APA
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Parandiyal, S., Kumar, A., Ehteshamuddin, M., Singh, A., Sheelvardhan, K., Ray, S., … Dasgupta, A. (2025). Metalearning Based Adaptive Compact Modeling Framework for Advanced Transistors across Technology Nodes. IEEE Transactions on Artificial Intelligence.
Chicago/Turabian
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Parandiyal, Srishti, Abhishek Kumar, M. Ehteshamuddin, Anamika Singh, Kumar Sheelvardhan, Samrat Ray, Abhishek Somani, Sourajeet Roy, and Avirup Dasgupta. “Metalearning Based Adaptive Compact Modeling Framework for Advanced Transistors across Technology Nodes.” IEEE Transactions on Artificial Intelligence (2025).
MLA
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Parandiyal, Srishti, et al. “Metalearning Based Adaptive Compact Modeling Framework for Advanced Transistors across Technology Nodes.” IEEE Transactions on Artificial Intelligence, 2025.
BibTeX Click to copy
@article{srishti2025a,
title = {Metalearning Based Adaptive Compact Modeling Framework for Advanced Transistors across Technology Nodes},
year = {2025},
journal = {IEEE Transactions on Artificial Intelligence},
author = {Parandiyal, Srishti and Kumar, Abhishek and Ehteshamuddin, M. and Singh, Anamika and Sheelvardhan, Kumar and Ray, Samrat and Somani, Abhishek and Roy, Sourajeet and Dasgupta, Avirup}
}
This paper presents an adaptive and automated device modeling framework valid across different technology nodes and device structures. A novel metalearning-based surrogate model using Prior Knowledge Input with Difference Artificial Neural Network (PKID ANN) combined with advanced transfer learning (TL) is developed. This approach is validated using various advanced FET devices. In addition to transferring weights and biases from pretrained model, a scaled low-fidelity model is developed for efficient training of different primary target models. Two TL techniques, full and partial knowledge transfer, are compared, with PKID ANN with partial transfer learning (PKID-PTL) showing significant speed-up in all phases of model development. The proposed PKID-PTL technique is a potential candidate for efficient device modeling allowing seamless model automation across technology nodes and devices with the least human intervention.