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

Research output: Contribution to journalArticlepeer-review


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 different 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.

Original languageEnglish
Article number1088
JournalElectronics (Switzerland)
Issue number9
StatePublished - 1 May 2021


  • Behavioral model
  • Mixed-signal simulation
  • Model generator


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