https://github.com/harmanveer-2546/wafer-fault-detection

The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.
https://github.com/harmanveer-2546/wafer-fault-detection

clustering data-transformation feature-selection machine-learning matplotlib numpy pandas python random-forest roc-auc-curve roc-auc-score seaborn sklearn svc xgboost

Last synced: 6 days ago
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The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/wafer-fault-detection
• Owner: harmanveer-2546
• Created: 2024-06-08T07:33:23.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-08T07:41:50.000Z (5 months ago)
• Last Synced: 2024-06-08T08:45:06.098Z (5 months ago)
• Topics: clustering, data-transformation, feature-selection, machine-learning, matplotlib, numpy, pandas, python, random-forest, roc-auc-curve, roc-auc-score, seaborn, sklearn, svc, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 788 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Wafer Fault Detection:

In electronics, a wafer (also called a slice or substrate) is a thin slice of semiconductor, such as a crystalline silicon (c-Si), used to fabricate integrated circuits and, in photovoltaics, manufacture solar cells. The wafer serves as the substrate(the foundation for constructing other components) for microelectronic devices built in and upon the wafer.

Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they consist of a few hundred sensors. Wafers are fundamental to photovoltaic power generation, and production thereof requires high technology. Photovoltaic power generation system converts sunlight energy directly to electrical energy.

The motto behind figuring out the faulty wafers is to obliterate the need for manual manpower to do the same. And make no mistake when we're saying this, even when they suspect a certain wafer to be faulty, they had to open the wafer from scratch and deal with the issue, and by doing so all the wafers in the vicinity had to be stopped disrupting the whole process and stuff and this is when that certain wafer was indeed faulty, however, when their suspicion came outta be falsely negative, then we can only imagine the waste of time, man-power and of course, cost incurred.

## About Dataset:

The dataset contains information about 1000 wafers, each of which has been inspected for defects. The features of each wafer include its X and Y coordinates, the type of defect (crack, pit, or scratch), and the size of the defect. The labels indicate whether the wafer is faulty or not faulty.

The dataset can be used to train a machine learning model to predict whether a wafer is faulty based on its features. This could be used to improve the yield of semiconductor manufacturing processes.