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Statistical Compact Modeling with Artificial Neural Networks
  • Lining Zhang
Lining Zhang
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This letter proposes a machine learning (ML)-based method for statistical device modeling from a data-oriented perspective. A mapping from the process variation to the selected variational neurons is derived by the proposed algorithm and backward propagation of variation method (BPV) to capture the non-homogeneous effects of model parameters. The same size as the nominal ML model is used, with fewer variation parameters rather than numerous physical parameters, in favor of circuit simulation speed. In addition, a secondary classification of the selected variational neurons is applied to model the correlation between n- and p-type devices. The ML-based statistical modeling framework has been well implemented and verified on the GAA devices and circuits, which indicates its great potential in modeling of emerging device technology.