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Physics-Informed Neural Network-Assisted Compact Modeling of UTB-SOI and Nanowire MOSFETs for Ultra-Low Power Edge-AI Applications
Balamanikandan A , Venkataramanaiah N , Sukanya M , Sudhakar Reddy N , Gomathy G , Venkatachalam K
Abstract:   (84 Views)
Physics-informed neural networks (PINNs) offer a promising route to bridge device-level simulations and compact circuit models. In this work, we present a hybrid modeling framework that integrates TCAD datasets with a baseline compact model and applies a PINN correction to capture stress-condition effects with high fidelity. The proposed approach achieves ≤ 2% route mean square error (RMSE) across more than 2,000 bias points, maintaining stable predictions under temperature (273–373 K) and radiation (0–100 krad) variations. Extracted Berkeley Short-channel IGFET Model (BSIM) parameters enable direct SPICE simulation, ensuring compatibility with standard circuit design workflows. For deployment, the trained PINN is exported as a quantized ONNX model, achieving sub-millisecond inference and ultra-low energy consumption (0.25 pJ/op) on a Cortex-M55 platform. This dual pathway supports both high-accuracy circuit simulation and real-time edge inference, making it suitable for embedded applications under constrained conditions. Comparative analysis with recent ANN-based models confirms that our physics-informed approach offers superior interpretability, SPICE readiness, and deployment efficiency. All datasets, code, and models are released to support reproducibility, benchmarking, and further research in compact modeling and edge-AI integration.
Keywords: Physics-informed neural network, Compact model, SPICE, ONNX quantization, Edge-AI.
Full-Text [PDF 2516 kb]   (29 Downloads)    
Type of Study: Research Paper | Subject: VLSI
Received: 2025/07/25 | Revised: 2025/12/07 | Accepted: 2025/11/08
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.