Topology Optimization of Operational Amplifier in Continuous Space via Graph Embedding

Operational amplifier is a key building block in analog circuits. However, the design process of the operational amplifier is complex and time-consuming, as there are no practical automation tools available in the industry. This paper presents a new topology optimization method for operational amplifiers. The behavioral description of the operational amplifier is described using a directed acyclic graph (DAG), which is then transformed into a low-dimensional embedding in continuous space using a variational graph autoencoder. Topology search is performed in the continuous embedding space using stochastic optimization methods, such as Bayesian Optimization. The yield search results are then transformed back to operational amplifier topologies using a graph decoder. The proposed method is also equipped with a surrogate model for performance prediction. Experimental results show that the proposed approach can achieve significant speedup over the genetic searching algorithms. The produced three-stage operational amplifiers offer competitive performance compared to manual designs.