Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition

Jae-Won Nam,Youn Kyu Lee,Young-Kyun Cho

doi:10.3390/electronics11030408

Abstract

An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limited with regard to reflecting the intended operating conditions in the design parameters. Hereby, we propose what is termed a supervised learning artificial neural network (ANN) as a means by which to define an AMS regression model. This approach allows for rapid searches of complex design dimensions, including variations in performance metrics caused by process–voltage–temperature (PVT) changes. The method also reduces the considerable computation expense compared to that of simulation-program-with-integrated-circuit-emphasis (SPICE) simulations. Hence, the proposed AMS circuit design flow generates highly promising output to meet target requirements while showing an excellent ability to match the design target performance. To verify the potential and promise of our design flow, a successive approximation register analog-to-digital converter (SAR ADC) is designed with a 14 nm process design kit. In order to show the maximum single ADC performance (6-bit∼8-bit resolution and few GS/s conversion speed), we have set three different ADC performance targets. Under all SS/TT/FF corners, ±6.25% supply voltage variation, and temperature variation from −40 ∘C to 80 ∘C, the designed SAR ADC using our proposed AMS circuit optimization flow yields remarkable figure-of-merit energy efficiency (approximately 15 fJ/conversion step).

Highlights

Recent innovation in process technology has changed analog mixed-signal (AMS) circuits such that they operate more digitally by incorporating increased transistor density levels and transistor operating speeds
The achieved effective number of bits (ENOB) is lower than the target level, the ADC energy efficiency indicates that it has been optimized to perform best in any given situation
We propose a regression model-based AMS circuit design optimization flow utilizing parameterized operating conditions

Summary

Introduction

Recent innovation in process technology has changed analog mixed-signal (AMS) circuits such that they operate more digitally by incorporating increased transistor density levels and transistor operating speeds. Analog circuit design is governed by human intervention, such as topology selection and the use of a full custom layout. For this reason, optimizing the designs of analog circuits often follows well-known design circuit architectures and focuses more on optimizing each sub-module [2,3,4,5] and on finding joint optimum points during the sub-module integration step [6,7,8,9,10,11]. Designers have to carefully check three different aspects when optimizing an AMS circuit sub-module and considering integration between modules: (1) the optimization flow should reflect the flexible designer’s intention closely; (2) the execution time has to be quick as multiple iterative evaluations are required for resulting optimum output; and (3) the performance accuracy has to be guaranteed

Objectives

Methods

Results

Discussion

Conclusion