This paper presents a new approach to extract the temperature-dependent sensitivity of electron devices DC current through efficient, yet accurate, physics-based analysis. The novel technique is based on a Green’s function approach, where the response of the device to lattice (ambient) temperature variations is recovered by means of the linearization of the device equations around the nominal device working point and temperature. The same Green’s Functions are also used for other device variability analyses, e.g. random doping fluctuations or geometrical variations. A linear superposition of the device response to temperature variations with any other parameter variation, allows for a temperature-dependent device variability analysis, with virtually the same numerical burden as the fixed temperature one. In this paper we verify the technique against non-linearized (MonteCarlo) analyses. A metal gate FinFET is considered in two case studies: temperature-dependent deterministic variations of the fin doping concentration; temperature-dependent random workfunction variations due to metal granularity. The approach is extremely accurate up to 80 K above ambient temperature witha huge reduction in simulation time with respect to MonteCarlo approach.

A Novel TCAD Approach to Temperature Dependent DC FinFET Variability Analysis / Donati Guerrieri, S.; Bonani, F.; Ghione, G.. - ELETTRONICO. - (2018), pp. 230-233. (Intervento presentato al convegno 2018 European Microwave Integrated Circuits Conference (EuMIC) tenutosi a Madrid (Spain) nel 24-25 September) [10.23919/EuMIC.2018.8539887].

A Novel TCAD Approach to Temperature Dependent DC FinFET Variability Analysis

Donati Guerrieri, S.;Bonani, F.;Ghione, G.
2018

Abstract

This paper presents a new approach to extract the temperature-dependent sensitivity of electron devices DC current through efficient, yet accurate, physics-based analysis. The novel technique is based on a Green’s function approach, where the response of the device to lattice (ambient) temperature variations is recovered by means of the linearization of the device equations around the nominal device working point and temperature. The same Green’s Functions are also used for other device variability analyses, e.g. random doping fluctuations or geometrical variations. A linear superposition of the device response to temperature variations with any other parameter variation, allows for a temperature-dependent device variability analysis, with virtually the same numerical burden as the fixed temperature one. In this paper we verify the technique against non-linearized (MonteCarlo) analyses. A metal gate FinFET is considered in two case studies: temperature-dependent deterministic variations of the fin doping concentration; temperature-dependent random workfunction variations due to metal granularity. The approach is extremely accurate up to 80 K above ambient temperature witha huge reduction in simulation time with respect to MonteCarlo approach.
2018
978-2-87487-052-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2722711
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