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.

高级半导体设备技术在计算，数据存储，人工智能（AI）和量子技术中起着至关重要的作用。二维材料及其异质结构构成了开发未来半导体设备的有前途的材料和结构平台。尽管有希望的技术潜力，但它们的技术采用受到了一些主要挑战的阻碍，包括准确预测设备的性能并系统地评估设备性能。在此项目中，将开发计算机辅助的模拟和设计功能，以通过将AI方法与高级设备仿真相结合，以应对这些挑战。 AI引导的计算机仿真方法的开发将有助于快速准确地预测设备属性，并启用这些纳米级设备的自动和高效设计。该项目将为AI方法在纳米电子学中的应用中进行研究和教育的集成测试床。该项目将在半导体和AI技术领域互动和培训高中，本科和研究生。该项目中开发的建模工具将作为开源在线资源传播。该项目的目标是开发物理知识的机器学习（ML）模型和设备设计方法，以进行有效，自动模拟和设计范德瓦尔斯（WDW）半导体设备。拟议的研究活动包括：（1）以嵌入式或混合方式开发物理知识的ML模型，以进行量子运输设备模拟，并考虑提高效率，尊重设备物理学并减少所需的培训数据量； （2）开发一种多目标优化方法，通过同时考虑多个技术上重要的设备性能指标来系统地评估并全面地评估和全面地优化VDW - 材料和VDW - 重合结构设备； （3）开发一种有效的基于梯度的逆设计方法，该方法与量子传输设备模拟集成在一起，通过在量子传输方程及其溶液算法上应用自动分化方法； （4）测试并应用提出的方法来模拟和设计一组VDW - 材料和VDW - 直接结构设备，这些设备显示出了有希望的逻辑和内存设备性能。该项目开发了一个基本的知识库，用于利用快速开发机器学习方法和理论，以推动纳米电子设备的建模，模拟和设计能力。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。

项目成果

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.