A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models

Aasim Ashai, Aakash Jadhav, Biplab Sarkar
1/25/2025
cs.LGeess.SP

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Abstract

A deep-learning (DL) based methodology for automated extraction of BSIM-CMG compact model parameters from experimental gate capacitance vs gate voltage (Cgg-Vg) and drain current vs gate voltage (Id-Vg) measurements is proposed in this paper. The proposed method introduces a floating normalization scheme within a cascaded forward and inverse ANN architecture enabling user-defined parameter extraction ranges. Unlike conventional DL-based extraction techniques, which are often constrained by fixed normalization ranges, the floating normalization approach adapts dynamically to user-specified ranges, allowing for fine-tuned control over the extracted parameters. Experimental validation, using a TCAD calibrated 14 nm FinFET process, demonstrates high accuracy for both Cgg-Vg and Id-Vg parameter extraction. The proposed framework offers enhanced flexibility, making it applicable to various compact models beyond BSIM-CMG.

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Cite this paper

@article{ashai2025floating,
  title  = {A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models},
  author = {Aasim Ashai and Aakash Jadhav and Biplab Sarkar},
  year   = {2025},
  eprint = {2501.15190},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.15190v1}
}

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