AI-Enabled Design of mm-Wave & Sub-THz Frequency Chips with Reinforcement Learning and Inverse Methods

Abstract

In the Radio Frequency Integrated Circuit design industry today, the design process is both complex and labor-intensive, demanding deep domain expertise and significant time investment. A designer first starts with target performance specifications. After establishing a general architecture, the designer then chooses topologies for each gain stage, iteratively adjusting parameters until the active portion of a circuit is produced. Then, the designer must match the impedances of each stage, utilizing predefined parameter sweeps and heuristic techniques to adjust and optimize their passive component designs. This process is extremely tedious, taking anywhere from a few weeks to several months depending on the complexity of a design. To address this issue and expedite the design process, this thesis tackles the development of both passive and active components by utilizing machine learning methods. Specifically, we utilize inverse design methods for passive structures and reinforcement learning for active components to synthesize power amplifier circuits from end-to-end algorithmically. Moreover, we consolidate these tools into graphical user interfaces to provide a ready-to-use product for RFIC design engineers anywhere.