Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing

This research introduces the first-ever implementation of non-linear relaxation oscillators and coupled networks utilising energy-efficient Recessed-Source/Drain (Re-S/D) Silicon-On-Insulator (SOI) Feedback Field Effect Transistors (FBFETs). FBFETs provide a subthreshold slope (SS) that is close to...

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Main Authors: Sasi Kiran Suddarsi, Dhanaraj Kakkanattu Jagalchandran, Gopi Krishna Saramekala
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10795124/
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author Sasi Kiran Suddarsi
Dhanaraj Kakkanattu Jagalchandran
Gopi Krishna Saramekala
author_facet Sasi Kiran Suddarsi
Dhanaraj Kakkanattu Jagalchandran
Gopi Krishna Saramekala
author_sort Sasi Kiran Suddarsi
collection DOAJ
description This research introduces the first-ever implementation of non-linear relaxation oscillators and coupled networks utilising energy-efficient Recessed-Source/Drain (Re-S/D) Silicon-On-Insulator (SOI) Feedback Field Effect Transistors (FBFETs). FBFETs provide a subthreshold slope (SS) that is close to zero and a larger ON Current (<inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula>) compared to traditional MOSFETs. As a result, power consumption is reduced. The behaviour of relaxation oscillators is precisely regulated by manipulating hysteresis in the <inline-formula> <tex-math notation="LaTeX">$I_{D}$ </tex-math></inline-formula>-VS characteristics through device design parameters such as work function (WF), Re-S/D thickness (<inline-formula> <tex-math notation="LaTeX">$T_{Re-S/D}$ </tex-math></inline-formula>), and gate oxide thickness (<inline-formula> <tex-math notation="LaTeX">$T_{ox}$ </tex-math></inline-formula>). By altering the thickness of the Re-S/D layer, ranging from 0 nm to 50 nm, we achieved 61.96% decrease in energy per spike, 53.03% increase in spike frequency, and 42.7% decrease in power consumption compared with 50 nm Re-S/D thickness. Further, by varying the bias current of the Re-S/D SOI FBFET from 0.01 nA to 2.98 nA, the oscillating frequency of the relaxation oscillator ranged from 55.55 Hz to 27.5 KHz, and the power consumption ranged from 0.6166 pW to 91.895 pW. Furthermore, the behaviour of the relaxation oscillator is observed by varying the circuit parameters such as gate voltage (<inline-formula> <tex-math notation="LaTeX">$V_{GF}$ </tex-math></inline-formula>) of Re-S/D SOI FBFET, Supply Voltage (<inline-formula> <tex-math notation="LaTeX">$V_{DD}$ </tex-math></inline-formula>), and NMOS currents (<inline-formula> <tex-math notation="LaTeX">$I_{M1}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$I_{M2}$ </tex-math></inline-formula>), while keeping the device parameters constant. In addition, a coupled network was constructed with two relaxation oscillators, which exhibited synchronization dynamics in response to changes in the relaxation oscillators. Due to the controlled oscillations of a coupled network, our proposed Re-S/D SOI FBFET oscillator presents a promising avenue for efficiently implementing neuromorphic computing algorithms.
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spelling doaj-art-e128fbe0c5c14ac69660989127fa0f3c2025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219585419586510.1109/ACCESS.2024.351653710795124Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic ComputingSasi Kiran Suddarsi0https://orcid.org/0000-0002-2500-3342Dhanaraj Kakkanattu Jagalchandran1https://orcid.org/0000-0002-2127-361XGopi Krishna Saramekala2https://orcid.org/0000-0003-0608-843XDepartment of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, IndiaThis research introduces the first-ever implementation of non-linear relaxation oscillators and coupled networks utilising energy-efficient Recessed-Source/Drain (Re-S/D) Silicon-On-Insulator (SOI) Feedback Field Effect Transistors (FBFETs). FBFETs provide a subthreshold slope (SS) that is close to zero and a larger ON Current (<inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula>) compared to traditional MOSFETs. As a result, power consumption is reduced. The behaviour of relaxation oscillators is precisely regulated by manipulating hysteresis in the <inline-formula> <tex-math notation="LaTeX">$I_{D}$ </tex-math></inline-formula>-VS characteristics through device design parameters such as work function (WF), Re-S/D thickness (<inline-formula> <tex-math notation="LaTeX">$T_{Re-S/D}$ </tex-math></inline-formula>), and gate oxide thickness (<inline-formula> <tex-math notation="LaTeX">$T_{ox}$ </tex-math></inline-formula>). By altering the thickness of the Re-S/D layer, ranging from 0 nm to 50 nm, we achieved 61.96% decrease in energy per spike, 53.03% increase in spike frequency, and 42.7% decrease in power consumption compared with 50 nm Re-S/D thickness. Further, by varying the bias current of the Re-S/D SOI FBFET from 0.01 nA to 2.98 nA, the oscillating frequency of the relaxation oscillator ranged from 55.55 Hz to 27.5 KHz, and the power consumption ranged from 0.6166 pW to 91.895 pW. Furthermore, the behaviour of the relaxation oscillator is observed by varying the circuit parameters such as gate voltage (<inline-formula> <tex-math notation="LaTeX">$V_{GF}$ </tex-math></inline-formula>) of Re-S/D SOI FBFET, Supply Voltage (<inline-formula> <tex-math notation="LaTeX">$V_{DD}$ </tex-math></inline-formula>), and NMOS currents (<inline-formula> <tex-math notation="LaTeX">$I_{M1}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$I_{M2}$ </tex-math></inline-formula>), while keeping the device parameters constant. In addition, a coupled network was constructed with two relaxation oscillators, which exhibited synchronization dynamics in response to changes in the relaxation oscillators. Due to the controlled oscillations of a coupled network, our proposed Re-S/D SOI FBFET oscillator presents a promising avenue for efficiently implementing neuromorphic computing algorithms.https://ieeexplore.ieee.org/document/10795124/Silicon-on-insulator (SOI)feedback field-effect transistor (FBFET)recessed-source/drain (Re-S/D)relaxation oscillator
spellingShingle Sasi Kiran Suddarsi
Dhanaraj Kakkanattu Jagalchandran
Gopi Krishna Saramekala
Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
IEEE Access
Silicon-on-insulator (SOI)
feedback field-effect transistor (FBFET)
recessed-source/drain (Re-S/D)
relaxation oscillator
title Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
title_full Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
title_fullStr Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
title_full_unstemmed Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
title_short Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing
title_sort energy efficient recessed source drain soi feedback fet based oscillators and coupled networks for neuromorphic computing
topic Silicon-on-insulator (SOI)
feedback field-effect transistor (FBFET)
recessed-source/drain (Re-S/D)
relaxation oscillator
url https://ieeexplore.ieee.org/document/10795124/
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AT dhanarajkakkanattujagalchandran energyefficientrecessedsourcedrainsoifeedbackfetbasedoscillatorsandcouplednetworksforneuromorphiccomputing
AT gopikrishnasaramekala energyefficientrecessedsourcedrainsoifeedbackfetbasedoscillatorsandcouplednetworksforneuromorphiccomputing