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Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classification
Conference proceeding   Open access

Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classification

Abhishek Mishra, Suman Kumar, Anush Lingamoorthy, Anup Das and Nagarajan Kandasamy
Proceedings - International Test Conference, pp 16-20
03 Nov 2024
DOI: https://doi.org/10.1109/ITC51657.2024.00011
url
http://arxiv.org/abs/2411.19422View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Accuracy Benchmark testing Computational efficiency Integrated circuit synthesis Manufacturing neuromorphic computing Open source software Pattern classification Spiking neural networks Wafer map pattern classification Computer Architecture
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.

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