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In this paper, we present an empirical method for efficient analog design retargeting by combining design knowledge reuse and circuit synthesis. The method first decomposes the source system into circuit blocks and extracts the performance parameter specifications of each circuit block. Then, it scales each circuit block and defines a design space in the target technology. Subsequently, each circuit block is synthesized. Our assumption is that if the synthesized circuit blocks retain the same set of performance specifications, then the overall system after retargeting would have the same performance specification as the source system. We experiment the method on a fourth order continuous-time Delta-Sigma modulator.

With increasing process parameter variations in nanometre regime, circuits and systems encounter significant performance variations and therefore statistical analysis has become increasingly important. For complex analog and mixed-signal circuits and systems, efficient yet accurate statistical analysis has been a challenge mainly due to significant simulation and modelling time. In the past years, there have been various approaches proposed for statistical analysis of analog and mixed-signal circuits. A recent work is reported to address statistical analysis for continuous-time Delta-Sigma modulators. In this article, we generalise that method and present a hierarchical method for efficient statistical analysis of complex analog and mixed-signal circuits while maintaining reasonable accuracy. At circuit level, we use the response surface modelling method to extract quadratic models of circuit-level performance parameters in terms of process parameters. Then at system level, we use behavioural models and apply the Monte-Carlo method for statistical evaluation of system performance parameters. We illustrate and validate the method on a continuous-time Delta-Sigma modulator and an analog filter.

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