# Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classification

arXiv: https://arxiv.org/abs/2411.19422 · HTML: https://mlsanity.com/paper/2411.19422/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning](https://mlsanity.com/paper/2604.27629/) (2026)
- [Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification](https://mlsanity.com/paper/2510.19463/) (2025)
- [Efficient Aspect Term Extraction using Spiking Neural Network](https://mlsanity.com/paper/2601.06637/) (2026)

## Abstract (arXiv)

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.