A summary of the semiconductor PCM control chart dashboard created for IE 4331. Includes data cleaning steps, X-bar and R charts, and capability results for the selected PCM characteristic.

This project applies data analysis and machine learning techniques for defect detection and process monitoring in semiconductor manufacturing, aiming to identify anomalies, improve yield, and ensure consistent product quality

This analysis applies Statistical Process Control (SPC) techniques to monitor semiconductor resistivity measurements. Retrospective Phase I analysis identified and excluded two special-cause subgroups (6 and 16) to establish stable control limits. Using these limits, Phase II online monitoring showed all subgroups in control with no evidence of mean or dispersion shifts. The study highlights the importance of identifying assignable causes, maintaining stable processes, and continuing routine SPC for process reliability.

A statistical analysis of lithography CD data using t-tests. The report examines whether the process mean differs from the 32 nm target and estimates sample requirements for comparing two lithography tools.

Production Line Analysis at Ahuja Radios This report analyzes production performance across three sites of Ahuja Radios: Sunwood, Royal Industries, and Sigma Technologies. Using R and R Markdown, I evaluated monthly outputs, units per hour, and utilization levels. The report also simulates the impact of recommended improvements (live output tracking, better workforce balancing, and reduced idle time) and compares before vs after performance using bar plots and scatter plots. Key insights highlight how operational changes can boost utilization and throughput, with Sunwood showing the largest improvement potential and Royal/Sigma achieving incremental gains. Prepared by: Rijul Bajaj Date: 01 August 2025

test document for class 1 for markdown