Abstract

Low-voltage low-power (LVLP) circuit design and optimization is a hard and time-consuming task. In this study, we are interested in the application of the newly proposed meta-modelling technique to alleviate such burdens. Kriging-based surrogate models of circuits’ performances were constructed and then used within a metaheuristic-based optimization kernel in order to maximize the circuits’ sizing. The JAYA algorithm was used for this purpose. Three topologies of CMOS current conveyors (CCII) were considered to showcase the proposed approach. The achieved performances were compared to those obtained using conventional LVLP circuit sizing techniques, and we show that our approach offers interesting results.

Highlights

  • Low-power low-voltage (LVLP) circuits are of paramount importance in analog, mixed-signal and radiofrequency (AMS/RF) systems

  • We respectively present a brief overview of Particle Swarm Optimization (PSO) and the details regarding the JAYA algorithm

  • For comparison with [9] and [49], we considered that all NMOS transistors’ channels three topologies of CMOS CCIIs operating in weak inversion using the Kriging technique, using have the same widths (Wn) and the same lengths (Ln)

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Summary

Introduction

Low-power low-voltage (LVLP) circuits are of paramount importance in analog, mixed-signal and radiofrequency (AMS/RF) systems. Two main approaches have been used so far: (i) the so-called knowledge-based technique [9], which is very time consuming and mainly depends on the experience of the skilled designer; (ii) the optimization-based design, which consists of using an optimization algorithm for optimally sizing the considered circuit/system [9]. The first considers the symbolic equations of the circuit/system performances/constraints [15,16,17] This approach is known to be rapid but lacks accuracy. It consists of making the appeal to use an electric simulator as an evaluator In this way, accurate results can be obtained; the approach is time consuming due to the number of calls to the simulator. Evaluated.InInother otherwords, words, consists this new technique offers advantages of both conventional approaches: accuracy and rapid evaluation.

Section
The Optimization Kernel
The Particle Swarm Optimization Algorithm
1: A class
Similar to Application
Relative
ApplicationI3
Application 3
Conclusions

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