Mobile QR Code QR CODE

REFERENCES

1 
Kuzum D., 2011, Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing, Nano letters, Vol. 12, No. 5, pp. 2179-2186DOI
2 
Breitwisch M. J., Cheek R. W., Lam C. H., Modha D. S., Rajendran B., :US Patent 20100299297.DOI
3 
Kim S., 2015, NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning, 2015 IEEE international electron devices meeting (IEDM), pp. 17.1.1-17.1.4.DOI
4 
Wang Z., 2015, A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems, Frontiers in neuroscience, Vol. 8, pp. 438DOI
5 
Pantazi A., 2016, All-memristive neuromorphic computing with level-tuned neurons, Nano-technology, Vol. 27, No. 35, pp. 355205Google Search
6 
Stefano Ambrogio , 2016, Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses, Frontiers in neuroscience, Vol. 10, No. 56DOI
7 
Tomas Tuma , 2016, Detecting Correlations Using Phase-Change Neurons and Synapses, IEEE Electron Device Letters, Vol. 37, No. 9, pp. 1238-1241DOI
8 
Nandakumar S. R., 2017, Supervised Learning in Spiking Neural Networks with MLC PCM Synapses, 2017 75th Annual Device Research Conference (DRC), pp. 1-2DOI
9 
Barbera Selina La, 2018, Narrow Heater Bottom Electrode‐Based Phase Change Memory as a Bidirectional Artificial Synapse, Advanced Electronic Materials, Vol. 4, No. 9, pp. 1800223DOI
10 
Irem Boybat, 2018, Neuromorphic computing with multi-memristive Synapses, Nature communi-cations, Vol. 9, No. 1, pp. 2514DOI
11 
Suri M., 2011, Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction, 2011 International Electron Devices Meeting, pp. 4.4.1-4.4.4DOI
12 
Suri M., 2012, Physical aspects of low power synapses based on phase change memory devices, Journal of Applied Physics, Vol. 112, No. 5, pp. 054904DOI
13 
Bichler O., 2012, Visual Pattern Extraction Using Energy-Efficient, IEEE Transactions on Electron Devices, Vol. 59, No. 8, pp. 2206-2214DOI
14 
Suri M., 2011, Phase change memory for synaptic plasticity application in neuromorphic systems, The 2011 International Joint Conference on Neural Networks. IEEE, pp. 619-624DOI
15 
Querlioz D., 2011, Simulation of a memristor-based spiking neural network immune to device variations, The 2011 International Joint Con-ference on Neural Networks. IEEE, pp. 1775-1781DOI
16 
Querlioz D., 2012, Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches, 2012 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH). IEEE, pp. 203-210DOI
17 
Querlioz D., 2013, Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices, IEEE Transactions on Nano-technology, Vol. 12, No. 3, pp. 288-295DOI
18 
Irem Boybat, 2017, Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks, 2017 13th Conference on Ph. D. Research in Microelectronics and Electronics, pp. 13-16DOI
19 
Pritish Narayanan, 2017, Neuromorphic Tech-nologies for Next-Generation Cognitive Com-puting, Electron Devices Technology and Manufacturing Conference (EDTM)DOI
20 
G.W.Burr , 2015, Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element, IEEE Transactions on Electron Devices, Vol. 62, No. 11, pp. 3498-3507DOI