Analog layout synthesis requires some elements in the circuit netlist to be matched and placed symmetrically. However, the set of symmetries is very circuit-specific and a versatile algorithm, applicable to a broad variety of circuits, has been elusive. This paper presents a general methodology for the automated generation of symmetry constraints, and applies these constraints to guide automated layout synthesis. While prior approaches were restricted to identifying simple symmetries, the proposed method operates hierarchically and uses graph-based algorithms to extract multiple axes of symmetry within a circuit. An important ingredient of the algorithm is its ability to identify arrays of repeated structures. In some circuits, the repeated structures are not perfect replicas and can only be found through approximate graph matching. A fast graph neural network based methodology is developed for this purpose, based on evaluating the graph edit distance. The utility of this algorithm is demonstrated on a variety of circuits, including operational amplifiers, data converters, equalizers, and low-noise amplifiers.
|Original language||English (US)|
|Journal||IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD|
|State||Published - Nov 2 2020|
|Event||39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States|
Duration: Nov 2 2020 → Nov 5 2020
Bibliographical noteFunding Information:
This work was supported in part by DARPA IDEA program under SPAWAR contract N660011824048.
© 2020 Association on Computer Machinery.
- Analog layout automation
- machine learning