Glenn Wolfe

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Papers by Glenn Wolfe

Adaptive sampling and modeling of analog circuit performance parameters with pseudo-cubic splines

IEEE/ACM International Conference on Computer Aided Design, 2004. ICCAD-2004.

Many approaches to analog performance parameter macro modeling have been investigated by the rese... more Many approaches to analog performance parameter macro modeling have been investigated by the research community. These models are typically derived from discrete data obtained from circuit simulation using numerous input combinations of component sizes for a given circuit topology. The simulations are computationally intensive, therefore it is advantageous to reduce the number of simulations necessary to build an accurate macro

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An Error-Driven Adaptive Grid Reflnement Algorithm for Automatic Generation of Analog Circuit Performance Macromodels

Asp Dac, 2005

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An error-driven adaptive grid refinement algorithm for automatic generation of analog circuit performance macromodels

Proceedings of the 2005 conference on Asia South Pacific design automation - ASP-DAC '05, 2005

AbstractIn this paper, we present an error-driven adaptive sampling algorithm called adaptive gr... more AbstractIn this paper, we present an error-driven adaptive sampling algorithm called adaptive grid re-finement (AGR) algorithm to automatically generate performance macromodels for analog circuits. Start-ing from samples on a coarse grid, the AGR algorithm builds a global ...

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Accuracy driven performance macromodeling of feasible regions during synthesis of analog circuits

Proceedings of the 15th ACM Great Lakes symposium on VLSI, 2005

1. INTRODUCTION Circuit sizing is one of the most important steps of any analog design process. T... more 1. INTRODUCTION Circuit sizing is one of the most important steps of any analog design process. The objective of circuit sizing is to find a design (sizes and bias) satisfying the given set of performance goals. The circuit sizer has two integral components: a search ...

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Extraction and use of neural network models in automated synthesis of operational amplifiers

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2003

Fast and accurate performance estimation methods are essential to automated synthesis of analog c... more Fast and accurate performance estimation methods are essential to automated synthesis of analog circuits. Development of analog performance models is difficult due to the highly nonlinear nature of various analog performance parameters. This paper presents a neural network-based methodology for creating fast and efficient models for estimating the performance parameters of CMOS operational amplifier topologies. Effective methods for generation and use of the training data are proposed to enhance the accuracy of the neural models. The efficiency and accuracy of the resulting performance models are demonstrated via their use in a genetic algorithm-based circuit synthesis system. The genetic synthesis tool optimizes a fitness function based on user-specified performance constraints. The performance parameters of the synthesized circuits are validated by SPICE simulations and compared with those predicted by the neural network models. Experimental studies demonstrate that neural network modeling is an effective, fast, and accurate methodology for performance estimation.

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Glenn Wolfe

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