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

I-Corps项目更广泛的影响/商业潜力是开发射频集成电路（RFIC）设计的软件平台，具有更准确的仿真预测。作为无线系统的关键，RFIC产业的规模约为500亿美元，而RFIC公司在被称为“带出”的最终试错制造过程上花费了约150亿美元。每次胶带取出需要12周，成本约为100万美元，可能需要3-5次。通过减少设计迭代，可以显著节省时间和成本。这个I-Corps项目是基于rfic的发展。RFIC的寄生效应和较差的仿真精度需要多次带出才能满足规格。在未来的5G时代，这个问题会变得更加严重，因为毫米波频率寄生更难建模，需要更多的带出轮。利用正在开发的技术，通过将传统的基于物理的模型与RFIC仿真中的定制机器学习（ML）训练相结合，可以显著提高仿真精度。该奖项反映了美国国家科学基金会的法定使命，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。

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.