We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.
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Updated
Oct 1, 2023 - Jupyter Notebook
We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.
End to end machine learning project for accurately detecting faults in wafers based on sensor data.
This project aims to process 2D images of semiconductor silicon wafers to identify any defects on the wafers as well as their corresponding locations.
Classification of wafer defect map patterns
This is a neural network designed to classify wafer imperfections without feature engineering.
data fetched by wafers (thin slices of semiconductors) is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not. Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors.
Faulty Wafer Detection
data fetched by wafers (thin slices of semiconductors) is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not. Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors.
This repo contains well-designed tabular features created through image processing techniques in order to predict a scratch on wafers.
This repository hosts a comprehensive end-to-end machine learning project focused on Wafer Fault Detection, implemented using Python with Flask.
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