Mimicry of Excitatory and Inhibitory Artificial Neuron With Leaky Integrate-and-Fire Function by a Single MOSFET
Abstract
For the first time, a leaky integrate-and-fire (LIF) neuron with both excitatory and inhibitory characteristics is demonstrated using a single MOSFET. No additional circuits such as comparator, reset circuit, or current-to-voltage converter as well as a membrane capacitor are needed, and thus the LIF neuron is realized with a footprint of 6 F2. Because of its gate terminal, neuron firing is selectively inhibited, which can improve the energy efficiency of the neuromorphic system by inducing sparse activity. Furthermore, the spiking property of the neuron can be controlled by the gate, which can provide additional room to enhance the classification accuracy.
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The operation of the cryptoristor relies on the mechanism of STL, which occurs when sufficient holes are provided via impact ionization under a high lateral electric field, temporarily stored in an FB, and suddenly escaped from the FB.
“The operation of the cryptoristor relies on the mechanism of STL, which occurs when a sufficient number of holes are provided via impact ionization under a high lateral electric field, temporarily stored in an FB, and suddenly escaped from the FB (33–36).”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [33]
Cites
Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004764 · Cited by 23,191 · see citing works
Receptive fields of single neurones in the cat's striate cortex
Hubel, D. H., & Wiesel, T. N. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006308 · Cited by 4,653 · see citing works
Which Model to Use for Cortical Spiking Neurons?
Izhikevich, E. M. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.832719 · Cited by 2,555 · see citing works
Neuromorphic Silicon Neuron Circuits
Indiveri, G., Linares-Barranco, B., Hamilton, T. J., André, V. S., Etienne‐Cummings, R., Delbrück, T., Liu, S., Dudek, P., Hafliger, P. D., Renaud, S., et al. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00073 · Cited by 1,836 · see citing works
Stochastic phase-change neurons
Tůma, T., Pantazi, A., Gallo, M. L., Sebastian, A., & Eleftheriou, E. S. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,107 · see citing works
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S., & Pfeiffer, M. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280696 · Cited by 1,060 · see citing works
Indiveri, G., Chicca, E., & Douglas, R. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.860850 · Cited by 1,009 · see citing works
Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., & Indiveri, G. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00141 · Cited by 678 · see citing works
A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm
Merolla, P. A., Arthur, J. V., Akopyan, F., Imam, N., Manohar, R., & Modha, D. S. (2011) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2011.6055294 · Cited by 432 · see citing works
Serb, A., Bill, J., Khiat, A., Berdan, R., Legenstein, R. A., & Prodromakis, T. (2016) · Nature Communications · doi:10.1038/ncomms12611 · Cited by 337 · see citing works
Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET
Dutta, S., Kumar, V., Shukla, A., Mohapatra, N. R., & Ganguly, U. (2017) · Scientific Reports · doi:10.1038/s41598-017-07418-y · Cited by 226 · see citing works
Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons
Sengupta, A., Panda, P., Wijesinghe, P., Kim, Y., & Roy, K. (2016) · Scientific Reports · doi:10.1038/srep30039 · Cited by 194 · see citing works
Highly Compact Artificial Memristive Neuron with Low Energy Consumption
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., Wang, Y., Zhang, Z., Ji, X., Li, G., et al. (2018) · Small · doi:10.1002/smll.201802188 · Cited by 117 · see citing works
Leaky Integrate-and-Fire Biristor Neuron
Han, J.-W., & Meyyappan, M. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2856092 · Cited by 99 · see citing works
Self-citationExperimental Demonstration of Ferroelectric Spiking Neurons for Unsupervised Clustering
Wang, Z., Crafton, B., Gomez, J. T., et al. (2018) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2018.8614586 · Cited by 95 · see citing works
Kim, H., Hwang, S., Park, J., Yun, S., Lee, J., & Park, B. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2809661 · Cited by 89 · see citing works
Hwang, S., Kim, H., Park, J., Kwon, M.-W., Baek, M.-H., Lee, J.-J., & Park, B. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2853635 · Cited by 52 · see citing works
Sparse Activity and Sparse Connectivity in Supervised LearningPreprint
Thom, M., & Palm, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08367 · Cited by 42 · see citing works
A Bandgap-Engineered Silicon-Germanium Biristor for Low-Voltage Operation
Moon, J.-B., Moon, D., & Choi, Y. (2014) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2013.2288272 · Cited by 36 · see citing works
Self-citationMultistate Resistive Switching Memory for Synaptic Memory Applications
Hota, M. K., Hedhili, M. N., Wehbe, N., McLachlan, M. A., & Alshareef, H. N. (2016) · Advanced Materials Interfaces · doi:10.1002/admi.201600192 · Cited by 29 · see citing works
Trigger and Self-Latch Mechanisms of n-p-n Bistable Resistor
Han, J.-W., & Meyyappan, M. (2014) · IEEE Electron Device Letters · doi:10.1109/led.2013.2297277 · Cited by 24 · see citing works
Self-citationSparse activity and sparse connectivity in supervised learning
ThomMarkus, & PalmGünther (2013) · Journal of Machine Learning Research · doi:10.5555/2567709.2502615 · Cited by 23 · see citing works
An ultra-compact and low power neuron based on SOI platform
Ostwal, V., Meshram, R. H., Rajendran, B., & Ganguly, U. (2015) · International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA) · doi:10.1109/vlsi-tsa.2015.7117569 · Cited by 20 · see citing works
A Silicon Biristor With Reduced Operating Voltage: Proposal and Analysis
Kumar, M. J., Maheedhar, M., & Varma, P. P. (2014) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2014.2384518 · Cited by 20 · see citing works
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Han, J.-K., Seo, M., Yu, J.-M., Suh, Y.-J., & Choi, Y.-K. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.3001953 · Cited by 39 · see citing works
Self-citationCryptographic transistor for true random number generator with low power consumption
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“When a constant current is applied to the drain as an input, a spike-shaped voltage is iteratively produced as an output from the same drain electrode through a repeated charging and discharging process reported in previous works ( 33 – 35 , 39 , 40 ).”
Self-citation
- Authors
- 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.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- ORCID
- Venue
- IEEE Electron Device Letters
- Published
- 2020-02
- Volume
- 41
- Issue
- 2
- Pages
- 208-211
- ISSN
- 0741-3106 (ISSN-L)
- 1558-0563
- OpenAlex
- W2996276847
- Cited by
- 127
- References
- 24
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 26 · 2023: 20 · 2022: 20 · 2021: 8 · 2020: 6
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Electron Device Letters (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- National Research Foundation of Korea (2017H1A2A1042274, 2018R1A2A3075302, 2019M3F3A1A03079603)
- IC Design Education Center
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- leaky integrate-and-fire neurons, neuromorphic circuits, energy efficiency, artificial neurons
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