Delay/power modeling and optimization of FinFET circuit modules under PVT variations: Observing the trends between the 22nm and 14nm technology nodes

Aoxiang Tang, Xun Gao, Lung Yen Chen, Niraj Kumar Jha

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The semiconductor industry hasmoved to FinFETs because of their superior ability tomitigate short-channel effects relative to CMOS. Thus, good FinFET delay and power models are urgently needed to facilitate FinFET IC design at the upcoming technology nodes. Another urgent problem that needs to be addressed with continued technology scaling is how to analyze circuit performance and power consumption under process, voltage, and temperature (PVT) variations. Such variations arise due to limitations of lithography that lead to variations in the physical dimensions of the device or due to environmental variations. In this article, we propose a delay/power modeling framework for analysis of FinFET logic circuits under PVT variations. We present models for FinFET logic gates and three FinFET SRAM cells. We use GenFin, which is a genetic algorithm based statistical circuit-level delay/power optimizer, to produce the models for functional units (FUs) employed in a processor. We compare the impact of PVT variations at the 22nm and 14nm FinFET technology nodes. We evaluate cache performance for various cache capacities and temperatures as well as that of FUs. Our device simulation results show that the 3σ/μ spread for 14nm circuits is, on average, 38.5% higher in dynamic power and 21.4% higher in logarithm of leakage power relative to 22nm FinFET circuits. However, the delay spread depends on the circuit.

Original languageEnglish (US)
Article number42
JournalACM Journal on Emerging Technologies in Computing Systems
Volume12
Issue number4
DOIs
StatePublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • FinFETs
  • PVT variations
  • Parametric yield
  • SRAM
  • Statistical analysis

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