Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classification

2024/11/29 by Abhishek Kumar Mishra, Mishra, Abhishek, Suman Kumar +7 · 1 citation
Engineering · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Thin-Film Transistor Technologies

paper · pdf · doi:10.48550/arxiv.2411.19422

openalex publication_date 2024/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions. Wafer2Spike achieves an average classification accuracy of 98% on the WM-811k wafer benchmark dataset. It is also superior to existing approaches for classifying defect patterns that are underrepresented in the original dataset. Wafer2Spike achieves this improved precision with great computational efficiency.

Cited by

• Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification

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