Abstract:The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a two-stage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuc… Show more
“…The implementation of meta-heuristics depends on the nature of the problem. In general, design methods for analog integrated circuits use heuristics based on natural processes such as Genetic Algorithms [42], Simulated Annealing, Artificial Neural Networks [43,35], Particle Swarm [44,45] and other bio-inspired approaches [29]. These methods have a high probability of finding a solution close to the global optimal after several iterations [1].…”
Analog integrated circuits are present in most electronic systems. They must be designed carefully in order to achieve the required performance specifications. Although almost half of the design effort in a chip is spent in analog modules, there are not yet a consolidated industry of analog design automation tools capable to fully synthesize analog circuits in a fast and generic way. This is due the fact that automatic or semi-automatic design tools have to deal with high design complexity, which includes several specifications and design variables. A lot of research effort have been done in this field in the past years, proposing methods for automating parts of the design flow, from topology selection to devices sizing and layout generation. This paper presents a review of the state-of-the art in analog design automation methods, focusing on techniques and algorithms used to size robust circuits while efficiently exploring the design space.
“…The implementation of meta-heuristics depends on the nature of the problem. In general, design methods for analog integrated circuits use heuristics based on natural processes such as Genetic Algorithms [42], Simulated Annealing, Artificial Neural Networks [43,35], Particle Swarm [44,45] and other bio-inspired approaches [29]. These methods have a high probability of finding a solution close to the global optimal after several iterations [1].…”
Analog integrated circuits are present in most electronic systems. They must be designed carefully in order to achieve the required performance specifications. Although almost half of the design effort in a chip is spent in analog modules, there are not yet a consolidated industry of analog design automation tools capable to fully synthesize analog circuits in a fast and generic way. This is due the fact that automatic or semi-automatic design tools have to deal with high design complexity, which includes several specifications and design variables. A lot of research effort have been done in this field in the past years, proposing methods for automating parts of the design flow, from topology selection to devices sizing and layout generation. This paper presents a review of the state-of-the art in analog design automation methods, focusing on techniques and algorithms used to size robust circuits while efficiently exploring the design space.
“…This constraint demands a reliable method to attain the transistor sizing by balancing all the desired parameters of the design. The use of metaheuristic optimization algorithms to determine the transistor sizes can be a solution to this problem (e.g., [ 239 , 240 ]). The minimization of the parameters can be accomplished by selecting one or more objective functions and considering the rest of the parameters as a constraint.…”
Section: Applications In Microelectronicsmentioning
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
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