I/UCRC: Phase I: Center for Advanced Electronics through Machine Learning (CAEML)

I/UCRC：第一阶段：机器学习先进电子学中心 (CAEML)

• 批准号: 1624811
• 负责人: Maxim Raginsky
• 金额: $ 60万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1624811&HistoricalAwards=false
• 关键词: UCRC; Phase; Center; Advanced; Electronics

The semiconductor industry is perennially one of America's top exporters. Worldwide semiconductor sales for 2014 reached $335.8 billion, and the number of U.S. jobs in this sector was estimated to be around 250,000 in 2013. More broadly, the U.S. tech industry, which depends on semiconductor innovation to spur new products and applications, is itself estimated to represent no less than 5.7% of the entire U.S. private sector workforce (at nearly 6.5 million jobs), and with a tech industry payroll of $654 billion in 2014, it accounted for over 11% of all U.S. private sector payroll. Yet despite its success, the industry must continue to innovate if the U.S. is to retain global leadership in this highly competitive area. The complexity of modern microelectronic products necessitates the use of computer tools to formulate and verify product designs prior to manufacturing. When a product doesn't operate as intended or suffers early failures, this can often be attributed to inadequacy of the models used during the design process. In fact, the shortcomings of existing approaches for system component modeling have become a serious impediment to continued innovation. The Center for Advanced Electronics through Machine Learning (CAEML) proposes to create machine-learning algorithms to derive models used for electronic design automation with the objective of enabling fast, accurate design of microelectronic circuits and systems. Success will make it much easier and cheaper to optimize a system design, allowing the industry to produce lower-power and lower-cost electronic systems without sacrificing functionality. The eventual result will be significant growth in capabilities that will drive innovation throughout the electronics industry, leading to new devices and applications, continued entrepreneurial leadership, and economic growth. While achieving those goals, CAEML will also focus on diversifying the undergraduate engineering student body and improving the undergraduate experience. Students from groups traditionally underrepresented in engineering will be targeted for recruitment as undergraduate research assistants. Member companies will provide internships and mentors for participating students, and the diverse graduate and undergraduate student researchers in CAEML will receive hands-on multidisciplinary education. CAEML will also participate in all three site universities' existing avenues for student and faculty engagement with local youth. In particular, university-based summer camps are a tried and tested method of making high-school students familiar with and comfortable on our campuses. The Girls' Adventures in Mathematics, Engineering, and Science (GAMES) summer camp program at the University of Illinois at Urbana-Champaign ("Illinois") brings high-school girls to campus for a week of hands-on engineering activities and camaraderie. The engineering content for many of the GAMES camps, including the one on electrical engineering, is developed by engineering faculty. CAEML undergraduate and graduate students can serve as counselors or instructors for camps; the CAEML team proposes to develop new activities and workshops for high-school campers on all three sites' campuses. In addition, the Beginning Teacher STEM Conference at Illinois brings 150 teachers who have just completed their first year in the classroom to the Urbana-Champaign campus for 2 days to deepen their knowledge of STEM fields and try out activities for use in their classrooms; several of the sessions are taught by College of Engineering faculty including those affiliated with CAEML. The Center for Advanced Electronics through Machine Learning (CAEML) will create machine-learning algorithms to derive models used for electronic design automation, with the objective of enabling fast, accurate design of microelectronic circuits and systems. The electronics industry's continued ability to innovate requires the creation of optimization methodologies that result in low-power integrated systems that meet performance specifications, despite being composed of components whose characteristics exhibit variability and that operate in different physical or signal domains. Today, shortcomings in accuracy and comprehensiveness of component-level behavioral models impede the advancement of computer-aided electronic system design optimization. The model accuracy also impacts system verification. Ultimately, the proper functionality of an electronic system is verified through testing of a representative sample. However, modern electronic systems are so complex that it is unthinkable to bring one to the manufacturing stage without first verifying its operation using simulation. Today, simulation generally does not ensure that an integrated circuit or electronic system will pass qualification testing the first time, and failures are often attributed to insufficiency of the simulation models. With an improved modeling capability, one could achieve better design efficiency, and also perform design optimization. For system simulation, behavioral models of the components' terminal responses are desired for both computational tractability and protection of intellectual property. Despite many years of significant effort by the electronic design automation community, there is not a general, systematic method to generate accurate and comprehensive behavioral models, in part because of the nonlinear, complex, and multi-port nature of the components being modeled.CAEML will pioneer the use of machine-learning methods to extract behavioral models of electronic components and subsystems from simulation waveforms and/or measurement data. The Center will make 2 primary contributions to the field of machine learning: it will demonstrate the application of machine learning to electronics modeling, and develop the entire machine-learning pipeline. Historically, machine-learning theorists have focused on the model learning and evaluation tasks, but CAEML will focus on end-to-end performance of the pipeline, including data acquisition, selection and filtering, as well as cost function specification. CAEML will develop a methodology to use prior knowledge, i.e., physical constraints and the domain knowledge provided by designers, to speed up the learning process. Novel methods of incorporating component variability, including that due to semiconductor process variations, will be developed. The intended end-users are electronic design automation (EDA) tool developers, IC design houses, and system design and manufacturing companies.CAEML consists of 3 sites: Illinois, Georgia Tech, and NC State. The scope of research at each site encompasses both algorithm development and the application of the derived models to a variety of IC and system design tasks. Investigators at all 3 university sites have unique skills and expertise while sharing interests in electronic design automation, IC design, system-level signal integrity, and power distribution. To leverage the cross-campus expertise, many of the Center's proposed projects involve investigators from more than one site. The Illinois investigators have special expertise in computational electromagnetics, electrostatic discharge (ESD), and optimization; they bring capabilities in areas such as circuit design for ESD-induced error detection, computationally-efficient stochastic electromagnetic field simulation, reduced-order modeling and behavioral modeling of electrical/electromagnetic circuits and systems, and multi-domain physics modeling in the presence of uncertainty and variability. All three sites have strong research records in the fields of signal integrity analysis and electronic design automation. Excellent computational resources are available at Illinois for the proposed work; the necessary test and measurement equipment is also available, including a system-level ESD test-bed.

项目成果

Surrogate Modeling with Complex-valued Neural Nets and its Application to Design of sub-THz Patch Antenna-in-Package

• DOI: 10.1109/ims37964.2023.10187990
• 发表时间: 2023-06
• 期刊: 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023
• 作者: O. Akinwande;Osama Waqar Bhatti;Kai-Qi Huang;Xingchen Li;Madhavan Swaminathan