(cache)Efficient Estimation of PSIJ via Jitter Transfer Function and Knowledge-based Neural Networks | IEEE Conference Publication | IEEE Xplore

Efficient Estimation of PSIJ via Jitter Transfer Function and Knowledge-based Neural Networks


Abstract:

In this paper, an efficient method for analysis of power supply induced jitter (PSIJ) is presented. In the proposed approach, the noise spectrum for an arbitrary noise is...Show More

Abstract:

In this paper, an efficient method for analysis of power supply induced jitter (PSIJ) is presented. In the proposed approach, the noise spectrum for an arbitrary noise is generated via Fourier series and the knowledge-based neural network (KBNN) is generated to accurately predict the response of PSIJ transfer function (PSIJTF) using the training data extracted from two types of models, analytical closed-form expressions as well as computationally expensive circuit simulator. Employing KBNN based transfer function model with the noise spectrum gives reasonably accurate estimation of PSIJ for multiple input noises. A case study with 32nm CMOS technology is presented to demonstrate the validity of the proposed model compared to a circuit simulator.
Date of Conference: 07-10 May 2023
Date Added to IEEE Xplore: 12 June 2023
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Conference Location: Aveiro, Portugal

I. Introduction

In the resent years, rapid innovations in the semiconductor industry and scaling of CMOS transistors have helped to maintain relatively constant power per unit area while allowing more transistors to be packed in the same chip-area. These modern high-speed and high-density systems operating at higher frequencies require quality power supply modules in order to power billions of devices contained in them. Since the noise margins have significantly reduced due to the reduction in the supply levels, the quality and performance of electronic systems can be greatly affected. Hence, designing for ensuring power integrity becomes increasingly challenging in modern electrical circuits.

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