发表机构
Purdue University; Imec USA; Imec; University of Granada; Applied Materials（普渡大学; imec美国分部; imec; 格拉纳达大学; 应用材料公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出首个基于机器学习的工艺技术协同优化框架，结合实验数据揭示二维TMD-FET的工艺-性能关联，可高效指导栅极堆叠优化。
AI 中文摘要
我们提出首个基于实验机器学习（ML）的工艺技术协同优化（PTCO）框架，用于直接从具有统计意义的实验数据而非纯模拟数据优化二维过渡金属二硫化物（TMD）场效应晶体管（FET）的制备。我们引入过渡电压指标VTrans，量化从关态到开态切换所需的栅极电压，并揭示其与亚阈值摆幅（SS）的直接关联，凸显一个同时影响关态和开态性能的被忽视的切换特性。该框架整合了自动化指标提取、多目标工艺配方排序和预测建模，可从有限的实验数据中揭示隐藏的工艺-性能关联，并预测未探索的制备工艺配方的性能。实验验证显示其与ML预测结果高度一致，证明该框架能通过迭代实验反馈高效指导栅极堆叠优化。
英文摘要
We present the first experimental machine learning (ML)-enabled Process-Technology Co-Optimization (PTCO) framework for optimizing 2D transition metal dichalcogenide (TMD) FET fabrication directly from statistically meaningful experimental data rather than pure simulation data. We first introduce a transition voltage metric, VTrans, to quantify the gate voltage required for off-to-on switching and reveal its direct correlation with subthreshold swing (SS), highlighting an overlooked switching characteristic that governs both off-state and on-state performance. By integrating automated metric extraction, multi-objective recipe ranking, and predictive modeling, our framework uncovers hidden process-performance correlations and predicts the performance of unexplored fabrication recipes from limited experimental data. Experimental validation shows close agreement with ML predictions, thus demonstrating the framework's ability to efficiently guide gate stack optimization through iterative experimental feedback.
