n",
        "    # Calculate precision and recall, handling division by zero\n",
        "    precision = num_tp / (num_tp + num_fp) if (num_tp + num_fp) > 0 else 0.0\n",
        "    recall = num_tp / (num_tp + num_fn) if (num_tp + num_fn) > 0 else 0.0\n",
        "\n",
        "    # Calculate F1 score\n",
        "    f1_score = (2 * precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0\n",
        "\n",
        "    # Store the values in the table\n",
        "    pr_table.loc[failure_types[idx], \"Recall\"] = recall\n",
        "    pr_table.loc[failure_types[idx], \"Precision\"] = precision\n",
        "    pr_table.loc[failure_types[idx], \"F1\"] = f1_score\n",
        "\n",
        "# Display the Precision-Recall table\n",
        "print(pr_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHMmk6RlzyJN",
        "outputId": "17f12897-c6e2-45a1-8f30-00964c827a10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Recall Precision        F1\n",
            "Center     0.958885  0.962783   0.96083\n",
            "Donut      0.872315  0.911471  0.891463\n",
            "Edge-Loc   0.912774  0.855484  0.883201\n",
            "Edge-Ring  0.982662  0.981585  0.982123\n",
            "Loc        0.756918  0.885389  0.816129\n",
            "Near-full  0.932602  0.892723  0.912227\n",
            "Random     0.675273  0.865441  0.758621\n",
            "Scratch    0.931915  0.952174  0.941935\n",
            "none       0.989955  0.973249  0.981531\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given above is the recall, precision, and F1 scores of each defect class. We can further analyze that Loc defect classification is not precise. When it does identify a loc, it gets it wrong about 24.4% of the time. A random classification is also not as precise as the others, only getting a prediction correct 67.5% of the time. However, compared to the MATLAB model, the F1 scores are on average higher.\n",
        "\n",
        "Below, precision-recall curves are plotted. Precision-recall curves are used to understand a model's ability to identify true positives (reflected by recall) while avoiding false positives (reflected by precision)."
      ],
      "metadata": {
        "id": "mp7zxkYsmJyw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "# Binarize the output labels\n",
        "y_test_bin = label_binarize(y_test, classes=np.arange(num_classes))\n",
        "\n",
        "# Predict probabilities for each class\n",
        "y_score = model.predict(X_test_tf)\n",
        "\n",
        "# Calculate precision-recall curve for each class\n",
        "precision = dict()\n",
        "recall = dict()\n",
        "for i in range(num_classes):\n",
        "    precision[i], recall[i], _ = precision_recall_curve(y_test_bin[:, i], y_score[:, i])\n",
        "\n",
        "# Plot precision-recall curve for each class\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i in range(num_classes):\n",
        "    plt.plot(recall[i], precision[i], label=f'Class {failure_types[i]}')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend(loc='best')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "ACwm-oXh5qnA",
        "outputId": "479341fe-4e8f-427b-a301-eccf46d266f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2349/2349\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ideally, the precision-recall cruve should be steep and tend toward (1, 1), such as Center and edge-ring. Those that perform the best in predicting classes tend toward a steeper curve passing through (1, 1), while those that are worse performing such as Random and Loc in this model are less steep and tend toward the diagonal from (0, 1) to (1, 0).\n",
        "\n",
        "Compared to the PR curves produced by the MATLAB model, the predictions for scratch are much higher while those for random are far worse. However, overall the tensorflow model performed better as the F1 scores suggested."
      ],
      "metadata": {
        "id": "3J3Lrd1ltRq-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvUMW5hL0JlM"
      },
      "source": [
        "## 6. Conclusions\n",
        "\n",
        "Wafer defects offer a lot of insight on which step of the semiconductor manufacturing process an error may have occured on. Additionally, training models to an open-source programming platform like Python offers variability and easy tuning for compatible industrial practice. This suggests applications in collaboration utillizing quatum federated learning projects.\n",
        "\n",
        "In this project, we developed a defect pattern classification method using a convolutional neural network model using the tensorflow software based on a MATLAB model. We achieved results that suggest that this model is better at predicting wafer defects than its MATLAB counterpart. This yields two great advantages, as previously mentioned:\n",
        "\n",
        "1. For future industrial practices, implementation of quick semiconductor wafer defects can quickly and reliably detect wafer defects and suggest where manufacturing mishaps may have occured.\n",
        "2. Creates a larger open source platform that can be accessible to a larger network."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opB-bHOy0JlN"
      },
      "source": [
        "## References\n",
        "\n",
        "Wu, Ming-Ju, Jyh-Shing R. Jang, and Jui-Long Chen. “Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets.” IEEE Transactions on Semiconductor Manufacturing 28, no. 1 (February 2015): 1–12. https://doi.org/10.1109/TSM.2014.2364237.\n",
        "\n",
        "https://www.mathworks.com/help/vision/ug/classify-defects-on-wafer-maps-using-deep-learning.html\n",
        "\n",
        "https://github.com/iamxichen/Semiconductor-Wafer-Defect-Classification/blob/main/Semiconductor%20Wafer%20Defect%20Classification.ipynb\n",
        "\n",
        "https://www.tensorflow.org/tutorials/images/cnn\n",
        "\n",
        "http://mirlab.org/dataSet/public/"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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      "provenance": []
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