Analog Neuromorphic Module Based on Carbon Nanotube Synapses
Source abstract
Abstract
We report an analog neuromorphic module composed of p-type carbon nanotube (CNT) synapses and an integrate-and-fire (I&F) circuit. The CNT synapse has a field-effect transistor structure with a random CNT network as its channel and an aluminum oxide dielectric layer implanted with indium ions as its gate. A positive voltage pulse (spike) applied on the gate attracts electrons into the defect sites of the gate dielectric layer, and the trapped electrons are gradually released after the pulse is removed. The electrons modify the hole concentration and induce a dynamic postsynaptic current in the CNT channel. Multiple input spikes induce excitatory or inhibitory postsynaptic currents via excitatory or inhibitory CNT synapses, which flow toward an I&F circuit to trigger output spikes. The dynamic transfer function between the input and output spikes of the neuromorphic module is analyzed. The module could potentially be scaled up to emulate biological neural networks and their functions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Photoreceptor and optogenetics research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Aerospace Engineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
References (22)
Show source-provided references
1. Fischetti M. · Sci. Am. Mag. · 2011 · 305 · 10410.1038/scientificamerican1111-104 ↗
2. Watson A. · Science · 1997 · 277 · 193410.1126/science.277.5334.1934 ↗
3. Koch C. · Nature · 1997 · 385 · 20710.1038/385207a0 ↗
4. Byrne, J.; Byrne, J. H.; Roberts, J. L.,From Molecules to Networks: An Introduction to Cellular and Molecular Neuroscience;Academic Press:San Diego, CA, 2009; pg470,478,544–545.
5. Indiveri G. · IEEE Trans. Neural Network · 2006 · 17 · 21110.1109/TNN.2005.860850 ↗
6. Arthur J. V. · IEEE Trans. Circuits Syst. I · 2011 · 58 · 103410.1109/TCSI.2010.2089556 ↗
7. Mahowald M. · Nature · 1991 · 354 · 51510.1038/354515a0 ↗
8. DiVincenzo D. P. · Science · 2011 · 334 · 5010.1126/science.1211284 ↗
9. Pickett M. D. · Nat. Mater. · 2013 · 12 · 11410.1038/nmat3510 ↗
10. Ramakrishnan S. · IEEE Trans. Biomed. Circuits and Systems · 2011 · 5 · 24410.1109/TBCAS.2011.2109000 ↗
11. Alibart F. · Adv. Funct. Mater. · 2010 · 20 · 33010.1002/adfm.200901335 ↗
12. Kuzum D. · Nano Lett. · 2011 · 12 · 217910.1021/nl201040y ↗
13. Hasegawa T. · Adv. Mater. · 2010 · 22 · 183110.1002/adma.200903680 ↗
14. Park H. · Nat. Nanotechnol. · 2012 · 7 · 78710.1038/nnano.2012.189 ↗
15. Erokhin V. · J. Bionanosci. · 2011 · 1 · 2410.1007/s12668-011-0004-7 ↗
16. Kim K. · Adv. Mater. · 2013 · 25 · 169310.1002/adma.201203116 ↗
17. Chen C. · Nanotechnology · 2012 · 23 · 27520210.1088/0957-4484/23/27/275202 ↗
18. Joshi J. · IEEE LiSSA Workshop · 2011 · 139
19. Nayak A. · Adv. Funct. Mater. · 2012 · 22 · 360610.1002/adfm.201200640 ↗
20. Chen Y. · IEEE Int. Joint Conf. Neural Network · 2008 · 1615
21. Indiveri G. · IEEE Int. Symp. Circuits Syst. · 2003 · 4 · IV-820
22. Debanne D. · J. Physiol. · 1996 · 491 · 16310.1113/jphysiol.1996.sp021204 ↗