Modeling and Extraction of Causal Information in Analog Circuits

Automated analog circuit synthesis algorithms are conceptually different from manual design procedures mainly because of their limited learning capabilities, including understanding the causal justifications for certain design decisions. However, the causal information is critical for activities like constructing new circuit topologies, incrementally updating existing designs, circuit verification, and improving design optimization. This paper presents a model to represent the causal elements embedded in an analog circuit design and the methodology and algorithms to automatically extract the causal information. Experiments discuss the methodology for two state-of-the-art OpAmp/OTA circuits.