Trans-Net: Knowledge-Transferring Analog Circuit Optimizer with a Netlist-Based Circuit Representation

Finding an optimal point in the design space of analog circuits requires a substantial time-consuming effort even for skillful circuit designers. There have been extensive studies on automated sizing of transistors in analog circuits based on machine learning (ML) algorithms. However, the previous approaches suffer from lack of expandability and necessitate an inevitable retraining process of the given model to apply for optimization of different circuits. The graph-based representation of a circuit with reinforcement learning (RL) achieved a knowledge transfer when optimizing the same circuit with different process technologies. However, it can be hardly applied to different circuit topologies due to the failure of generalizing the training of RL agent. This paper introduces Trans-Net, an analog circuit optimizer that is capable of supporting the knowledge transfer across different circuits as well as different process technologies with a circuit representation that defines the circuit topology by one-to-one mapping from SPICE netlist. The proposed analog circuit optimizer successfully supports multiple circuits within a single ML model, showcasing its effectiveness on five different circuit topologies across three different process technologies.