Helmut Graeb
ABSTRACT
Analog circuit design is characterized by a plethora of implicit design and technology aspects available to the experienced designer. In order to create useful computer-aided design methods, this implicit knowledge has to be captured in a systematic and hierarchical way. A key approach to this goal is to "learn" the knowledge from the netlist of an analog circuit. This requires a library of structural and functional blocks for analog circuits together with their individual constraints and performance equations, graph homomorphism techniques to recognize blocks that can have different structural implementations and I/O pins, as well as synthesis methods that exploit the learned knowledge. In this contribution, we will present how to make use of the functional and structural hierarchy of operational amplifiers. As an application, we explore the capabilities of machine learning in the context of structural and functional properties and show that the results can be substantially improved by pre-processing data with traditional methods for functional block analysis. This claim is validated on a data set of roughly 100,000 readily sized and simulated operational amplifiers.
- Inga Abel and Helmut Graeb. 2022. FUBOCO: Structure Synthesis of Basic Op-Amps by FUnctional BlOck COmposition. ACM Trans. Des. Autom. Electron. Syst., Vol. 27, 6, Article 63 (jun 2022), 27 pages. https://doi.org/10.1145/3522738Google Scholar
Digital Library
- Inga Abel, Maximilian Neuner, and Helmut Graeb. 2022a. A Functional Block Decomposition Method for Automatic Op-Amp Design. Integr. VLSI J., Vol. 85, C (jul 2022), 108--120. https://doi.org/10.1016/j.vlsi.2022.04.006Google ScholarDigital Library
- Inga Abel, Maximilian Neuner, and Helmut E. Graeb. 2022b. A Hierarchical Performance Equation Library for Basic Op-Amp Design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 41, 7 (2022), 1976--1989. https://doi.org/10.1109/TCAD.2021.3101691Google ScholarCross Ref
- Helmut Graeb. 2022. Analog Synthesis - The Deterministic Way. In Proceedings of the 2022 International Symposium on Physical Design (Virtual Event, Canada) (ISPD '22). Association for Computing Machinery, New York, NY, USA, 167--174. https://doi.org/10.1145/3505170.3511043Google ScholarDigital Library
- H. Graeb, S. Zizala, J. Eckmueller, and K. Antreich. 2001. The sizing rules method for analog integrated circuit design. In IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281). 343--349. https://doi.org/10.1109/ICCAD.2001.968645Google ScholarCross Ref
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- Kishor Kunal, Tonmoy Dhar, Meghna Madhusudan, Jitesh Poojary, Arvind K. Sharma, Wenbin Xu, Steven M. Burns, Jiang Hu, Ramesh Harjani, and Sachin S. Sapatnekar. 2023. GNN-based Hierarchical Annotation for Analog Circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2023), 1-1. https://doi.org/10.1109/TCAD.2023.3236269Google ScholarDigital Library
- K.R. Lakshmikumar, R.A. Hadaway, and M.A. Copeland. 1986. Characterisation and modeling of mismatch in MOS transistors for precision analog design. IEEE Journal of Solid-State Circuits, Vol. 21, 6 (1986), 1057--1066. https://doi.org/10.1109/JSSC.1986.1052648Google ScholarCross Ref
- Tobias Massier, Helmut Graeb, and Ulf Schlichtmann. 2008. The Sizing Rules Method for CMOS and Bipolar Analog Integrated Circuit Synthesis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 27, 12 (2008), 2209--2222. https://doi.org/10.1109/TCAD.2008.2006143Google ScholarDigital Library
- Rituj Patel, Husni Habal, and Konda Reddy Venkata. 2021. Machine Learning based Structure Recognition in Analog Schematics for Constraints Generation, Vol. 6. https://dvcon-proceedings.org/wp-content/uploads/machine-learning-based-structure-recognition-in-analog-schematics-for-constraints-generation-paper.pdfGoogle Scholar
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- Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical Graph Representation Learning with Differentiable Pooling. (2018). https://doi.org/10.48550/ARXIV.1806.08804v4Google ScholarCross Ref
Index Terms
- Learning from the Implicit Functional Hierarchy in an Analog Netlist
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