PanithanS/Defect-Prediction-in-Semiconductor-Lithography
Lithography defect prediction for microchip manufacturing optimization with machine learning model
This helps microchip manufacturers optimize the critical lithography process to reduce defects and increase the number of working chips per wafer. By taking your lithography exposure energy (Dose) and focal length (Focus) settings, it predicts whether those settings will result in a 'good chip' or a 'defective chip'. This tool is for process engineers or quality control specialists in semiconductor manufacturing.
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Use this if you need to quickly determine the optimal light exposure settings for your lithography tools to maximize defect-free chip production.
Not ideal if you are looking to optimize other stages of semiconductor manufacturing beyond lithography or require predictions based on a broader range of input parameters.
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Jupyter Notebook
License
MIT
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Last pushed
Dec 02, 2023
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