A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913667069509632 |
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| author | Ashai, Aasim Jadhav, Aakash Sarkar, Biplab |
| author_facet | Ashai, Aasim Jadhav, Aakash Sarkar, Biplab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models Ashai, Aasim Jadhav, Aakash Sarkar, Biplab Machine Learning Signal Processing 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. |
| title | A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.15190 |