Skip to main content
SpringerLink
Log in

Utilizing Forward Characteristics of Pocket Doped SiGe Tunnel FET for Designing LIF Neuron Model

  • Research
  • Published:
Silicon Aims and scope Submit manuscript

Abstract

In this paper, a single SiGe Tunnel FET is used to design a Leaky Integrate and Fire (LIF) neuron with significant improvement in area, energy and cost. SiGe Tunnel Field-Effect Transistor (FET) transfer characteristic with steep sub-threshold swing has been used to observe LIF neuronal characteristics. By employing calibrated simulation using Atlas 2D, we have verified that the TFET with LIF characteristics can effectively replicate neuron behavior without relying on external circuitry. The proposed LIF neuron, based on SiGe TFET, exhibits significantly reduced energy consumption, specifically 210 fJ per spike. This energy consumption is \(\approx \)215 times lower compared to previously reported single-device neurons in existing literature. Additionally, we have achieved an impressive recognition precision of 91.3 % for Modified National Institute of Standards and Technology (MNIST) images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Khanday MA, Khanday FA, Bashir F, Zahoor F (2023) Exploiting steep sub-threshold swing of tunnel fet for energy-efficient leaky integrate-and-fire neuron model. IEEE Trans Nanotechnol

  2. Kino H, Fukushima T, Tanaka T (2020) Generation of stdp with non-volatile tunnel-fet memory for large-scale and low-power spiking neural networks. IEEE J Electron Devices Soc 8:1266–1271

    Article  CAS  Google Scholar 

  3. Kamal N, Singh J, Lahgere A, Tiwari PK (2023) Ultra-low power reconfigurable synaptic and neuronal transistor for spiking neural network. IEEE Trans Nanotechnol

  4. Garg N, Pratap Y, Gupta M, Kabra S (2019) Impact of different localized trap charge profiles on the short channel double gate junctionless nanowire transistor based inverter and ring oscillator circuit. AEU - Int J Electron Commun 108:251–261

    Article  Google Scholar 

  5. Vanlalawmpuia K, Ghosh P (2023) Performance assessment of dielectrically modulated negative capacitance germanium source vertical tunnel fet biosensor for detection of breast cancer cell lines. AEU - Int J Electron Commun 171:154902

    Article  Google Scholar 

  6. Bousari NB, Anvarifard MK, Haji-Nasiri S (2019) Improving the electrical characteristics of nanoscale triple-gate junctionless finfet using gate oxide engineering. AEU - Int J Electron Commun 108:226–234

    Article  Google Scholar 

  7. Bhattacharya S, Tripathi SL, Kamboj VK (2023) Design of tunnel fet architectures for low power application using improved chimp optimizer algorithm. Eng Comput 39(2):1415–1458

    Article  Google Scholar 

  8. Kamal N, Singh J (2021) A highly scalable junctionless fet leaky integrate-and-fire neuron for spiking neural networks. IEEE Trans Electron Dev 68(4):1633–1638

    Article  CAS  Google Scholar 

  9. Trivedi AR, Datta S, Mukhopadhyay S (2014) Application of silicon-germanium source tunnel-fet to enable ultralow power cellular neural network-based associative memory. IEEE Trans Electron Dev 61(11):3707–3715

    Article  Google Scholar 

  10. Zahoor F, Hussin FA, Isyaku UB, Gupta S, Khanday FA, Chattopadhyay A, Abbas H (2023) Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing. Discov Nano 18(1):36

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mani E, Nimmagadda P, Basha SJ, El-Meligy MA, Mahmoud HA (2024) A finfet-based low-power, stable 8t sram cell with high yield. AEU-Int J Electron Commun 175:155102

    Article  Google Scholar 

  12. Kim Y, Kim H, Oh K, Park JH, Baek C-K (2024) Highly biomimetic spiking neuron using sige heterojunction bipolar transistors for energy-efficient neuromorphic systems. Sci Rep 14(1):8356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rozenberg M, Schneegans O, Stoliar P (2019) An ultra-compact leaky-integrate-and-fire model for building spiking neural networks. Sci Rep 9(1):11123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572

    Article  CAS  PubMed  Google Scholar 

  15. Dayan Rubin DB, Chicca E, Indiveri G (2004) Firing proprieties of an adaptive analog vlsi neuron

  16. Healy JN (2017) A leaky integrate-and-fire neuron with adjustable refractory period and spike frequency adaptation

  17. Wang R, Thakur CS, Hamilton TJ, Tapson J, van Schaik A (2015) A compact avlsi conductance-based silicon neuron. In: 2015 IEEE biomedical circuits and systems conference (BioCAS), pp 1–4. IEEE

  18. Indiveri G, Linares-Barranco B, Hamilton TJ, Schaik AV, Etienne-Cummings R, Delbruck T, Liu S-C, Dudek P, Häfliger P, Renaud S et al (2011) Neuromorphic silicon neuron circuits. Front Neurosci 5:73

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mayer F, Le Royer C, Damlencourt J-F, Romanjek K, Andrieu F, Tabone C, Previtali B, Deleonibus S (2008) Impact of soi, si 1–x ge x oi and geoi substrates on cmos compatible tunnel fet performance. In: 2008 IEEE International Electron Devices Meeting, pp 1–5. IEEE

  20. Han J-K, Geum D-M, Lee M-W, Yu J-M, Kim SK, Kim S, Choi Y-K (2020) Bioinspired photoresponsive single transistor neuron for a neuromorphic visual system. Nano Lett 20(12):8781–8788

  21. Duan Q, Jing Z, Zou X, Wang Y, Yang K, Zhang T, Wu S, Huang R, Yang Y (2020) Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat Commun 11(1):3399

  22. Chatterjee D, Kottantharayil A (2019) A cmos compatible bulk finfet-based ultra low energy leaky integrate and fire neuron for spiking neural networks. IEEE Electron Device Lett 40(8):1301–1304

    Article  CAS  Google Scholar 

  23. Dutta S, Kumar V, Shukla A, Mohapatra NR, Ganguly U (2017) Leaky integrate and fire neuron by charge-discharge dynamics in floating-body mosfet. Sci Rep 7(1):8257

    Article  PubMed  PubMed Central  Google Scholar 

  24. Das B, Schulze J, Ganguly U (2018) Ultra-low energy lif neuron using si nipin diode for spiking neural networks. IEEE Electron Device Lett 39(12):1832–1835

    Article  CAS  Google Scholar 

  25. Suresh B, Bertele M, Breyer ET, Klein P, Mulaosmanovic H, Mikolajick T, Slesazeck S, Chicca E (2019) Simulation of integrate-and-fire neuron circuits using hfo 2-based ferroelectric field effect transistors. In: 2019 26th IEEE international conference on electronics, circuits and systems (ICECS), pp 229–232. IEEE

  26. Woo S, Cho J, Lim D, Park Y-S, Cho K, Kim S (2020) Implementation and characterization of an integrate-and-fire neuron circuit using a silicon nanowire feedback field-effect transistor. IEEE Trans Electron Dev 67(7):2995–3000

    Article  CAS  Google Scholar 

  27. Khanday MA, Bashir F, Khanday FA (2022) Single germanium mosfet-based low energy and controllable leaky integrate-and-fire neuron for spiking neural networks. IEEE Trans Electron Dev 69(8):4265–4270

    Article  CAS  Google Scholar 

  28. Han J-K, Seo M, Kim W-K, Kim M-S, Kim S-Y, Kim M-S, Yun G-J, Lee G-B, Yu J-M, Choi Y-K (2019) Mimicry of excitatory and inhibitory artificial neuron with leaky integrate-and-fire function by a single mosfet. IEEE Electron Device Lett 41(2):208–211

    Article  Google Scholar 

  29. Bashir F, Zahoor F, Alzahrani AS, Khan AR (2023) A single schottky barrier mosfet based leaky integrate and fire neuron for neuromorphic computing. Express Briefs, IEEE Transactions on Circuits and Systems II

    Book  Google Scholar 

  30. Chavan T, Dutta S, Mohapatra NR, Ganguly U (2020) Band-to-band tunneling based ultra-energy-efficient silicon neuron. IEEE Trans Electron Dev 67(6):2614–2620

    Article  CAS  Google Scholar 

  31. Chander S, Sinha SK, Chaudhary R (2024) Simulation study of multi-source hetero-junction tfet-based capacitor less 1t dram for low power applications. Mater Sci Eng: B 300:117080

    Article  CAS  Google Scholar 

  32. Chander S, Sinha SK, Chaudhary R, Goswami R (2022) Effect of noise components on l-shaped and t-shaped heterojunction tunnel field effect transistors. Semicond Sci Technol 37(7):075011

    Article  CAS  Google Scholar 

  33. Chander S, Sinha SK, Chaudhary R (2022) Comprehensive review on electrical noise analysis of tfet structures. Superlattices Microstruct 161:107101

    Article  CAS  Google Scholar 

  34. Pindoo IA, Sinha SK, Chander S (2021) Performance analysis of heterojunction tunnel fet device with variable temperature. Appl Phys A 127:1–10

    Article  Google Scholar 

  35. Bashir F, Loan SA, Rafat M, Alamoud ARM, Abbasi SA (2015) A high performance gate engineered charge plasma based tunnel field effect transistor. J Comput Electron 14(2):477–485

  36. Panda S, Dash S (2022) Drain dielectric pocket engineering: its impact on the electrical performance of a hetero-structure tunnel fet. Silicon 14(15):9305–9317

    Article  CAS  Google Scholar 

  37. Mishra V, Verma YK, Gupta SK, Rathi V (2021) A sige-source doping-less double-gate tunnel fet: design and analysis based on charge plasma technique with enhanced performance. Silicon 1–8

  38. (2019) Atlas device simulation software. Silvaco Int., Santa Clara, CA, USA

  39. Wang Z, Crafton B, Gomez J, Xu R, Luo A, Krivokapic Z, Martin L, Datta S, Raychowdhury A, Khan AI (2018) Experimental demonstration of ferroelectric spiking neurons for unsupervised clustering. In: 2018 IEEE international electron devices meeting (IEDM), pp 13–3. IEEE

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia

    Faisal Bashir & Ali S. Alzahrani

  2. Interdisciplinary Research Center for Advanced Materials, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Furqan Zahoor

Authors
  1. Faisal Bashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Furqan Zahoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali S. Alzahrani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FB.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Visualization, Data Curation, Writing–Original Draft. FZ.: Validation, simulation, Resources, Data Curation, Review & Editing. ASZ: Review, Formal analysis, Investigation, Visualization.

Corresponding author

Correspondence to Faisal Bashir.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Disclosure of Potential Conflicts of Interest

There is no conflict of interest among the authors while submitting the manuscript.

Consent for Publication

Yes

Informed Consent

All authors have been informed before submitting the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bashir, F., Zahoor, F. & Alzahrani, A.S. Utilizing Forward Characteristics of Pocket Doped SiGe Tunnel FET for Designing LIF Neuron Model. Silicon (2024). https://doi.org/10.1007/s12633-024-03016-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12633-024-03016-6

Keywords

Search

Navigation