AI-enabled RF-to-THz IC Design Space Discovery and Inverse Design Flow

The design of radio-frequency reaching up to higher millimeter-wave (mmWave) and Terahertz (THz) frequencies is considered a black art. It requires years of human enterprise, deep human insights, co-design across multiple domains (such as circuits, layout and electromagnetics), and time/resource intensive iterative design simulations. The methodology is highly bottom - up using single functional elements, and subsequently building larger and more complex structures with it. Also unlike digital processors, there has not been a successful path towards automated design flow in RFIC yet. In this paper, we demonstrate a deep-learning based approach towards high frequency design that allows not only an algorithmic design approach, but also opens up a new design space that we have not explored before. We show inverse design of complex arbitrary shaped electromagnetic (EM) structures with designer S-parameters synthesized in minutes, which can be combined with active circuitry to realize circuits with unprecedented performance. We show this with a deep learning based mmWave PA with a record 30-94+ GHz P<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sat,3dB</inf> bandwidth, supporting concurrent multiband transmission for the first time at mmWave. We also demonstrate rapid synthesis of complex multi-port structures, allowing a path towards power combining with unique capabilities. We comment on the path towards full end-to-end RFIC design, synthesis and layout. AI can transform RFIC design when utilized effectively.