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

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至关重要的作用。二维材料及其异质结为未来半导体器件的发展提供了广阔的材料和结构平台。尽管有很大的技术潜力，但它们的技术采用受到几个主要挑战的阻碍，包括准确预测设备性能和系统评估设备性能。在该项目中，将开发计算机辅助仿真和设计能力，通过将人工智能方法与先进的设备仿真相结合来应对这些挑战。人工智能引导的计算机仿真方法的发展将有助于快速准确地预测器件性能，并实现这些纳米级器件的自动高效设计。该项目将为人工智能方法在纳米电子学中的应用的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。在这个项目中开发的建模工具将作为一个开源的在线资源进行传播。该项目的目标是开发基于物理的机器学习（ML）模型和设备设计方法，用于有效和自动地模拟和设计范德华（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物理信息ML模型，用于量子输运设备模拟，考虑提高效率，尊重设备物理，减少所需的训练数据量；(2)通过同时考虑多个技术上重要的器件性能指标，开发一种多目标优化方法，系统评估和综合优化vdw材料和vdw异质结器件；(3)通过对量子输运方程及其求解算法应用自微分方法，开发了一种高效的基于梯度的逆设计方法，该方法与量子输运器件模拟相结合；(4)对所提出的方法进行了测试和应用，模拟和设计了一套vdw材料和vdw异质结器件，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库。该奖项反映了美国国家科学基金会的法定使命，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。

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.