arunsinghbabal/Automated-Defective-Substrate-Identification-for-Expedited-Manufacturing
Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The project retrains itself after every prediction, making it more robust and generalized over time.
This tool helps manufacturing and quality control engineers in semiconductor and solar panel fabrication plants quickly identify defective wafers (substrates) before they are used in production. You provide sensor data readings from your wafers, and the system classifies each wafer as 'good' or 'bad', outputting a report on which wafers should be replaced. It continuously learns from new data to improve its accuracy over time.
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Use this if you need to automate the quality control process for semiconductor wafers or solar cell substrates to reduce waste and improve manufacturing efficiency.
Not ideal if you are looking for a tool to analyze defects in finished integrated circuits or solar panels, as this focuses on raw substrate quality.
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Language
Python
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Last pushed
Feb 02, 2023
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