Causal Information Prediction for Analog Circuit Design Using Variable Selection Methods Based on Machine Learning
- Resource Type
- Conference
- Authors
- Abuelnasr, Ahmed; Amer, Mostafa; Ragab, Ahmed; Gosselin, Benoit; Savaria, Yvon
- Source
- 2021 IEEE International Symposium on Circuits and Systems (ISCAS). :1-5 May, 2021
- Subject
- Components, Circuits, Devices and Systems
Machine learning algorithms
Filtering
Input variables
Machine learning
Analog circuits
Circuit synthesis
Wrapping
Analog Circuit Design
Causal Discovery
Accelerated Design
Machine Learning
- Language
- ISSN
- 2158-1525
This paper proposes a methodology based on machine learning to find apparent causal relations between performance targets and design variables in analog circuits. Diversified filtering and wrapping variable selection algorithms are utilized to construct a causal graph that identifies the major circuit design parameters that can be used to optimize the performance of analog circuits. Based on the constructed causal graph, a sequence of design procedures can be extracted and followed to optimize the performance of a design. The proposed methodology is validated using a two-stage op-amp. The obtained causal graph agrees with analytical design equations published in the literature for the selected two-stage op-amp. The results also show that the proposed methodology can accelerate the circuit design process and effectively help designers understand the reasoning behind different design decisions.