Investigating the prediction of semiconductor wafer production through classification AI models

Ahan Mathew; Rajat Dandekar

doi:10.36948/ijfmr.2026.v08i03.79520

Investigating the prediction of semiconductor wafer production through classification AI models

Author(s)	Ahan Mathew, Rajat Dandekar
Country	Singapore
Abstract	Abstract Semiconductor wafer manufacturing is highly susceptible to defects that can reduce production yield and increase manufacturing costs. This study investigates the use of machine learning classification models to predict defective semiconductor wafers at an early stage of production using the SECOM dataset from the University of California Irvine. Several classification approaches, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and XGBoost, were evaluated alongside imbalance-handling techniques such as SMOTE oversampling and cost-sensitive learning. Model performance was assessed using precision, recall, F1-score, accuracy, and AUC metrics. Results showed that baseline models achieved high overall accuracy due to the dominance of non-defective samples but performed poorly in detecting defective wafers. Hybrid approaches using SMOTE and class balancing slightly improved minority-class recall, although defect detection performance remained limited because of the severe class imbalance within the dataset. The findings highlight both the challenges and potential of AI-based approaches for semiconductor defect prediction and suggest that future improvements should focus on advanced resampling methods, imbalance-specific algorithms, and ensemble learning techniques to enhance predictive reliability in manufacturing environments.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-26
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.79520

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 3 Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Investigating the prediction of semiconductor wafer production through classification AI models

Share this