发表机构
Georgia Institute of Technology; Samsung Electronics Co., Ltd.; NVIDIA（佐治亚理工学院; 三星电子有限公司; 英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出首个智能体TCAD校准工作流，利用LLM编排和残差灵敏度测试，为氧化物半导体晶体管实现快速、可迁移的模型校准，目标函数降低14.3倍。
AI 中文摘要
实验性TCAD校准对于新兴氧化物半导体晶体管的预测性技术建模至关重要。然而，由于模型的不确定性，这一过程仍然耗时且依赖专家经验。多个物理模型和参数集可以重现相同的实测转移特性，而仅靠局部拟合无法唯一确定潜在的器件物理机制。我们首次展示了一种针对已制备的底栅In--W--O（BG-IWO）晶体管的智能体TCAD校准工作流。从实测转移曲线和器件信息出发，该工作流利用测量-TCAD残差和局部灵敏度测试来选择有界的参数修正或评估额外的物理模型，并且只接受能够改善器件指标的更新。LLM智能体编排整个工作流，而Sentaurus负责器件物理计算。对于2%钨含量的参考器件，五次智能体建议的更新产生了一个固定的校准模型，将多指标器件目标函数$J$降低了14.3倍。在变漏极偏压测试中，最大$V_{\mathrm{th}}$/$I_{\mathrm{on}}$误差为36.1 mV/0.022 decade；在变沟道长度测试中，最大误差为46.2 mV/0.062 decade，这证明了模型在偏压和几何结构上的可迁移性，而非局部参数拟合。钨含量测试提供了对工艺敏感性的洞察。该智能体工作流为新兴器件技术的模型开发提供了一条更快的途径。
英文摘要
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.
CommentsPreprint. Submitted to an IEEE journal for review
