I-Corps Teams: Machine Learning (ML)-powered Data Analyzer for Radio Frequency Integrated Circuits (RFIC) Design

I-Corps Teams：用于射频集成电路 (RFIC) 设计的机器学习 (ML) 驱动的数据分析器

• 批准号: 2016398
• 负责人: Roman Lubynsky
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016398&HistoricalAwards=false
• 关键词: Corps; Teams; Machine; Learning; ML

The broader impact/commercial potential of this I-Corps project is the development of a software platform for radio frequency integrated circuits (RFIC) design with more accurate simulation prediction. The RFIC industry, key for wireless systems, is approximately $50 B, and RFIC companies spend about $15 B on the final trial-and-error fabrication process known as "tape-out". Each tape-out takes 12 weeks and costs ~$1 M, and 3-5 may be required. Significant savings may be realized in both time and cost by reducing design iterations.This I-Corps project is based on the development of RFICs . RFIC’s parasitic effect and poor simulation accuracy requires multiple tape-outs to meet specifications. In the future 5G era, this problem gets worse as mmWave frequency parasitics are even harder to model and more tape-out rounds will be required. Using the technology under development, it may be possible to improve significantly simulation accuracy by combining traditional physics-based models and customized machine-learning (ML) training in RFIC simulation.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.

这个I-Corps项目更广泛的影响/商业潜力是开发一个用于射频集成电路（RFIC）设计的软件平台，具有更准确的模拟预测。 RFIC产业是无线系统的关键产业，大约有500 B美元，RFIC公司在最后的试错制造过程（称为“流片”）上花费大约150 B美元。 每次流片需要12周，成本约为100万美元，可能需要3-5个。通过减少设计迭代，可以显著节省时间和成本。RFIC的寄生效应和较差的仿真精度要求多个流片以满足规格。在未来的5G时代，这个问题会变得更糟，因为毫米波频率寄生效应更难建模，并且需要更多的流片轮次。使用正在开发的技术，通过在RFIC仿真中结合传统的基于物理的模型和定制的机器学习（ML）培训，可以显著提高仿真精度。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。