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visual_inspection.py
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visual_inspection.py
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'''****************************************************************************
* visual_inspection.py: Visual Inspection Models
******************************************************************************
* v0.1 - 01.03.2019
*
* Copyright (c) 2019 Tobias Schlosser (tobias@tobias-schlosser.net)
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
****************************************************************************'''
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D
'''
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks
Schlosser, Tobias ; Beuth, Frederik ; Friedrich, Michael ; Kowerko, Danny
'''
def model_CNN_custom_Schlosser2019_good_bad_chips(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=48, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(3, 3)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(filters=96, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=144, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(filters=192, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(units=192, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model
def model_CNN_custom_Schlosser2019_in_out_chips(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model
def model_CNN_custom_Schlosser2019_good_bad_streets(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=48, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(3, 3)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(filters=96, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=144, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(filters=192, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(1, 3))) # (2, 2) -> (1, 3)
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(units=192, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model
'''
Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class
Cheon, Sejune ; Lee, Hankang ; Kim, Chang Ouk ; Lee, Seok Hyung
'''
def model_CNN_custom_Cheon2019(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(units=512, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model
'''
Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network
Nakazawa, Takeshi ; Kulkarni, Deepak V.
'''
def model_CNN_custom_Nakazawa2018(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(units=256, activation='sigmoid'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model
'''
Deep Learning for Classification of the Chemical Composition of Particle Defects on Semiconductor Wafers
O'Leary, Jared ; Sawlani, Kapil ; Mesbah, Ali
'''
def model_CNN_custom_OLeary2020(input_shape, classes):
model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(units=4096, activation='tanh'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=4096, activation='tanh'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=4096, activation='tanh'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=classes, activation='softmax'))
return model