Deep Learning Accelerated Inverse Design of Lab-Scale Energy Efficient Heterojunctions for Wide-Bandgap Devices

宽带隙器件实验室规模节能异质结的深度学习加速逆向设计

基本信息

批准号：
2110033
负责人：
Ming Hu
金额：
$ 37.5万
依托单位：
University of South Carolina at Columbia
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110033&HistoricalAwards=false
关键词：
Deep Learning Accelerated Inverse Design

项目摘要

Efficient and reliable wide-bandgap high power transistors based on group III-nitrides (GaN, AlN, InN, etc.) are increasingly needed in today’s most demanding industries such as electric vehicles, data centers, radars, and consumer electronics. The functions of these systems rely heavily on the efficiency of removing excess heat from the devices’ active area, which is composed of function materials, substrates, and associated interfaces and heterojunctions, the dominant factor determining the overall interfacial thermal resistance (ITR) of the devices. Therefore, appropriate design and manufacturing of corresponding interfaces with minimized ITR are crucial to developing next-generation wide-bandgap devices. However, due to the immense search space of interfacial structures, it is impractical to evaluate all potential interfacial configurations using current trail-and-error experimental or computational approaches. One of the promising strategies is to use machine learning techniques, which are transforming the engineering field spanning from property predictions to inverse design. The overarching goal of this project is to develop novel deep neural network algorithms and workflow for the inverse design of lab-scale tailored interfacial structures to realize thermally efficient high-power wide-bandgap devices, along with experimental validation and demonstration. The success of this project will provide computational design tools and experimental fabrication protocols, not only to facilitate disruptive developments of key high-power electronic systems by breaking the bottleneck of thermal inefficiency issue, but also to speed up the material-to-industry processes. The project offers a unified platform to promote interdisciplinary collaborations spanning computational thermal science, experimental physics, and data science. The developed algorithms would benefit all engineers who study structure-device property relationships. This project will also increase public understanding and appreciation of machine learning for accelerating structure discovery and inspiring young researchers to pursue careers in STEM. Minority graduate students will get involved and trained in this interdisciplinary research project to strengthen high quality workforce in STEM.Aiming to address the main obstacles in the inverse design of heterojunctions for thermally efficient III-nitrides transistor devices, a set of key deep learning based techniques in the full inverse design pipeline will be developed: (1) deep neural network potentials will be developed, for calculating interatomic force constants to accurately and efficiently deal with large number of compositions with hundreds to thousands of nano-scale interfaces via nonequilibrium Green’s function method, which is not feasible for other traditional computational approaches. This will be facilitated by using frequency-resolved phonon transmission coefficient curves as the learning target in neural network training, which is the dominant factor in determining desired ITR across the functional interface or heterojunction and is unique for specific interface and provides more detailed hidden information of interfacial phonon transport. (2) powerful deep learning and spatial deep convolutional neural networks will be exploited in order to learn the features of phonon transmission curves and then unravel the complex, nonlinear, and usually implicit relationship between atomic structures of heterostructures and interfacial thermal resistance. (3) Genetic algorithms and the newest generative adversarial networks for the inverse design of hypothetical interfaces or heterojunctions will be developed. (4) A versatile and state-of-the-art technique, namely pulsed laser deposition, will be used to synthesize atomically thin films that have been theoretically proposed, with thickness approaching a monolayer, and their ITR will be validated. By combining computational thermal science with data science and experiment teams, this project will transform the study of complex interfacial thermal transport process using deep learning strategy and significantly accelerate the exploration process for optimal interfacial structures to achieve best thermal management performance and thus spur the practical implementation in semiconductor industry.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.

当今要求最严苛的行业，例如电动汽车、数据中心、雷达和消费电子产品，越来越需要基于 III 族氮化物（GaN、AlN、InN 等）的高效可靠的宽带隙高功率晶体管。很大程度上取决于从器件活性区域去除多余热量的效率，该活性区域由功能材料、基板以及相关界面和异质结组成，是决定整体界面热阻 (ITR) 的主导因素因此，适当设计和制造具有最小 ITR 的相应界面对于开发下一代宽带隙器件至关重要。然而，由于界面结构的搜索空间巨大，使用它来评估所有潜在的界面配置是不切实际的。当前的试错实验或计算方法之一是使用机器学习技术，该技术正在将工程领域从属性预测转变为逆向设计。该项目的总体目标是开发新型深度神经网络。算法和实验室规模定制界面结构逆向设计的工作流程，以实现热效率高功率宽带隙器件，以及实验验证和演示，该项目的成功将提供计算设计工具和实验制造协议，不仅有助于促进。该项目通过打破热效率低下的瓶颈，推动关键高功率电子系统的颠覆性发展，同时也加快了材料到工业的进程，提供了一个统一的平台，促进计算热科学、实验物理学和计算热科学等领域的跨学科合作。数据科学。开发的算法将使所有研究结构与器件属性关系的人受益，该项目还将提高公众对机器学习的理解和欣赏，以加速结构发现并激励年轻研究人员从事 STEM 职业，少数研究生将参与其中并接受培训。旨在加强 STEM 领域高素质劳动力的跨学科研究项目。为了解决热高效 III 族氮化物晶体管器件异质结逆向设计中的主要障碍，将开发一套完整逆向设计流程中基于深度学习的关键技术：（1）将开发深度神经网络势，用于计算原子间力常数，通过非平衡格林函数方法准确有效地处理具有数百至数千个纳米级界面的大量成分，这对于其他传统计算方法是不可行的这将通过使用频率分辨声子传输系数曲线作为神经网络训练的学习目标来促进，它是确定跨功能界面或异质结的所需 ITR 的主导因素，并且对于特定界面来说是唯一的，并提供更详细的信息。 (2)将利用强大的深度学习和空间深度卷积神经网络来学习声子传输曲线的特征，然后揭示异质结构的原子结构和原子结构之间复杂的、非线性的、通常隐含的关系。 (3) 将开发用于假设界面或异质结逆向设计的遗传算法和最新的生成对抗网络。最先进的技术，即脉冲激光沉积，将用于合成理论上提出的厚度接近单层的原子薄膜，并将通过计算热科学与数据科学相结合来验证其ITR。实验团队表示，该项目将利用深度学习策略转变对复杂界面热传输过程的研究，并显着反映对最佳界面结构以实现最佳热管理性能的探索过程，从而促进在半导体行业的实际实施。该奖项是 NSF 的法定使命并且已经通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Physics guided deep learning for generative design of crystal materials with symmetry constraints

物理引导深度学习用于具有对称约束的晶体材料的生成设计

DOI：
10.1038/s41524-023-00987-9
发表时间：
2023-03
期刊：
npj Computational Materials
影响因子：
9.7
作者：
Zhao, Yong;Siriwardane, Edirisuriya M. Dilanga;Wu, Zhenyao;Fu, Nihang;Al;Hu, Ming;Hu, Jianjun
通讯作者：
Hu, Jianjun

Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques

使用机器学习技术根据基本材料特性预测晶格热导率

DOI：
10.1039/d2ta08721a
发表时间：
2023-03
期刊：
Journal of Materials Chemistry A
影响因子：
11.9
作者：
Qin, Guangzhao;Wei, Yi;Yu, Linfeng;Xu, Jinyuan;Ojih, Joshua;Rodriguez, Alejandro David;Wang, Huimin;Qin, Zhenzhen;Hu, Ming
通讯作者：
Hu, Ming

Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach

通过间接自下而上的机器学习方法解锁大量多样的立方晶体的声子特性

DOI：
10.1038/s43246-023-00390-3
发表时间：
2023-12
期刊：
Communications Materials
影响因子：
7.8
作者：
Rodriguez, Alejandro;Lin, Changpeng;Shen, Chen;Yuan, Kunpeng;Al;Zhang, Xiaoliang;Zhang, Hongbin;Hu, Ming
通讯作者：
Hu, Ming

Screening Outstanding Mechanical Properties and Low Lattice Thermal Conductivity Using Global Attention Graph Neural Network

使用全局注意力图神经网络筛选出色的机械性能和低晶格导热系数