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A Multi-Modal Attention-Based Framework for Good Die in Bad Neighborhood Methodology
Conference proceeding

A Multi-Modal Attention-Based Framework for Good Die in Bad Neighborhood Methodology

Mohammad Ershad Shaik, Abhishek Mishra, Nagarajan Kandasamy and Nur A. Touba
Proceedings of the ... IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (Online), pp 1-6
21 Oct 2025
DOI: https://doi.org/10.1109/DFT66274.2025.11257528

Abstract

attention-based neural network model good-die-in-bad-neighborhoods multiple modality model Neural networks Numerical models Semiconductor device manufacture Semiconductor device modeling Semiconductor device reliability Semiconductor device testing test escapes Throughput Very large scale integration Visualization
Detecting latent defects and reducing defective parts per million (DPPM) are crucial for improving semiconductor test quality and reliability. Good Die in Bad Neighborhood (GDBN) identifies and eliminates potentially defective dies, even if they pass standard tests. We propose a multi-modal attention-based framework that uses wafer-level defect visual patterns along with numerical test parametric data to improve GDBN identification. Experiments using the industrial semiconductor wafer dataset WM-811K demonstrate multi-modal fusion with an attention-based model captures more test escapes & a 22 % greater reduction in DPPM compared to existing GDBN methods.

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