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

Analyzed real-world foundry data and built a classifier to detect semiconductor wafer defects during the manufacturing process.

Reinforcement Learning, specifically Deep Q-Networks, was applied to semiconductor manufacturing data to efficiently identify substandard products, with a model optimized using the F1-Metric achieving 87% accuracy and a 22.4% time-saving, highlighting RL's potential in enhancing manufacturing processes.

CONTEXT: A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/ variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. These signals can be used as features to predict the yield type. And by analysing and trying out different combinations of features, essential signals that are impacting the yield type can be identified. • DATA DESCRIPTION: sensor-data.csv : (1567, 592) The data consists of 1567 examples each with 591 features. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing. Target column “ –1” corresponds to a pass and “1” corresponds to a fail and the data time stamp is for that specific test point. • PROJECT OBJECTIVE: We will build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not

Build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.

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.

A code to classify defects on semi-conductor wafers, based on the research paper "A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes", IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 33, NO. 3, AUGUST 2020

WaferIQ is a semiconductor yield analysis and defect intelligence platform integrating spatial defect simulation, probabilistic yield modeling, and ML-assisted pattern classification. It combines deterministic process modeling with LLM-based diagnostic reasoning to analyze manufacturing impact across advanced process nodes.

Semiconductor manufacturing process

“Pass/Fail Yield Prediction in Semiconductor Manufacturing Using High-Dimensional Sensor Data”

No description available

ISEN 613 - Project

Analysis of data from 512 sensors in a semiconductor manufacturing process

Codes for paper "Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing"

Chemical Engineer focused on semiconductor manufacturing and process optimization. Building Python and SQL tools for manufacturing data analysis, statistical process control, and engineering automation.

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

The Deep Autoencoder for feature extraction from sensor data of semiconductor manufacturing processes - IEEE-TSM

I will build a machine learning classifier to predict the Pass/Fail yield of a particular semiconductor manufacturing process.

This project analyzes the SECOM semiconductor manufacturing dataset with the objective of predicting rare process failures using high-dimensional sensor data.

Applied Lean Manufacturing principles using Python to analyze and optimize a simulated semiconductor fabrication process through value stream mapping, waste reduction, and performance visualization.

Smart yield prediction and anomaly detection for semiconductor manufacturing processes using Machine learning models. This project focuses on analyzing sensor data and process parameters to procatively forecast final wafer yield, optimize fabrication control, and reduce waste in the semiconductor supply chain.

A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system.

EAP (Equipment Automation Program) is a control equipment process program, it usually supported to semiconductor manufacture. I want to write a open EAP by my experience.

End-to-end AI/ML system for semiconductor manufacturing that predicts test yield, detects process anomalies, and surfaces root-cause signals from high-dimensional sensor data. Built with reproducible pipelines, explainable models, and production-style APIs.

The project was accomplished by employing supervised learning, ensemble modeling, and unsupervised learning techniques to build and train a prediction model to identify Pass/Fail yield of a particular process entity for a semiconductor manufacturing company.

The project was accomplished by employing supervised learning, ensemble modeling, and unsupervised learning techniques to build and train a prediction model to identify Pass/Fail yield of a particular process entity for a semiconductor manufacturing company.

This repository implements a Wafer Fault Detection System leveraging advanced machine learning and data processing techniques. It automates the classification of wafers into faulty or non-faulty categories, ensuring enhanced quality control and operational efficiency in semiconductor manufacturing.

The **Wafer Fault Detection** project involves developing a machine learning model to accurately identify manufacturing defects in semiconductor wafers, ensuring quality control and reducing production losses. It leverages data-driven insights to detect anomalies and optimize the fabrication process.

This project involves implementing Verilog code on a MachXO3LF FPGA board manufactured by Lattice Semiconductor. The simulation and verification process is facilitated using ModelSim, showcasing waveform representations for thorough analysis and testing.

SEMICONDUCTOR MANUFACTURING PROCESS