Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing

Md Hasan Raza Ansari; Hanrui Li; Nazek El-Atab

doi:10.1109/ICEE56203.2022.10118253

Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing

Md Hasan Raza Ansari, Hanrui Li, Nazek El-Atab^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work highlights the application of a junctionless (JL) transistor with charge trapping mechanism as an artificial synaptic device for neuromorphic computing. In this work, synapse behaviors ((short-term potentiation (STP), long-term potentiation (LTP), and depression (LTD))) have been validated and analyzed by storing the positive charges (holes) in the floating body and charge trapping nitride layer. JL device can be operated at a lower drain voltage (VDS = 0.8 V) to trigger the band-to-band tunneling and impact ionization mechanisms. The device achieves a higher and linear conductance value, and the non-linearity value for LTP is 0.1, which is beneficial for neural networks. Estimated conductance values from the device are utilized to estimate the pattern recognition and achieve an accuracy of 85 % with the CNN algorithm and CIFAR-10 datasets.

Original language	English (US)
Title of host publication	2022 IEEE International Conference on Emerging Electronics, ICEE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665491853
DOIs	https://doi.org/10.1109/ICEE56203.2022.10118253
State	Published - 2022
Event	2022 IEEE International Conference on Emerging Electronics, ICEE 2022 - Bangalore, India Duration: Dec 11 2022 → Dec 14 2022

Publication series

Name	2022 IEEE International Conference on Emerging Electronics, ICEE 2022

Conference

Conference	2022 IEEE International Conference on Emerging Electronics, ICEE 2022
Country/Territory	India
City	Bangalore
Period	12/11/22 → 12/14/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Charge Trapping Memory
Junctionless
LTD
LTP
Neural Network

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Surfaces, Coatings and Films
Instrumentation

Access to Document

10.1109/ICEE56203.2022.10118253

Cite this

Ansari, M. H. R., Li, H., & El-Atab, N. (2022). Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing. In 2022 IEEE International Conference on Emerging Electronics, ICEE 2022 (2022 IEEE International Conference on Emerging Electronics, ICEE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEE56203.2022.10118253

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title = "Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing",

abstract = "This work highlights the application of a junctionless (JL) transistor with charge trapping mechanism as an artificial synaptic device for neuromorphic computing. In this work, synapse behaviors ((short-term potentiation (STP), long-term potentiation (LTP), and depression (LTD))) have been validated and analyzed by storing the positive charges (holes) in the floating body and charge trapping nitride layer. JL device can be operated at a lower drain voltage (VDS = 0.8 V) to trigger the band-to-band tunneling and impact ionization mechanisms. The device achieves a higher and linear conductance value, and the non-linearity value for LTP is 0.1, which is beneficial for neural networks. Estimated conductance values from the device are utilized to estimate the pattern recognition and achieve an accuracy of 85 % with the CNN algorithm and CIFAR-10 datasets.",

keywords = "Charge Trapping Memory, Junctionless, LTD, LTP, Neural Network",

author = "Ansari, {Md Hasan Raza} and Hanrui Li and Nazek El-Atab",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Emerging Electronics, ICEE 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/ICEE56203.2022.10118253",

language = "English (US)",

series = "2022 IEEE International Conference on Emerging Electronics, ICEE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Ansari, MHR , Li, H & El-Atab, N 2022, Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing. in 2022 IEEE International Conference on Emerging Electronics, ICEE 2022. 2022 IEEE International Conference on Emerging Electronics, ICEE 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Emerging Electronics, ICEE 2022, Bangalore, India, 12/11/22. https://doi.org/10.1109/ICEE56203.2022.10118253

Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing. / Ansari, Md Hasan Raza ; Li, Hanrui ; El-Atab, Nazek.
2022 IEEE International Conference on Emerging Electronics, ICEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Conference on Emerging Electronics, ICEE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This work highlights the application of a junctionless (JL) transistor with charge trapping mechanism as an artificial synaptic device for neuromorphic computing. In this work, synapse behaviors ((short-term potentiation (STP), long-term potentiation (LTP), and depression (LTD))) have been validated and analyzed by storing the positive charges (holes) in the floating body and charge trapping nitride layer. JL device can be operated at a lower drain voltage (VDS = 0.8 V) to trigger the band-to-band tunneling and impact ionization mechanisms. The device achieves a higher and linear conductance value, and the non-linearity value for LTP is 0.1, which is beneficial for neural networks. Estimated conductance values from the device are utilized to estimate the pattern recognition and achieve an accuracy of 85 % with the CNN algorithm and CIFAR-10 datasets.

AB - This work highlights the application of a junctionless (JL) transistor with charge trapping mechanism as an artificial synaptic device for neuromorphic computing. In this work, synapse behaviors ((short-term potentiation (STP), long-term potentiation (LTP), and depression (LTD))) have been validated and analyzed by storing the positive charges (holes) in the floating body and charge trapping nitride layer. JL device can be operated at a lower drain voltage (VDS = 0.8 V) to trigger the band-to-band tunneling and impact ionization mechanisms. The device achieves a higher and linear conductance value, and the non-linearity value for LTP is 0.1, which is beneficial for neural networks. Estimated conductance values from the device are utilized to estimate the pattern recognition and achieve an accuracy of 85 % with the CNN algorithm and CIFAR-10 datasets.

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Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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