The developing optimization algorithms provide promising solutions for speeding up analog integrated circuit sizing. However, the optimization of complicated circuits whose solution regions are narrow remains to be a challenge. With a limited number of sampling points due to the restriction of computational resources, it is difficult for traditional algorithms to achieve satisfactory results for such circuits. To solve this problem, this article proposes a rule-guided genetic algorithm (RG-GA) for analog circuit optimization. Different from the random mutation approach in the traditional genetic algorithm (GA), the RG-GA introduces a design rule-guided mutation (RGM) mechanism which helps to find the solution region in a more straightforward fashion. Instead of handing over circuit optimization tasks to pure mathematical algorithms, the proposed method takes advantages of valuable design knowledge to improve searching efficiency. This novel algorithm is implemented and deployed to design a two-stage rail-to-rail operational amplifier (OPA), an LC voltage controlled oscillator (LC-VCO) and a four-stage OPA. Experimental results show that compared to the traditional GA method, the RG-GA achieves about 1.5 and 3.3 times speed enhancement for the two-stage rail-to-rail OPA and the LC-VCO, respectively. For the four-stage OPA, the RG-GA method can find an acceptable point within the given number of iterations while the traditional GA could not.

中文翻译：

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基于规则引导遗传算法的模拟电路设计与优化系统

不断发展的优化算法为加速模拟集成电路规模化提供了有前途的解决方案。然而，解决方案区域狭窄的复杂电路的优化仍然是一个挑战。由于计算资源的限制，采样点数量有限，传统算法很难对此类电路取得满意的结果。为了解决这个问题，本文提出了一种用于模拟电路优化的规则引导遗传算法（RG-GA）。与传统遗传算法 (GA) 中的随机变异方法不同，RG-GA 引入了设计规则引导变异 (RGM) 机制，有助于以更直接的方式找到解决方案区域。而不是将电路优化任务交给纯数学算法，所提出的方法利用有价值的设计知识来提高搜索效率。实施并部署了这种新颖的算法，以设计一个两级轨到轨运算放大器 (OPA)、一个 LC 压控振荡器 (LC-VCO) 和一个四级 OPA。实验结果表明，与传统的 GA 方法相比，RG-GA 分别为两级轨到轨 OPA 和 LC-VCO 实现了约 1.5 和 3.3 倍的速度提升。对于四阶段 OPA，RG-GA 方法可以在给定的迭代次数内找到一个可接受的点，而传统的 GA 不能。一个 LC 压控振荡器 (LC-VCO) 和一个四级 OPA。实验结果表明，与传统的 GA 方法相比，RG-GA 分别为两级轨到轨 OPA 和 LC-VCO 实现了约 1.5 和 3.3 倍的速度提升。对于四阶段 OPA，RG-GA 方法可以在给定的迭代次数内找到一个可接受的点，而传统的 GA 不能。一个 LC 压控振荡器 (LC-VCO) 和一个四级 OPA。实验结果表明，与传统的 GA 方法相比，RG-GA 分别为两级轨到轨 OPA 和 LC-VCO 实现了约 1.5 和 3.3 倍的速度提升。对于四阶段 OPA，RG-GA 方法可以在给定的迭代次数内找到一个可接受的点，而传统的 GA 不能。