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Chip surface defect classification device and method based on deep learning

A defect classification and deep learning technology, applied in the field of chip surface defect classification devices based on deep learning, can solve problems such as difficulty in meeting the training data demand of deep convolutional neural networks, affecting model accuracy, and limited number of defect pictures, etc. The effect of improving classification accuracy, improving training accuracy, and improving classification efficiency

Pending Publication Date: 2020-03-27
SHANGHAI INTEGRATED CIRCUIT RES & DEV CENT
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Problems solved by technology

However, in actual production, due to the limited number of defect images generated online, it is difficult to meet the large demand for deep convolutional neural network training data; if the number of defect images used for training is insufficient, the accuracy of the model will be affected

Method used

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  • Chip surface defect classification device and method based on deep learning
  • Chip surface defect classification device and method based on deep learning

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Embodiment 1

[0056] In the present invention, the identification model can be a deep convolutional neural network model, and its specific training steps are:

[0057] Step 1: Set the training image size in the training image set to 3 channels 224×224; in this embodiment, it can also be set to 1 channel image, and the channel size can be any value between 0-256;

[0058] Step 2: Input the training image to the input layer of the deep convolutional neural network model, and the size of the input layer of the deep convolutional neural network model is 224×224×3;

[0059] Step 3: The training image output from the input layer of the deep convolutional neural network model is input to the first convolutional layer C1 of the deep convolutional neural network model; the size of the first convolutional layer C1 of the deep convolutional neural network model is 224×224 ×64, using ReLU as the activation function; in this embodiment, the size of the sliding matrix can also be other values ​​such as 3...

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Abstract

The invention discloses a chip surface defect classification method based on deep learning. The method comprises the following steps of S01, inputting shot images into an image preprocessing unit, theimage preprocessing unit preprocessing the shot images to form processed images, combining the shot images and the processed images into an image set, and the image set comprising a training image set; S02, inputting the training image set into a training unit for model training to obtain a recognition model; and S03, inputting a to-be-recognized image into the recognition model, and obtaining acorresponding defect type through recognition of the recognition model. According to the chip surface defect classification device and method based on deep learning provided by the invention, the original shot image is preprocessed by using an image processing method including scaling, rotation and wrong cutting, so that a large number of training images and test images are formed, and the training accuracy of a recognition model is improved.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a device and method for classifying chip surface defects based on deep learning. Background technique [0002] Integrated circuit chips are widely used in various fields and are the key to national economic development and information security. However, during the manufacturing process of its package, the defects generated on the chip surface will directly affect the working life and reliability. Traditional manual detection and classification methods have shortcomings such as relying on subjective experience, time-consuming and labor-consuming, and high false detection rate, which can no longer meet the needs of high-precision and high-speed production lines. [0003] The traditional chip defect classification technology based on machine vision has a relatively mature framework system, which includes the following processes in turn: image acquisition, defect image segmentation, de...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/241G06F18/214
Inventor 傅豪王鹏飞李琛段杰斌周涛王修翠余学儒
Owner SHANGHAI INTEGRATED CIRCUIT RES & DEV CENT
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