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Balachandar Jeganathan

Publications (6)

Automated Diabetic Retinopathy Severity Classification Using Transfer Learning with DenseNet201

Balachandar Jeganathan · Current Journal of Applied Science and Technology · 2026

Diabetic Retinopathy (DR) remains one of the most common causes of preventable blindness worldwide, particularly among working-age adults between 20 and 64 years of age. Despite its prevalence, early diagnosis continues to be a significant clinical challenge because the disease f...

Open access Research Article 10.9734/cjast/2026/v45i54690

Comparative Analysis of Fully Convolutional Networks (FCN), SegNet, and U-Net for Semantic Segmentation of Synthetic Wafer Map Defect Patterns

Balachandar Jeganathan · Asian Journal of Advanced Research and Reports · 2026

Wafer defect maps serve as a primary diagnostic tool for process engineers, enabling the visualization of spatial defect distributions and the identification of potential equipment or process issues. As technology nodes shrink and device densities increase, the volume of inspecti...

Open access Research Article 10.9734/ajarr/2026/v20i31302

A Review of Deep Learning Methods for Wafer Defect Detection with a Focus on YOLO-series Detectors

Balachandar Jeganathan · Current Journal of Applied Science and Technology · 2026

The semiconductor industry faces increasing demands for precise and efficient wafer defect detection as device miniaturisation continues. Traditional manual inspection methods have proven inadequate for modern high-density wafer manufacturing, necessitating automated detection so...

Open access Research Article 10.9734/cjast/2026/v45i24668

Beyond the Golden Die: A Hybrid Die-to-Database Deep Learning Framework for Systematic Defect Detection

Balachandar Jeganathan · Current Journal of Applied Science and Technology · 2025

Traditional Die-to-Die (D2D) inspection methods struggle to detect systematic and die-repeating defects that commonly occur in advanced semiconductor manufacturing nodes. To address these limitations, this work proposes a hybrid Die-to-Database (D2DB) inspection framework that br...

Open access Research Article 10.9734/cjast/2025/v44i124644

Machine Learning and Deep Learning in Wafer Defect Detection: Current State and Future Directions

Balachandar Jeganathan · Current Journal of Applied Science and Technology · 2025

The semiconductor manufacturing industry demands increasingly sophisticated quality control mechanisms as device miniaturization approaches atomic scales. Wafer defect detection, a critical component of semiconductor fabrication, has undergone significant transformation with the...

Open access Research Article 10.9734/cjast/2025/v44i124637

Transformer-Based and Generative AI Framework for Automated Defect Detection in Semiconductor Wafer Inspection

Balachandar Jeganathan · Current Journal of Applied Science and Technology · 2025

The continuous downscaling of semiconductor feature sizes and the introduction of complex multilayer architectures have made wafer defect detection increasingly critical to maintaining high device yield and process stability. Conventional automated inspection systems that rely on...

Open access Research Article 10.9734/cjast/2025/v44i114633