GPCAD: a tool for CMOS op-amp synthesis
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
MAELSTROM: efficient simulation-based synthesis for custom analog cells
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Design of pipeline analog-to-digital converters via geometric programming
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
OPERA: optimization with ellipsoidal uncertainty for robust analog IC design
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Simultaneous multi-topology multi-objective sizing across thousands of analog circuit topologies
Proceedings of the 44th annual Design Automation Conference
Theory and Applications of Robust Optimization
SIAM Review
Robust Analog/RF Circuit Design With Projection-Based Performance Modeling
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Equation-based optimization using geometric programming (GP) for automated synthesis of analog circuits has recently gained broader adoption. A major outstanding challenge is the inaccuracy resulting from fitting the complex behavior of scaled transistors to posynomial functions. Fitting over a large region can be grossly inaccurate, and in fact, poor posynomial fit can lead to failure to find a true feasible solution. On the other hand, fitting over smaller regions and then selecting the best region, incurs exponential complexity. In this paper, we advance a novel optimization strategy that circumvents these dueling problems in the following manner: by explicitly handling the error of the model in the course of optimization, we find a potentially suboptimal, but feasible solution. This solution subsequently guides a range-refinement process of our transistor models, allowing us to reduce the range of operating conditions and dimensions, and hence obtain far more accurate GP models. The key contribution is in using the available oracle (SPICE simulations) to identify solutions that are feasible with respect to the accurate behavior rather than the fitted model. The key innovation is the explicit link between the fitting error statistics and the rate of the error uncertainty set increase, which we use in a robust optimization formulation to find feasible solutions. We demonstrate the effectiveness of our algorithm on a two benchmarks: a two-stage CMOS operational amplifier and a voltage controlled oscillator designed in TSMC 0.18μm CMOS technology. Our algorithm is able to identify superior solution points producing uniformly better power and area values under gain constraint with improvements of up to 50% in power and 10% in area for the amplifier design. We also demonstrate that when utilizing the models with the same level of modeling error, our method yields solutions that meet the constraints while the violations for the standard method were as high as 45% and larger than 15% for several constraints.