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A Reinforcement Learning Framework for Good Die in Bad Neighborhood Analysis
Conference proceeding

A Reinforcement Learning Framework for Good Die in Bad Neighborhood Analysis

Mohammad Ershad Shaik, Abhishek Mishra, Nagarajan Kandasamy and Nur A. Touba
Proceedings / Design, Automation and Test in Europe, pp 1-7
20 Apr 2026
DOI: https://doi.org/10.23919/DATE69613.2026.11539490

Abstract

Aluminum Costing Costs Dies Equations good-die-in-bad-neighborhood Labeling Learning (artificial intelligence) Modeling Printing reinforcement learning Semiconductor device testing silent data corruption Testing Semiconductors
Good-Die-in-Bad-Neighborhood (GDBN) analysis is a critical challenge in semiconductor manufacturing, where overly aggressive rejection reduces yield, while lenient acceptance in-creases test escapes and outgoing defective parts per million (DPPM). This asymmetric trade-off creates a multi-objective optimization problem spanning defect coverage, yield preservation, and return-material-authorization cost, often beyond the reach of conventional gradient-based methods. In this work, we employ reinforcement learning to develop an attention-based Deep Q-Network (DQN) framework tailored for GDBN-driven decision making. The DQN agent learns an optimal die-level screening policy from local wafer patches along with numerical test parametric data, optimizing actions that maximize cumulative long-term reward. By incorporating an attention mechanism, our model captures neighborhood-aware spatial dependencies across dies, enabling context-sensitive decision-making that balances yield and quality. We evaluated our method on the publicly available WM-811K wafer dataset, demonstrating substantial improvements in DPPM reduction and yield-cost tradeoffs compared to existing approaches. The results demonstrate that reinforcement learning provides a scalable and effective solution for adaptive defect screening in high-volume semiconductor test environments.

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