Practical example from the SPIE short course "Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization"

Collaboration of two technologies (Machine Learning and TCAD) to improve the productivity in Semiconductor manufacturing industry

Predict transparent semiconductors using machine learning

Machine Learning to Promote Efficient Screening of Low-Contact Electrode for Two-Dimensional Semiconductor Transistor Under Limited Data

The inputs of various sensors for different wafers have been provided. In electronics, a wafer (also called a slice or substrate) is a thin slice of semiconductor used for the fabrication of integrated circuits. The goal is to build a machine learning model which predicts whether a wafer needs to be replaced or not(i.e., whether it is working or not) based on the inputs from various sensors. There are two classes: +1 and -1.

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.

MVC framework for adaptive test and lightning-fast machine learning for semiconductor data analysis.

Code of the publication "From Experimental Values to Predictive Models: Machine Learning-Driven Energy Level Determination in Organic Semiconductors"

The Waffer Fault Detection project in Python involves using machine learning algorithms to detect and classify defects on silicon waffers in semiconductor manufacturing.

A machine learning project for predicting Pass/Fail yield in semiconductor manufacturing, leveraging models like SVM, Random Forest, and Naive Bayes, with a focus on data analysis, visualization, and high accuracy results.

Machine Learning Tailoring Organic Semiconductors

data for paper "Discovery of Crystallizable Organic Semiconductors with Machine Learning"

Scripts from a paper on discovering ternary semiconductors with machine learning and crystal structure prediction

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Octahedral Motif-Guided Design of Optoelectronic Semiconductors via Interpretable Machine Learning

Strain-Composition-Bandgap mapping in III-V semiconductors using machine learning models

BandGapAlign is a machine learning project designed to predict band gaps and band alignment in semiconductors.

Machine Learning, Future Selection, Model Tuning of the Semiconductor Dataset to detect the Failure in the Manufacturing.

Machine learning models for predicting semiconductor properties using XGBoost and advanced feature engineering techniques to accelerate materials science research.

This is a learning project that aims to build a predictive model to control quality in semiconductor manufacturing using machine learning techniques. The project uses the SECOM dataset and applies various models.

Operational Analytics for HCL–Foxconn Semiconductor OSAT Facility is an end-to-end data analyst portfolio project that simulates real-world manufacturing operations. It focuses on improving production efficiency, analyzing equipment downtime, optimizing workforce attendance, and predicting operational risks using machine learning,SQL, and Power BI.

Instrumented Wafer (Thermocouples, Bonded Wafer or RTDs) finds application in semiconductor processing equipment where knowing and controlling the temperature at the surface of a wafer is critical.So Cost is getting high when we pull off each wafer for checking whether it is in working condition or not.So solving this problem using sensory data and implements machine learning over it to predict whether wafer sensor is in working condition or not.

The inputs of various sensors for different wafers have been provided. In electronics, a wafer (also called a slice or substrate) is a thin slice of semiconductor used for the fabrication of integrated circuits. The goal is to build a machine learning model which predicts whether a wafer needs to be replaced or not(i.e., whether it is working or not) based on the inputs from various sensors. There are two classes: +1 and -1. +1 means that the wafer is in a working condition and it doesn’t need to be replaced. -1 means that the wafer is faulty and it needs to be replaced.

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Machine Learning for Semiconductor Manufacturing Defect Prediction

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Defect Identification in semiconductors using machine learning

In this project, I explore the possibility of using Technology Computer Aided Design (TCAD) to assist machine learning for semiconductor device failure trouble shooting and device reverse engineering.

Enterprise-grade, production-ready data analytics and machine learning platform for semiconductor manufacturing.