Agentic TCAD Calibration Workflow for Oxide Semiconductor Transistors

2026-09-10

AI Summary

为解决氧化物半导体晶体管TCAD校准耗时且依赖专家的问题，提出了一种基于代理的自动化校准流程，通过测量-TCAD残差和局部灵敏度测试优化模型参数。

Abstract

Experimental TCAD calibration is essential for predictive technology modeling of emerging oxide semiconductor transistors. However, it remains time-consuming and expert dependent because of model ambiguity. Multiple physical models and parameter sets can reproduce the same measured transfer characteristics, while local fitting alone cannot uniquely identify the underlying device physics. We present the first demonstration of an agentic TCAD calibration workflow for a fabricated bottom-gate In--W--O (BG-IWO) transistor. Starting from the measured transfer curve and device information, the workflow uses measurement--TCAD residuals and local sensitivity tests to select bounded parameter corrections or evaluate additional physical models, and accept only updates that improve device metrics. The LLM agent orchestrates the workflow, while Sentaurus governs the device physics. For the 2\%-W reference device, five agent-suggested updates yield a fixed calibrated model, reducing the multi-metric device objective $J$ by 14.3$\times$. Maximum $V_{\mathrm{th}}$/$I_{\mathrm{on}}$ errors are 36.1~mV/0.022 decade for varying-drain-bias tests and 46.2~mV/0.062 decade for varying-channel-length tests, demonstrating model transferability across bias and geometry rather than a local parameter fit. W-composition tests provide process-sensitive insight. This agentic workflow provides a faster route to model development for emerging device technologies.

Problem

• TCAD Calibration
• Oxide Semiconductor Transistors
• Model Ambiguity

Innovation

• agentic TCAD calibration
• bottom-gate In-W-O transistor
• measurement-TCAD residuals
• local sensitivity tests
• LLM agent