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Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0
Journal article - Supervised student publication
Wafer map pattern recognition using ternary spiking neural networks
Microprocessors and Microsystems, Forthcoming
15 Jun 2026
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Abstract
The analysis of wafer map patterns is crucial for detecting manufacturing defects in integrated circuits. Although deep neural networks have been used for this task, spiking neural networks (SNNs) offer a more energy-efficient alternative by using spike activations, replacing multiplications with simpler additions. We develop an SNN with ternary spiking neuron model that outputs values of
instead of the traditional
, to enhance the neuron’s information capacity and learning. It also improves the model’s control over the influence of each type of spike on the membrane potential, helping to prevent excessive activation and maintain stability by balancing excitatory and inhibitory signals. Using direct spike training with pseudogradients, our model outperforms both binary SNNs and DNNs on the WM-811k wafer benchmark dataset, excelling in identifying critical defect patterns that are underrepresented in the dataset with high accuracy and computational efficiency.
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Details
- Title
- Wafer map pattern recognition using ternary spiking neural networks
- Creators
- Abhishek Kumar Mishra (Corresponding Author) - Drexel UniversityAnup Kumar Das - Drexel University, Electrical and Computer EngineeringNagarajan Kandasamy - Drexel University, Electrical and Computer Engineering
- Publication Details
- Microprocessors and Microsystems, Forthcoming
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991022189374104721