  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.
