模拟电路设计优化的元启发式研究综述

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abdelaziz Lberni , Malika Alami Marktani , Abdelaziz Ahaitouf , Ali Ahaitouf
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引用次数: 0

摘要

随着CMOS技术的不断缩小,超大规模集成电路(VLSI)的设计复杂性正在迅速增加。特别是模拟电路设计,由于元件尺寸对性能的关键影响,仍然非常耗时。虽然元启发式在模拟电路自动化中的应用可以追溯到20世纪80年代,但模拟设计任务的日益复杂和减少设计周期的需要引发了使用元启发式方法来解决这些挑战的新兴趣。在本文中,我们提供了一个全面的和最新的回顾现有的研究应用的元启发式模拟电路设计自动化,包括电路综合,尺寸和布局综合,同时评估其在满足设计目标的有效性。本文从元启发式的角度进行了深入探讨,并指出了未来探索的重点研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metaheuristics for analog circuit design optimization: A survey
As CMOS technology continues to scale down, the design complexity of very large-scale integrated circuits (VLSI) is rapidly increasing. Analog circuit design, in particular, remains time-consuming due to the critical impact of component dimensions on performance. Although the application of metaheuristics in analog circuit automation dates back to the 1980s, the growing complexity of analog design tasks and the need to reduce design cycles has sparked renewed interest in using metaheuristic approaches to address these challenges. In this paper, we provide a comprehensive and up-to-date review of existing studies on the application of metaheuristics in analog circuit design automation, including circuit synthesis, sizing, and layout synthesis, while assessing their effectiveness in meeting design objectives. The paper provides an in-depth discussion from the metaheuristics perspective and highlights key research directions for future exploration.
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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