APEX: Automating Parameter Extraction of Compact Models with Differential Neural Network Approximation

The traditional compact model parameter extraction highly depends on engineers' expertise, leading to a time-consuming and iterative process. To address the above issue, this paper proposes an automatic parameter extraction method for compact models, APEX. The proposed APEX framework adopts an artificial neural network (ANN) method as an approximation of compact models using model parameters as inputs and IV/CV data as outputs. The model parameters are efficiently extracted using an automatic differential mechanism based on the ANN-approximated compact model. Experimental results demonstrate that our proposed framework achieves good fitting accuracy and scalability across device structures when evaluating GAA and FinFET devices. A fitting error of less than 4% is achieved on the open-source benchmark.