Lithography defect prediction for microchip manufacturing optimization with machine learning model

Accelerating Defect Prediction in Semiconductors Using Graph Neural Networks

This project applies machine learning algorithms - K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) - to predict semiconductor manufacturing outcomes. Through feature selection and cross-validation, it identifies the most effective model and provides insights for future data-driven decision-making.

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This project tackles defect prediction in semiconductor manufacturing using sensor data from wafer production processes.

Machine Learning for Semiconductor Manufacturing Defect Prediction

Lithography defect prediction for microchip manufacturing optimization with machine learning model

AI-Powered Semiconductor Defect Prediction System

Left-Shifting Semicondutor Defect Detection Using ML techniques

This repository presents a complete machine learning and deep learning pipeline for analyzing the UCI SECOM Dataset, a real-world dataset from semiconductor manufacturing. The objective is to predict whether a chip passes or fails quality control, enabling predictive quality assurance in manufacturing processes.

This project focuses on building ML models to predict defects in semiconductor manufacturing. Early defect prediction enables proactive intervention, improving product quality, boosting yield, and reducing overall production costs.

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Semiconductor Defect Level Prediction using Physics-Informed Machine Learning

SECOM 데이터셋을 활용한 반도체 불량 예측 머신러닝 프로젝트

A Django-based defect prediction platform for semiconductor fab data

Defect Prediction in Semiconductor Sensor Data Using Machine Learning and Dimensionality Reduction

End-to-end ML pipeline for semiconductor wafer defect prediction with SHAP explainability

A portfolio of semiconductor data analysis projects, including wafer defect classification and yield prediction

AI + Edge AI + LLM system for wafer yield prediction, defect inspection, and intelligent semiconductor manufacturing analytics.

Monocular depth estimation from SEM images using deep learning. Synthetic SEM images from Nebula train a CNN model (ResNet + UNet) for precise depth prediction, aiding defect detection in semiconductor manufacturing.

Learning materials and KNIME workflows from a workshop on Machine Learning applications in the Semiconductor Industry. Covers data preprocessing, model training, and evaluation using visual workflows for process optimization and defect prediction.

This assignment focuses on detecting scratch-related defects in semiconductor wafer maps using tabular data. The task includes feature engineering, handling class imbalance, model training and evaluation, and generating final predictions on a held-out test set for submission.