2021 — 2024 |
Li, Chen Hu, Jianjun (co-PI) [⬀] Hu, Ming Lee, Dongkyu |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Deep Learning Accelerated Inverse Design of Lab-Scale Energy Efficient Heterojunctions For Wide-Bandgap Devices @ University of South Carolina At Columbia
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.
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