Efficient Circuit Structure Analysis for Automatic Behavioral Model Generation in Mixed-Signal System Simulation

Ling-Yen Song, Yu-Kang Lou, Ching-Ho Lin, Chien-Nan Liu, Juinn-Dar Huang, Jing-Yang Jou, Meng-Jung Lee, Yu-Lan Lo
2021 Electronics  
For mixed-signal systems, identifying the analog and digital circuit blocks in the transistor-level netlist has many benefits for system analysis and verification. However, existing approaches still have difficulty handling large mixed-signal designs with millions of transistors, especially when multiple analog structure patterns are included. In this paper, we propose an efficient structure recognition methodology to support analyzing highly complex designs with various circuit structures and
more » ... ifferent devices. In order to tackle the complexity of real cases, a hierarchical partition-based analysis methodology and an encoding-based fast screening technique are proposed in this work. To correctly ascertain the boundary of analog and digital structures, we propose an enhanced direct current connection (DCC) partition method and combine it with the analog structure analysis flow. The non-transistor devices, such as resistors and capacitors, are also included in our recognition flow to improve the recognition capability and accuracy. As demonstrated with two industrial cases, the behavioral models generated from the structure recognition results do help to improve the efficiency of the AMS system verification.
doi:10.3390/electronics10091088 doaj:2f52f1dc48bb421e911afcaf74739a97 fatcat:ww242ly5pbatxowub5ggzf7lbm