  In this paper, we propose a machine-learning assisted modeling framework in
design-technology co-optimization (DTCO) flow. Neural network (NN) based
surrogate model is used as an alternative of compact model of new devices
without prior knowledge of device physics to predict device and circuit
electrical characteristics. This modeling framework is demonstrated and
verified in FinFET with high predicted accuracy in device and circuit level.
Details about the data handling and prediction results are discussed. Moreover,
same framework is applied to new mechanism device tunnel FET (TFET) to predict
device and circuit characteristics. This work provides new modeling method for
DTCO flow.
