Spiking neurons from tunable Gaussian heterojunction transistors.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 03 2020
26 03 2020
Historique:
received:
20
10
2019
accepted:
03
03
2020
entrez:
29
3
2020
pubmed:
29
3
2020
medline:
29
3
2020
Statut:
epublish
Résumé
Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing.
Identifiants
pubmed: 32218433
doi: 10.1038/s41467-020-15378-7
pii: 10.1038/s41467-020-15378-7
pmc: PMC7099079
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1565Références
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 1–23 (2011).
Schaik, A. v., Jin, C., McEwan, A. & Hamilton, T. J. in 2010 IEEE Int. Symp. Circ. S. 4253–4256 (IEEE, 2010).
Wijekoon, J. H. B. & Dudek, P. in 2009 IEEE Biomed. Circ. S. 193-196 (IEEE, 2009).
Culurciello, E., Etienne-Cummings, R. & Boahen, K. A. A biomorphic digital image sensor. IEEE J. Solid-St. Circ. 38, 281–294 (2003).
doi: 10.1109/JSSC.2002.807412
Indiveri, G. In 2003 IEEE Int. Symp. Circ. S. 820-823 (IEEE, 2003).
Folowosele, F. et al. In 2009 IEEE Int. Symp. Circ. S. 2149-2152 (IEEE, 2009).
Asai, T., Kanazawa, Y. & Amemiya, Y. A subthreshold MOS neuron circuit based on the Volterra system. IEEE Trans. Neural Netw. 14, 1308–1312 (2003).
doi: 10.1109/TNN.2003.816357
Wijekoon, J. H. B. & Dudek, P. In 2008 IEEE Int. Symp. Circ. S. 1784-1787 (IEEE, 2008).
Akopyan, F. et al. TrueNorth: Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34, 1537–1557 (2015).
doi: 10.1109/TCAD.2015.2474396
Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).
doi: 10.1109/JPROC.2014.2304638
Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).
doi: 10.1021/nl904092h
Sangwan, V. K. et al. Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018).
doi: 10.1038/nature25747
Wang, L. et al. Artificial synapses based on multiterminal memtransistors for neuromorphic application. Adv. Funct. Mater. 29, 1901106 (2019).
doi: 10.1002/adfm.201901106
Sharad, M., Augustine, C., Panagopoulos, G. & Roy, K. Spin-based neuron model with domain-wall magnets as synapse. IEEE Trans. Nanotechnol. 11, 843–853 (2012).
doi: 10.1109/TNANO.2012.2202125
Shi, J., Ha, S. D., Zhou, Y., Schoofs, F. & Ramanathan, S. A correlated nickelate synaptic transistor. Nat. Commun. 4, 2676 (2013).
doi: 10.1038/ncomms3676
Jiang, J. et al. 2D MoS
doi: 10.1002/smll.201700933
Zhu, L. Q. et al. Multi-gate synergic modulation in laterally coupled synaptic transistors. Appl. Phys. Lett. 107, 143502 (2015).
doi: 10.1063/1.4932568
Sebastian, A., Pannone, A., Subbulakshmi Radhakrishnan, S. & Das, S. Gaussian synapses for probabilistic neural networks. Nat. Commun. 10, 4199 (2019).
doi: 10.1038/s41467-019-12035-6
pubmed: 6744503
pmcid: 6744503
Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114 (2012).
doi: 10.1038/nmat3510
Gao, L., Chen, P.-Y. & Yu, S. NbO
doi: 10.1063/1.4991917
Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018).
doi: 10.1038/s41928-018-0023-2
Thakur, C. S. et al. Large-scale neuromorphic spiking array processors: a quest to mimic the brain. Front. Neurosci. 12, 891 (2018).
doi: 10.3389/fnins.2018.00891
pubmed: 6287454
pmcid: 6287454
Jaiswal, A., Roy, S., Srinivasan, G. & Roy, K. Proposal for a leaky-integrate-fire spiking neuron based on magnetoelectric switching of ferromagnets. IEEE Trans. Electron Devices 64, 1818–1824 (2017).
doi: 10.1109/TED.2017.2671353
Wang, Z. et al. In 2018 IEEE Int. Electron Devices Meet. 300-303 (IEEE, 2018).
Izhikevich, E. M. Dynamical Systems in Neuroscience. (MIT Press, Cambridge, 2007).
Meng, X. J. et al. Temperature dependence of ferroelectric and dielectric properties of PbZr
doi: 10.1063/1.1522833
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).
doi: 10.1038/s41586-019-1157-8
pubmed: 6522354
pmcid: 6522354
Jariwala, D. et al. Gate-tunable carbon nanotube-MoS
doi: 10.1073/pnas.1317226110
Jariwala, D. et al. Hybrid, gate-tunable, van der Waals p-n heterojunctions from pentacene and MoS
doi: 10.1021/acs.nanolett.5b04141
Nourbakhsh, A., Zubair, A., Dresselhaus, M. S. & Palacios, T. Transport properties of a MoS
doi: 10.1021/acs.nanolett.5b04791
Li, Y. et al. Anti-ambipolar field-effect transistors based on few-layer 2D transition metal dichalcogenides. ACS Appl. Mater. Inter. 8, 15574–15581 (2016).
doi: 10.1021/acsami.6b02513
Hong, T. et al. Anisotropic photocurrent response at black phosphorus–MoS
doi: 10.1039/C5NR03400K
Lee, C.-H. et al. Atomically thin p–n junctions with van der Waals heterointerfaces. Nat. Nanotechnol. 9, 676 (2014).
doi: 10.1038/nnano.2014.150
Wu, E. et al. Photoinduced doping to enable tunable and high-performance anti-ambipolar MoTe
doi: 10.1021/acsnano.9b00201
He, X., Chow, W., Liu, F., Tay, B. & Liu, Z. MoS
doi: 10.1002/smll.201602558
Yoo, H., On, S., Lee, S. B., Cho, K. & Kim, J.-J. Negative transconductance heterojunction organic transistors and their application to full-swing ternary circuits. Adv. Mater. 31, 1808265 (2019).
doi: 10.1002/adma.201808265
Sangwan, V. K. et al. Self-aligned van der Waals heterojunction diodes and transistors. Nano Lett. 18, 1421–1427 (2018).
doi: 10.1021/acs.nanolett.7b05177
Li, D., Wang, B., Chen, M., Zhou, J. & Zhang, Z. Gate-controlled BP–WSe
doi: 10.1002/smll.201603726
Jariwala, D. et al. Large-area, low-voltage, antiambipolar heterojunctions from solution-processed semiconductors. Nano Lett. 15, 416–421 (2015).
doi: 10.1021/nl5037484
Jariwala, D., Marks, T. J. & Hersam, M. C. Mixed-dimensional van der Waals heterostructures. Nat. Mater. 16, 170–181 (2017).
doi: 10.1038/nmat4703
Yang, Z. et al. High-performance photoinduced memory with ultrafast charge transfer based on MoS
Amsterdam, S. H. et al. Electronic coupling in metallophthalocyanine–transition metal dichalcogenide mixed-dimensional heterojunctions. ACS Nano 13, 4183–4190 (2019).
doi: 10.1021/acsnano.8b09166
Geier, M. L. et al. Solution-processed carbon nanotube thin-film complementary static random access memory. Nat. Nanotechnol. 10, 944 (2015).
doi: 10.1038/nnano.2015.197
Gaviria Rojas, W. A. et al. Solution-processed carbon nanotube true random number generator. Nano Lett. 17, 4976–4981 (2017).
doi: 10.1021/acs.nanolett.7b02118
Phan, T. L. et al. Efficient gate modulation in a screening-engineered MoS
doi: 10.1021/acsami.9b05335
Hodgkin, A. L. & Huxley, A. F. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116, 449–472 (1952).
doi: 10.1113/jphysiol.1952.sp004717
pubmed: 1392213
pmcid: 1392213
Kang, K. & Shibata, T. An on-chip-trainable Gaussian-kernel analog support vector machine. IEEE Trans. Circuits-I 57, 1513–1524 (2010).
doi: 10.1109/TCSI.2009.2034234
Reynolds, D. A., Quatieri, T. F. & Dunn, R. B. Speaker verification using adapted Gaussian mixture models. Digit. Signal Process. 10, 19–41 (2000).
doi: 10.1006/dspr.1999.0361
Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017).
doi: 10.1080/01621459.2017.1285773
Crespo, J. L., Duro, R. J. & Pena, F. L. Gaussian synapse ANNs in multi- and hyperspectral image data analysis. IEEE Trans. Instrum. Meas. 52, 724–732 (2003).
doi: 10.1109/TIM.2003.814693