Hybrid Compact Modeling Strategy: A Fully-Automated and Accurate Compact Model with Physical Consistency

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Abstract
To overcome the difficulty in model parameter extraction and the narrow model coverage of physics-based compact models, artificial neural network-based compact models (ANN CM) have been developed. However, the ANN CM lacks physical consistency and sub-models for circuit simulations. In this work, we propose an innovative hybrid compact model that uses a physics-based compact model as a basis with accurate fitting capability over a wide range using two neural networks (NN). First, a bidirectional physics-based compact model generator neural network (PMG-NN) can automatically extract model parameter sets for both the drain current and gate capacitance within a few minutes. Second, a deep global correction neural network (DGC-NN) corrects the electrical inconsistencies caused by process variations to solve the narrow model coverage issues. The hybrid compact model (Hybrid CM) using DGC-NN can predict Ids, Cgg, Cgd, and Cgs of TCAD-simulated 3 nm nanosheet FETs (NSFETs) with greater than 98.5% accuracy. The hybrid compact model was implemented using Verilog-A and validated through the Gummel symmetry test and SPICE simulations. This new type of compact model has the potential to be the best solution for efficient process optimization and accurate SPICE simulation in real-world applications.
Funding Information
  • National Research Foundation of Korea (2020R1A5A1019649)
  • Ministry of Science and ICT, South Korea (2021-0-00754)

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