Algorithms for Topology Synthesis of Analog Circuits
In today's world, with ever increasing design complexity and constantly shrinking
device sizes, the microelectronics industry faces the need to develop an entire system on a single chip (SoC). This need gives rise to the responsibility of developing mature Computer-Aided-Design (CAD) tools to tackle such complexities. Unlike digital CAD tools, automated synthesis tools for the irreplaceable analog sections are still immature. Circuit-level analog synthesis comprises of two steps – Topology formation and Sizing of the topology. Topology selection and topology generation are two approaches to topology formation. Research in topology selection has almost been discontinued owing to heavy designer dependency. But with the advent of evolutionary techniques like Genetic Algorithm (GA) and Genetic Programming (GP), topology generation gained popularity. Topology generation is the art of generating device level circuit schematics satisfying user specifications. This thesis makes a genuine endeavor to develop topology generation tools individually for both passive analog circuits involving R, L, and C components and active circuits that involve additional MOS devices.
For passive circuits, we present a GA-based synthesis framework, where the component values for the first set of circuits are set through a deterministic computational technique. Further, the crossover technique for breeding off-springs from parent solutions obeys certain constraints to ensure the formation of structurally correct circuits. The work has been further extended with the introduction of novel selection and crossover strategies. The above techniques have been successful in synthesizing various low-pass and high-pass filter designs.
In the pursuit of developing an active circuit topology generator, we have developed a self-learning optimization algorithm involving multiple design variables. To measure the effectiveness of this technique, we applied it first to a relatively easier domain viz. MPLS computer network topology design. The tool produced optimal solutions for most of the test cases considered.
Drawing inspiration from the above work, we have extended the technique to active analog circuit synthesis. Here, we use a building block library that is adaptively formed based on the self-learning approach. It starts with basic elements like PMOS and NMOS and gradually includes bigger and functionally more meaningful blocks as the synthesis run progresses. Our next work on active synthesis incorporates the advantages of both a conventional GA as well as an augmented version of the dynamically formed building block library. Using the above techniques, we have synthesized two opamp and ring oscillator designs.
Finally, to strengthen the analog circuit topology design approach and increase its universal appeal further, we have developed a graph grammar based framework. Appropriate production rules are used to generate circuits through derivation trees. Our approach has certain advantages when compared to other tree-based techniques like GP. The framework also incorporates the concept of dynamic extraction and subsequent use of better building blocks. The work has been extended further to replace the numerical techniques used in quantifying the suitability of a block, with a fuzzy logic based inference system. The developed tool has been successful in synthesizing opamp and vco designs, producing both manual-like designs as well as novel designs.
School:University of Cincinnati
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:automated analog design topology generation optimization evolutionary algorithms filter graph grammar
Date of Publication:01/01/2008