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

FMSG：网络：通过仿真增强机器学习实现宽带隙半导体器件的网络制造

• 批准号: 2134374
• 负责人: Yuhao Zhang
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 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: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134374&HistoricalAwards=false
• 关键词: FMSG; Cyber; Cybermanufacturing; Wide; Bandgap

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.

半导体行业是最大的制造业之一，年收入接近5000亿美元。半导体器件是通过多个工艺步骤在大直径晶圆上制造的。良率是决定半导体制造成功与否的关键指标。当前的良率管理实践依赖于最大限度地减少晶圆材料的不均匀性，最大限度地提高每个步骤的工艺控制，并根据领域专业知识对整个晶圆应用必要的工艺调整。然而，由于材料和工艺不太成熟，新兴半导体器件（例如宽带隙（WBG）器件）在晶圆代工厂的制造良率仅为 50-80%。虽然宽带隙器件在电动汽车、数据中心、5G 通信和电网等应用中得到快速采用，但其制造产量有限已成为一个日益严重的问题。这个未来制造种子补助金 (FMSG) 网络制造项目建议在物理仿真增强机器的指导下，在每个工艺步骤中通过基于芯片或设备（而不是基于晶圆）的适应来实现半导体设备的自我预测和自适应网络制造学习（ML）框架。在这种目前尚不存在的半导体网络制造中，每个工艺步骤都应用了设备间的几何形状和设计适应，以智能地补偿固有材料属性和历史工艺步骤的变化。这笔种子资金将使用 WBG 功率二极管的小规模制造作为演示工具，以建立与自适应半导体制造中的机器学习集成相关的知识库。新的制造范式有可能导致机器学习和半导体交叉领域新产业的形成。该项目还提供了一个独特的场所来培训未来的技术人员，使其具备解决机器学习引导的半导体制造中跨学科问题的能力。这个跨学科项目将用于支持本科生研究活动和 K-12 学生的外展活动。 该种子基金的目的是利用垂直氮化镓功率二极管的小规模制造作为演示工具，确定并解决与半导体网络制造相关的基本知识差距，这是一种用于电动汽车功率应用的新兴宽带隙器件和电网。该项目的智力优点植根于半导体器件制造的全新理念，即通过分析和预测机器学习模型产生的芯片到芯片、设备到设备的适应。为了实现这种新的制造范式，该项目将重点解决以下问题：（a）将探索新的数据框架，以开发适用于物理电子设备的机器学习模型。使用计算机辅助设计模拟技术，物理模拟数据将增加 1,000-10,000 倍的实验设备数据，这些数据在成本和时间方面都很昂贵。 (b) 将探索创新的机器学习模型用于正向过程（根据一组给定的材料/设备参数预测设备性能指标）和逆向过程（根据给定的设备特性推断未来的过程参数、测量的历史过程步骤参数，以及设计目标）。 (c) 所提出的框架将通过测试车辆上的试点制造进行实验验证，并对最终产量的提高进行表征和评估。该未来制造项目由工程局 ECCS 和 CMMI 部门以及数学和物理科学理事会的 CHE 部门。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Rapid Inverse Design of GaN-on-GaN Diode with Guard Ring Termination for BV and (V F Q) −1 Co-Optimization

具有保护环终端的 GaN-on-GaN 二极管的快速逆向设计，用于 BV 和 (V F Q) â1 协同优化

• DOI: 10.1109/ispsd57135.2023.10147511
• 发表时间: 2023-05
• 期刊: 2023 35th International Symposium on Power Semiconductor Devices and ICs (ISPSD
• 作者: Yee, Nathan;Lu, Albert;Wang, Yifan;Porter, Matthew;Zhang, Yuhao;Wong, Hiu Yung
• 通讯作者: Wong, Hiu Yung

TCAD Simulation Models, Parameters, and Methodologies for β-Ga 2 O 3 Power Devices

β-Ga 2 O 3 功率器件的 TCAD 仿真模型、参数和方法

• DOI: 10.1149/2162-8777/accfbe
• 发表时间: 2023-05
• 期刊: ECS Journal of Solid State Science and Technology
• 影响因子: 2.2
• 作者: Wong; Hiu Yung
• 通讯作者: Hiu Yung

Vertical GaN diode BV maximization through rapid TCAD simulation and ML-enabled surrogate model

通过快速 TCAD 仿真和支持 ML 的替代模型实现垂直 GaN 二极管 BV 最大化

• DOI: 10.1016/j.sse.2022.108468
• 发表时间: 2022-12
• 期刊: Solid-State Electronics
• 影响因子: 1.7
• 作者: Lu, Albert;Marshall, Jordan;Wang, Yifan;Xiao, Ming;Zhang, Yuhao;Wong, Hiu Yung
• 通讯作者: Wong, Hiu Yung

Robust Avalanche in 1.7 kV Vertical GaN Diodes with a Single-Implant Bevel Edge Termination

具有单注入斜边端接的 1.7 kV 垂直 GaN 二极管中的稳健雪崩

• DOI: 10.1109/led.2023.3302312
• 发表时间: 2023-01
• 期刊: IEEE Electron Device Letters
• 影响因子: 4.9
• 作者: Xiao, Ming;Wang, Yifan;Zhang, Ruizhe;Song, Qihao;Porter, Matthew;Carlson, Eric;Cheng, Kai;Ngo, Khai;Zhang, Yuhao
• 通讯作者: Zhang, Yuhao

Study of Vertical Ga 2 O 3 FinFET Short Circuit Ruggedness using Robust TCAD Simulation

使用稳健 TCAD 仿真研究垂直 Ga 2 O 3 FinFET 短路耐用性

• DOI: 10.1149/2162-8777/ac9e73
• 发表时间: 2022-11
• 期刊: ECS Journal of Solid State Science and Technology
• 影响因子: 2.2
• 作者: Lu, Albert;Elwailly, Adam;Zhang, Yuhao;Wong, Hiu Yung
• 通讯作者: Wong, Hiu Yung