用自适应归一化提升芯片模型参数提取精度
A Floating Normalization Scheme for Deep Learning-Based Custom-Range Parameter Extraction in BSIM-CMG Compact Models
- 在前后向神经网络中引入可浮动的归一化机制
- 14纳米鳍式晶体管测试验证，参数提取准确率高
- 支持用户自定义参数范围，适配多种芯片模型
本文提出一种基于深度学习的自动化方法，从实验测得的栅电容-栅压（Cgg-Vg）和漏极电流-栅压（Id-Vg）数据中提取BSIM-CMG紧凑模型参数。该方法在级联前向与反向人工神经网络架构中引入浮动归一化方案，支持用户自定义参数提取范围。与传统深度学习方法依赖固定归一化范围不同，该方法可动态适应用户指定区间，实现对提取参数的精细调控。基于TCAD校准的14纳米鳍式晶体管工艺进行实验验证，Cgg-Vg与Id-Vg参数提取均表现出高精度。所提框架具有更强灵活性，可推广至其他紧凑模型应用。
原文摘要 · Abstract (English)
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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