The ALIGN Automated Analog Layout Engine: Progress, Learnings, and Open Issues

S. Sapatnekar
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Abstract

The ALIGN (Analog Layout, Intelligently Generated from Netlists) project [1, 2] is a joint university-industry effort to push the envelope of automated analog layout through a systematic new approach, novel algorithms, and open-source software [3]. Analog automation research has been active for several decades, but has not found widespread acceptance due to its general inability to meet the needs of the design community. Therefore, unlike digital design, which has a rich history of automation and extensive deployment of design tools, analog design is largely unautomated. ALIGN attempts to overcome several of the major issues associated with this lack of success. First, to mimic the human designer's ability to recognize sub-blocks and specify constraints, ALIGN has used machine learning (ML) based methods to assist in these tasks. Second, to overcome the limitation of past automation approaches, which are largely specific to a class of designs, ALIGN attempts to create a truly general layout engine by decomposing the layout automation process into a set of steps, with specific constraints that are specific to the family of circuits, which are divided into four classes: low-frequency components (e.g., analog-to-digital converters (ADCs), amplifiers, and filters); wireline components for high-speed links (e.g., equalizers, clock/data recovery circuits, and phase interpolators); RF/Wireless components (e.g., components of RF transmitters and receivers), and power delivery components (e.g., capacitor- and inductor-based DC-DC converters and low dropout (LDO) regulators). For each class of circuits, different sets of constraints are important, depending on their frequency, parasitic sensitivity, need for matching, etc., and ALIGN creates a unified methodological framework that can address each class. Third, in each step, ALIGN has generated new algorithms and approaches to help improve the performance of analog layout. Fourth, given that experienced analog designers desire greater visibility into the process and input into the way that design is carried out, ALIGN is built modularly, providing multiple entry points at which a designer may intervene in the process.
ALIGN自动模拟布局引擎:进展、学习和开放问题
ALIGN (Analog Layout, intelligent Generated from Netlists)项目[1,2]是一项大学和工业界的联合努力,旨在通过系统的新方法、新算法和开源软件[3]推动自动化模拟布局的发展。模拟自动化研究已经活跃了几十年,但由于它一般无法满足设计界的需求而没有得到广泛的接受。因此,与具有丰富自动化历史和广泛部署设计工具的数字设计不同,模拟设计在很大程度上是非自动化的。ALIGN试图克服与这种缺乏成功相关的几个主要问题。首先,为了模仿人类设计师识别子块和指定约束的能力,ALIGN使用了基于机器学习(ML)的方法来协助完成这些任务。其次,为了克服过去自动化方法的局限性,这些方法主要针对一类设计,ALIGN试图通过将布局自动化过程分解为一组步骤来创建真正的通用布局引擎,这些步骤具有特定于电路家族的特定约束,分为四类:低频组件(例如,模数转换器(adc),放大器和滤波器);用于高速链路的有线组件(例如,均衡器,时钟/数据恢复电路和相位插值器);RF/无线组件(例如,RF发射器和接收器的组件)和功率传输组件(例如,基于电容和电感的DC-DC转换器和低差(LDO)稳压器)。对于每一类电路,不同的约束集合是重要的,这取决于它们的频率、寄生灵敏度、匹配需求等,ALIGN创建了一个统一的方法框架,可以处理每一类。第三,在每个步骤中,ALIGN都生成了新的算法和方法来帮助提高模拟布局的性能。第四,考虑到经验丰富的模拟设计人员希望对过程有更大的可视性,并对设计的执行方式有更大的了解,ALIGN是模块化构建的,提供了多个入口点,设计人员可以在此入口点干预过程。
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