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

半导体行业一直是美国最大的出口行业之一。2014年全球半导体销售额达到3358亿美元，2013年美国该行业的就业人数估计约为25万人。更广泛地说，美国科技行业依赖于半导体创新来刺激新产品和应用，据估计，该行业本身就占美国整个私营部门劳动力的5.7%（近650万个就业岗位），2014年科技行业的工资总额为6540亿美元，占美国所有私营部门工资总额的11%以上。然而，尽管取得了成功，但如果美国想要在这个竞争激烈的领域保持全球领先地位，该行业必须继续创新。现代微电子产品的复杂性需要在制造之前使用计算机工具来制定和验证产品设计。当产品不能按预期运行或遭受早期故障时，这通常可归因于设计过程中使用的模型不充分。事实上，现有系统组件建模方法的缺点已经成为持续创新的严重障碍。通过机器学习的高级电子中心（CAEML）提议创建机器学习算法，以导出用于电子设计自动化的模型，目标是实现快速，准确的微电子电路和系统设计。成功将使优化系统设计变得更加容易和便宜，使行业能够在不牺牲功能的情况下生产更低功耗和更低成本的电子系统。最终的结果将是能力的显著增长，这将推动整个电子行业的创新，导致新的设备和应用，持续的企业领导和经济增长。在实现这些目标的同时，CAEML还将专注于多元化本科工程学生群体并改善本科体验。来自传统上在工程领域代表性不足的群体的学生将成为本科研究助理的招聘目标。成员公司将为参与的学生提供实习机会和导师，CAEML的研究生和本科生研究人员将接受多学科的实践教育。CAEML还将参与所有三所基地大学现有的学生和教师与当地青年互动的渠道。特别是，以大学为基础的夏令营是一种久经考验的方法，可以让高中生熟悉并适应我们的校园。佐治亚理工学院的HOT DAYS （https://www.ece.gatech.edu/outreach/hot-days）夏令营是一个为期一周的暑期项目，旨在通过各种半天的模块向高中生介绍电气和计算机工程概念，包括建造计算机、与机器人合作、使用音乐合成技术、构建简单的数字逻辑电路和用普通家用物品构建扬声器。将开发涵盖CAEML研究领域的其他模块，并将其纳入营地的时间表。CAEML的本科生和研究生可以担任夏令营的辅导员或辅导员。佐治亚理工学院的综合科学、数学和计算教育中心（CEISMC）为K-12教师和学生举办了各种各样的营地和项目。佐治亚理工学院CAEML的教师将参与这项工作，以及美国国家科学基金会资助的暑期教师包装经验，利用物理（stepup）计划（https://www.ece.gatech.edu/outreach/step-up-program），这是一个为期八周的亚特兰大大都会高中物理教师研究经验。通过机器学习的先进电子中心（CAEML）将创建机器学习算法，以导出用于电子设计自动化的模型，其目标是实现快速，准确的微电子电路和系统设计。电子行业的持续创新能力需要优化方法的创造，从而产生符合性能规范的低功耗集成系统，尽管组件的特性表现出可变性，并且在不同的物理或信号域中工作。目前，构件级行为模型在准确性和全面性方面的不足阻碍了计算机辅助电子系统设计优化的发展。模型的准确性也会影响系统的验证。最终，电子系统的正常功能是通过一个代表性样品的测试来验证的。然而，现代电子系统是如此复杂，如果不首先使用仿真验证其操作，将其带入制造阶段是不可想象的。今天，仿真通常不能保证集成电路或电子系统第一次就能通过资格测试，而失败往往归因于仿真模型的不足。通过提高建模能力，可以提高设计效率，并进行设计优化。在系统仿真中，为了计算的可追溯性和知识产权的保护，需要构件终端响应的行为模型。尽管电子设计自动化社区多年来做出了重大的努力，但没有一个通用的、系统的方法来生成准确和全面的行为模型，部分原因是建模组件的非线性、复杂和多端口性质。CAEML将率先使用机器学习方法从仿真波形和/或测量数据中提取电子元件和子系统的行为模型。该中心将对机器学习领域做出两项主要贡献：它将展示机器学习在电子建模中的应用，并开发整个机器学习管道。从历史上看，机器学习理论家专注于模型学习和评估任务，但CAEML将专注于管道的端到端性能，包括数据采集、选择和过滤，以及成本函数规范。CAEML将开发一种方法来使用先验知识，即物理约束和设计师提供的领域知识，以加快学习过程。将开发结合元件可变性的新方法，包括由于半导体工艺变化引起的可变性。预期的最终用户是电子设计自动化（EDA）工具开发人员、IC设计公司以及系统设计和制造公司。CAEML由3个站点组成：伊利诺伊州，乔治亚理工学院和北卡罗来纳州立大学。每个站点的研究范围包括算法开发和衍生模型在各种集成电路和系统设计任务中的应用。这三所大学的研究人员在电子设计自动化、集成电路设计、系统级信号完整性和电源分配方面都有独特的技能和专业知识。为了充分利用跨校园的专业知识，该中心的许多拟议项目涉及来自多个站点的研究人员。佐治亚理工学院的研究人员在先进的集成电路封装、电源完整性、多物理场模拟、计算电磁学、神经网络、优化和系统集成方面具有特殊的专业知识。这三个站点在信号完整性分析和电子设计自动化领域都有很强的研究记录。优异的计算资源在佐治亚理工学院是可用的，包括广泛的测量和制造设备。

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 HOT DAYS @ Georgia Tech (https://www.ece.gatech.edu/outreach/hot-days) camp is a week-long summer program designed to introduce high-school students to electrical and computer engineering concepts through various half-day modules including building a computer, working with robots, using music synthesis technology, building simple digital logic circuits and constructing a speaker from common household items. Additional modules covering CAEML research areas will be developed and incorporated into the camp's schedule. CAEML undergraduate and graduate students can serve as counselors or instructors for camps. Georgia Tech's Center for Education Integrating Science, Mathematics, and Computing (CEISMC) hosts a variety of camps and programs for K-12 teachers, as well as students. CAEML faculty at Georgia Tech will participate in that effort as well as the NSF-funded Summer Teacher Experience in Packaging, Utilizing Physics (STEP-UP) Program (https://www.ece.gatech.edu/outreach/step-up-program), which is an eight-week research experience for metro Atlanta high-school physics teachers.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 Georgia Tech investigators have special expertise in advanced IC packaging, power integrity, multi-physics simulation, computational electromagnetics, neural networks, optimization and system integration. All three sites have strong research records in the fields of signal integrity analysis and electronic design automation. Excellent computational resources are available at Georgia Tech for the proposed work including extensive measurement and fabrication facilities.