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Optimal design of a CMOS opamp via geometric programming
 IEEE Transactions on ComputerAided Design
, 2001
"... We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er ..."
Abstract

Cited by 51 (10 self)
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We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er design problem can be expressed as a special form of optimization problem called geometric programming, for which very e cient global optimization methods have been developed. As a consequence we can e ciently determine globally optimal ampli er designs, or globally optimal tradeo s among competing performance measures such aspower, openloop gain, and bandwidth. Our method therefore yields completely automated synthesis of (globally) optimal CMOS ampli ers, directly from speci cations. In this paper we apply this method to a speci c, widely used operational ampli er architecture, showing in detail how to formulate the design problem as a geometric program. We compute globally optimal tradeo curves relating performance measures such as power dissipation, unitygain bandwidth, and openloop gain. We show how the method can be used to synthesize robust designs, i.e., designs guaranteed to meet the speci cations for a
1.2 Distinct SA approaches
, 1061
"... Given a combinatorial optimization problem specified by a finite set of configurations or states S and by a cost function C defined on all the states j in S, the SA algorithm is characterized by a rule to generate randomly a new configuration with a certain probability, and by a random acceptance ru ..."
Abstract
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Given a combinatorial optimization problem specified by a finite set of configurations or states S and by a cost function C defined on all the states j in S, the SA algorithm is characterized by a rule to generate randomly a new configuration with a certain probability, and by a random acceptance rule according to which the new configuration is accepted or rejected. A parameter T controls the acceptance rule. The generic structure of the algorithm is presented in Fig.1. Theoretical investigations of the SA optimization technique have been reported in some literatures [8].