تقرير
Deep Learning Assisted Compact Modeling of Nanoscale Transistor
| العنوان: | Deep Learning Assisted Compact Modeling of Nanoscale Transistor |
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| المؤلفون: | Kam, Hei |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | Transistors are the basic building blocks for all electronics. Accurate prediction of their current-voltage (IV) characteristics enables circuit simulations before the expensive silicon tape-out. In this work, we propose using deep neural network to improve the accuracy for the conventional, physics-based compact model for nanoscale transistors. Physics-driven requirements on the neural network are discussed. Using finite element simulation as the input dataset, together with a neural network with roughly 30 neurons, the final IV model can well-predict the IV to within 1%. The trained model can readily be implemented by the hardware description language (HDL) such as VerilogA for circuit simulation. Comment: 7 page, 9 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2107.06167 |
| رقم الأكسشن: | edsarx.2107.06167 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |