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

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。

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.