ANN-based framework for modeling process induced variation using BSIM-CMG unified model

IF 1.4 4区物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Solid-state Electronics Pub Date : 2024-07-22 DOI:10.1016/j.sse.2024.108988

引用次数: 0

Abstract

In this work, we present a machine-learning augmented compact modeling framework for modeling process induced variations in advanced semiconductor devices. The framework employs BSIM-CMG unified compact model at the core and can be used for any advanced devices like GAA nanosheets and nanowires, FinFETs etc. We have validated the model with extensive numerical simulations and experimental data such as $14 nm$ technology FinFET and $24 nm$ technology Nanowire. Our results show excellent accuracy in modeling variability in key electrical parameters of the device including off-current (I_off), on-current (I_on), threshold voltage (V_th), subthreshold swing (SS) etc. We observe that the overall accuracy of the ML-based framework strongly depends on the nature and physical behavior of the core model used for modeling the nominal device.

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利用 BSIM-CMG 统一模型，建立基于 ANN 的流程诱导变异建模框架

在这项工作中，我们提出了一个机器学习增强紧凑建模框架，用于对先进半导体器件的工艺诱导变化进行建模。该框架以 BSIM-CMG 统一紧凑模型为核心，可用于 GAA 纳米片和纳米线、FinFET 等任何先进器件。我们通过大量的数值模拟和实验数据（如 14 纳米技术 FinFET 和 24 纳米技术纳米线）验证了该模型。我们的结果表明，该模型在模拟关断电流 (Ioff)、导通电流 (Ion)、阈值电压 (Vth)、亚阈值电压摆幅 (SS) 等器件关键电气参数的变化方面具有出色的准确性。我们注意到，基于 ML 的框架的整体准确性在很大程度上取决于用于标称器件建模的内核模型的性质和物理行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Solid-state Electronics 物理-工程：电子与电气

CiteScore

3.00

自引率

5.90%

发文量

212

审稿时长

3 months

期刊介绍： It is the aim of this journal to bring together in one publication outstanding papers reporting new and original work in the following areas: (1) applications of solid-state physics and technology to electronics and optoelectronics, including theory and device design; (2) optical, electrical, morphological characterization techniques and parameter extraction of devices; (3) fabrication of semiconductor devices, and also device-related materials growth, measurement and evaluation; (4) the physics and modeling of submicron and nanoscale microelectronic and optoelectronic devices, including processing, measurement, and performance evaluation; (5) applications of numerical methods to the modeling and simulation of solid-state devices and processes; and (6) nanoscale electronic and optoelectronic devices, photovoltaics, sensors, and MEMS based on semiconductor and alternative electronic materials; (7) synthesis and electrooptical properties of materials for novel devices.