Machine learning augmented TCAD assessment of corner radii in nanosheet FET

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

Solid-state Electronics Pub Date : 2025-04-04 DOI:10.1016/j.sse.2025.109114

Jyoti Patel , Bathula Satwik , Navjeet Bagga , Ishani Bais , Chirag Arora , Vivek Kumar , Ankit Dixit , Naveen Kumar , Vihar Georgiev , S. Dasgupta

引用次数: 0

Abstract

In this paper, we proposed a machine learning approach to assist the TCAD results in realizing a local cost and time-effective simulator for analyzing the performance metric of the vertically stacked Nanosheet FET (NSFET). The corners are responsible for field crowding inside the sheets, which significantly affects the parasitic capacitance and thereby reduces the I_ON/C_gg ratio. Thus, a detailed insight into corner radii optimization is worth needed. We used Sentaurus TCAD to obtain the results and further realized a local simulator using an XGBoost model to analyze process variations and the role of uneven radii corners in NSFET. In addition, a data augmentation strategy is proposed that leverages the powers of stacked autoencoders (SAE) and InfoGANs to enhance data generalization, improving model robustness and predictive reliability.

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机器学习增强了纳米片场效应管拐角半径的TCAD评估

在本文中，我们提出了一种机器学习方法来帮助TCAD结果实现一个局部成本和时间有效的模拟器，用于分析垂直堆叠纳米片场效应管（NSFET）的性能指标。边角导致片内的场拥挤，这显著影响寄生电容，从而降低了ION/Cgg比率。因此，需要详细了解拐角半径优化。我们使用Sentaurus TCAD获得了结果，并进一步利用XGBoost模型实现了局部模拟器，分析了过程变化以及不均匀半径角在NSFET中的作用。此外，提出了一种数据增强策略，利用堆叠自编码器（SAE）和InfoGANs的功能来增强数据泛化，提高模型的鲁棒性和预测可靠性。

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.