CDS&E: Machine-Learning-Driven Methods for Multiobjective and Inverse Design of van-der-Waals-Material-Based Devices

CDS

• 批准号: 2203625
• 负责人: Jing Guo
• 金额: $ 33.5万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203625&HistoricalAwards=false
• 关键词: CDS& Machine; Learning; Driven; Methods

Advanced semiconductor device technologies play a critical role in computing, data storage, artificial intelligence (AI), and quantum technologies. Two-dimensional materials and their heterojunctions form a promising material and structure platform to develop future semiconductor devices. Despite promising technological potentials, their technological adoption has been hindered by several major challenges including predicting device properties accurately and assessing device performance systematically. In this project, computer-aided simulation and design capabilities will be developed to address these challenges by combining AI methods with advanced device simulation. The development of AI-guided computer simulation methods will facilitate fast and accurate prediction of device properties, and enable automatic and efficient design of these nanoscale devices. This project will result in an integrated testbed for research and education on applications of AI methods in nanoelectronics. The project will engage and train high school, undergraduate, and graduate students in the fields of semiconductor and AI technologies. The modeling tools developed in this project will be disseminated as an open-source online resource.The goals of the project are to develop physics-informed machine-learning (ML) models and device design methods for efficient and automatic simulation and design of van der Waals (wdW) semiconductor devices. The proposed research activities include: (1) develop physics-informed ML models in an embedded or hybrid manner for quantum transport device simulations, with consideration of improving efficiency, respecting device physics, and reducing the amount of training data required; (2) develop a multi-objective optimization method to systematically assess and comprehensively optimize vdW-material and vdW-heterojunction devices, by simultaneously considering multiple technologically important device performance metrics; (3) develop an efficient gradient-based inverse design method that is integrated with quantum transport device simulations, by applying auto-differentiation methods over the quantum transport equation and its solution algorithms; (4) test and apply the methods proposed to simulate and design a set of vdW-material and vdW-heterojunction devices, which have shown promising logic and memory device performance. The project develops an essential knowledge base for harnessing rapidly developing machine learning methods and theory to advance the modeling, simulation, and design capabilities of nanoelectronic devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

先进的半导体器件技术在计算、数据存储、人工智能（AI）和量子技术中发挥着关键作用。二维材料及其异质结为未来半导体器件的发展提供了一个很有前途的材料和结构平台。尽管有很好的技术潜力，但它们的技术采用受到几个主要挑战的阻碍，包括准确预测器件特性和系统地评估器件性能。在该项目中，将开发计算机辅助模拟和设计能力，通过将人工智能方法与先进的器件模拟相结合来应对这些挑战。人工智能引导的计算机模拟方法的发展将有助于快速准确地预测器件特性，并实现这些纳米器件的自动有效设计。该项目将为人工智能方法在纳米电子学中的应用研究和教育提供一个综合测试平台。该项目将吸引和培训半导体和人工智能技术领域的高中生、本科生和研究生。本项目开发的建模工具将作为开放源代码在线资源进行传播。本项目的目标是开发基于物理的机器学习（ML）模型和器件设计方法，以便高效自动地模拟和设计货车德瓦尔斯（wdW）半导体器件。拟议的研究活动包括：（1）以嵌入式或混合方式开发基于物理学的ML模型，用于量子传输设备模拟，同时考虑提高效率、尊重设备物理特性并减少所需的训练数据量;（2）开发多目标优化方法，系统评估和综合优化vdw材料和vdw异质结器件，通过同时考虑多个技术上重要的器件性能指标;（3）通过在量子输运方程及其求解算法上应用自微分方法，开发与量子输运器件模拟集成的有效的基于梯度的逆设计方法;（4）测试和应用所提出的方法，模拟和设计了一组vdw材料和vdw异质结器件，这些器件显示出良好的逻辑和存储器件性能。该项目为利用快速发展的机器学习方法和理论来推进纳米电子器件的建模、仿真和设计能力开发了一个重要的知识基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Ab Initio Computational Screening and Performance Assessment of van der Waals and Semimetallic Contacts to Monolayer WSe2 P-Type Field-Effect Transistors

• DOI: 10.1109/ted.2023.3241569
• 发表时间: 2023-04
• 期刊: IEEE Transactions on Electron Devices
• 影响因子: 3.1
• 作者: Ning Yang;Y. Lin;Chih-Piao Chuu;M. Rahman;Tong Wu;Ang-Sheng Chou;Hung-Yu Chen;W. Woon;S. Liao;Shengxi Huang;Xiaofeng Qian;Jing Guo;I. Radu;H. P. Wong;Han Wang

Van der Waals Heterostructure Engineering for Ultralow-Resistance Contact in 2D Semiconductor P-Type Transistors

• DOI: 10.1007/s11664-024-10920-5
• 发表时间: 2024-01
• 期刊: Journal of Electronic Materials
• 影响因子: 2.1
• 作者: Ning Yang;Ting-Hao Hsu;Hung-Yu Chen;Jian Zhao;Hongming Zhang;Han Wang;Jing Guo

Phase Transition of MoTe 2 Controlled in van der Waals Heterostructure Nanoelectromechanical Systems

范德华异质结构纳米机电系统中 MoTe 2 相变的控制

• DOI: 10.1002/smll.202205327
• 发表时间: 2022
• 期刊: Small
• 影响因子: 13.3
• 作者: Ye, Fan;Islam, Arnob;Wang, Yanan;Guo, Jing;Feng, Philip X. ‐L.
• 通讯作者: Feng, Philip X. ‐L.