FMSG: Cyber: Cybermanufacturing of Wide-Bandgap Semiconductor Devices Enabled by Simulation Augmented Machine Learning

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Semiconductor industry is one of the largest manufacturing industries with annual revenue approaching $500 billion. Semiconductor devices are manufactured on large-diameter wafers through multiple process steps. Yield is a key metric determining the success in semiconductor manufacturing. The current practice of yield management relies on minimizing the wafer material non-uniformity, maximizing the process control in every step, and applying necessary process adaptions to the entire wafer based on domain expertise. However, the manufacturing yield of emerging semiconductor devices, e.g., wide-bandgap (WBG) devices, is merely 50-80% in the foundry, due to less mature materials and processes. While WBG devices are gaining quick adoption in applications like electric vehicles, data centers, 5G communications, and power grids, the limited yield of their manufacturing has become an increasingly serious concern. This Future Manufacturing Seed Grant (FMSG) CyberManufacturing project suggests the self-predictive and self-adaptive cybermanufacturing of semiconductor devices implemented through die- or device-based (instead of wafer-based) adaptions in each process step guided by a physical simulation augmented machine learning (ML) framework. In this semiconductor cybermanufacturing, which does not exist today, device-to-device adaptions in geometrics and designs are applied in each process step to intelligently compensate for the variability in inherent material properties and historical process steps. This seed grant will use the small-scale fabrication of WBG power diodes as a demonstration vehicle to establish the knowledge base related to the integration of ML in adaptive semiconductor manufacturing. The new manufacturing paradigm can potentially lead to the formation of new industries at the intersection of ML and semiconductors. This project also presents a unique venue to train future technicians with the capabilities of tackling interdisciplinary problems in ML-guided semiconductor manufacturing. This interdisciplinary project will be utilized to support undergraduate research activities and outreach activities for K-12 students. The objective of this seed grant is to identify and address the fundamental knowledge gaps related to the semiconductor cybermanufacturing, using the small-scale fabrication of vertical gallium nitride power diodes as a demonstration vehicle, which is an emerging WBG device for power applications in electric vehicles and power grids. The intellectual merits of this project are rooted in the fundamentally new philosophy for semiconductor device manufacturing, i.e., the die-to-die, device-to-device adaptions produced by analytic and predictive ML models. To realize this new manufacturing paradigm, this project will focus on tacking the following problems: (a) New data frameworks will be explored for the development of ML models applicable to physical electronic devices. Experimental device data, which are expensive in terms of cost and time, will be augmented by physical simulation data by 1,000-10,000 times using the Technology Computer-Aided Design simulations. (b) Innovative ML models will be explored for the forward process (predict device performance metrics from a given set of material/device parameters) and inverse process (deduce future process parameters for the given device characteristics, the measured historical process step parameters, and the design objectives). (c) The proposed framework will be experimentally demonstrated through pilot manufacturing on the test vehicle, and the final yield enhancement will be characterized and evaluated.This Future Manufacturing project is jointly funded by the Divisions of ECCS and CMMI in the Directorate of Engineering and the Division of CHE in the Directorate for Mathematical and Physical Sciences.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.