{"id":14,"date":"2018-01-31T08:55:59","date_gmt":"2018-01-31T08:55:59","guid":{"rendered":"http:\/\/www.rescue.deib.polimi.it\/?page_id=14"},"modified":"2020-10-14T15:07:48","modified_gmt":"2020-10-14T15:07:48","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.rescue.deib.polimi.it\/?page_id=14","title":{"rendered":"Publications"},"content":{"rendered":"

\"\"<\/a>Journal articles<\/span><\/h4>\n

[40] S. Bianchi, G. Pedretti, I, Mu\u00f1oz-Martin, A. Calderoni, N. Ramaswamy, S. Ambrogio and D. Ielmini, “A Compact Model for Stochastic Spike-Timing-Dependent-Plasticity (STDP) Based on Resistive Switching Memory (RRAM) Synapses, in IEEE Transaction on Electron Devices, vol. 67, no. 7 ,pp. 2800-2806, July 2020, DOI:10.1109\/TED.2020.2992386<\/em><\/i><\/span><\/span><\/p>\n

[39] Z. Sun, G. Pedretti, P. Mannocci, E. Ambrosi, A. Bricalli and D. Ielmini, “Time Complexity of In-Memory Solution of Linear Systems”, in IEEE Transaction on Electron Devices, vol. 67, no. 7 ,pp. 2945-2951, July 2020, DOI:10.1109\/TED.2020.2992435<\/em><\/i><\/span><\/span><\/p>\n

[38] G. Pedretti, P. Mannocci, S. Hashemkhani, V. Milo, O. Melnic, E. Chicca and D. Ielmini, “A Spiking Recurrent Neural Network with Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems”, in IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 6, no. 1, pp. 89-97, June 2020, DOI: 10.1109\/JXCDC.2020.2992691<\/em><\/i><\/span><\/span><\/p>\n

[37] S. Bianchi, I. Mu\u00f1oz-Martin and D. Ielmini, “Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning “, Frontiers in Neuroscience 14, pp. 379 (2020), DOI: 10.3389\/fnins.2020.00379<\/em><\/i><\/span><\/span><\/p>\n

[36] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli and D. Ielmini, “In-memory Pagerank Accelerator With a Cross-Point Array of Resistive Memories”, in IEEE Transaction on Electron Devices, vol. 67, no. 4,pp. 1466-1470, April 2020, DOI:10.1109\/TED.2020.2966908<\/em><\/i><\/span><\/span><\/p>\n

[35] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli and D. Ielmini, “In-memory Eigenvector Computation in Time O(1)”, Advanced Intelligent Systems, 2, 2000048, DOI:10.1002\/aisy.202000042<\/em><\/i><\/span><\/span><\/p>\n

[34] Z. Sun, G. Pedretti, A. Bricalli and D. Ielmini, \u201cOne-step regression and classification with cross-point resistive memory arrays\u201d Science Advances 6 (5) eaay2378 (2020). DOI: 10.1126\/sciadv.aay2378<\/em><\/i><\/span><\/span><\/p>\n

[33] Z. Wang, T. Zeng, Y. Ren, Y. Lin, H. Xu, X. Zhao, Y. Liu and D. Ielmini, \u201cRealization of a generalized Bienenstock\u2013Cooper\u2013Munro learning rule through triplet-STDP in memristors for spatiotemporal patterns,\u201d Nature Communications 11:1510 (2020). DOI: 10.1038\/s41467-020-15158-3<\/em><\/i><\/span><\/span><\/p>\n

[32]\u00a0R. Carboni and D. Ielmini, “Stochastic Memory Devices for Security and Computing”, Advanced Electronic Materials (2019)<\/em><\/i><\/span><\/span><\/p>\n

[31] R. Carboni, E. Vernocchi, M. Siddik, J. Harms, A. Lyle, G. Sandhu and D. Ielmini,\u00a0“A Physics-Based Compact Model of Stochastic Switching in Spin-Transfer Torque Magnetic Memory,” in\u00a0IEEE Transactions on Electron Devices, vol. 66, no. 10, pp. 4176-4182 (2019) DOI:10.1109\/TED.2019.2933315<\/em><\/i><\/span><\/span><\/p>\n

[30] W. Wang, M. Laudato, E. Ambrosi, A. Bricalli, E. Covi, Y.-H. Lin and D. Ielmini, “Volatile Resistive Switching Memory Based on Ag Ion Drift\/Diffusion\u2014Part II: Compact Modeling,” in\u00a0IEEE Transactions on Electron Devices, vol. 66, no. 9, pp. 3802-3808 (2019) DOI: 10.1109\/TED.2019.2928888<\/em><\/i><\/span><\/span><\/p>\n

[29] W. Wang, M. Laudato, E. Ambrosi, A. Bricalli, E. Covi, Y.-H. Lin and D. Ielmini, “Volatile Resistive Switching Memory Based on Ag Ion Drift\/Diffusion Part I: Numerical Modeling,” IEEE Transactions on Electron Devices, vol. 66, no. 9, pp. 3795-3801 (2019) DOI: 10.1109\/TED.2019.2928890<\/em><\/i><\/span><\/span><\/p>\n

[28] V. Milo, C. Zambelli, P. Olivo, E. P\u00e9rez, M. K. Mahadevaiah, O. G. Ossorio, Ch. Wenger and D. Ielmini, “Multilevel HfO2-based RRAM devices for low-power neuromorphic networks” APL Materials, Vol. 7, Issue 8 (2019) DOI:10.1063\/1.5108650<\/em><\/i><\/span><\/span><\/p>\n

[27] I. Mu\u00f1oz-Mart\u00edn, S. Bianchi, G. Pedretti, O. Melnic, S. Ambrogio and D. Ielmini, “Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks,” IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 5, no. 1, pp. 58-66, (2019). DOI:10.1109\/JXCDC.2019.2911135<\/em><\/i><\/span><\/span><\/p>\n

[26]\u00a0M. Lanza, et. al., \u201cRecommended methods to study resistive switching devices<\/em>,\u201d Advanced Electronic Materials 5, 1 (2019)<\/em><\/i><\/span><\/span><\/p>\n

[25] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli, W. Wang and D. Ielmini, \u201c<\/em>Solving matrix equations in one step with cross-point resistive arrays<\/em>,\u201d Proceedings of the National Academy of Science PNAS\u00a0116\u00a0<\/span>(10)\u00a0<\/span>4123-4128\u00a0<\/span>(2019).DOI:10.1073\/pnas.1815682116<\/em><\/i><\/span><\/span><\/p>\n

[24]\u00a0W. Wang, M. Wang, E. Ambrosi, A. Bricalli, M. Laudato, Z. Sun, X. Chen and D. Ielmini, \u201c<\/em>Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices<\/em>,\u201d Nature Communications 10, 81 (2019).DOI: 0.1038\/s41467-018-07979-0<\/em><\/i><\/span><\/span><\/p>\n

[23] W. Wang, G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli and D. Ielmini, \u201c<\/em>Learning of spatio-temporal patterns in a spiking neural network with resistive switching synapses<\/em>,\u201d Science Advances 4, 9(2018).\u00a0DOI: 10.1126\/sciadv.aat4752<\/em><\/i><\/span><\/span><\/p>\n

[22]\u00a0<\/em>Z. Sun, E. Ambrosi, A. Bricalli and D. Ielmini, \u201c<\/em>Logic Computing with stateful neural networks of resistive switches<\/em>,\u201d Advanced Materials 30, 38 (2018). DOI:\u00a010.1002\/adma.201802554<\/em><\/i><\/span><\/span><\/p>\n

[21] M. Wang, W. Wang, W. R. Leow, C. Wan, G. Chen, Y. Zeng, J. Yu, Y. Liu, P. Cai, D. Ielmini and X. Chen, \u201cEnhancing the Matrix Addressing of Flexible Sensory Arrays by a Highly Nonlinear threshold switch\u201d Advanced Materials 30, 33 (2018). DOI: 10.1002\/adma.201802516<\/em><\/i><\/span><\/span><\/p>\n

[20] <\/em>A. <\/em>Mehonic<\/em>\u00a0<\/em>,<\/em>A. L. Shluger\u00a0<\/em>,<\/em>D. Gao<\/em>, <\/em>I. Valov<\/em>, <\/em>E. Miranda<\/em>, <\/em>D. Ielmini<\/em>\u00a0<\/em>, <\/em>A. Bricalli<\/em>, <\/em>E. Ambrosi<\/em>, <\/em>C. Li<\/em>, <\/em>J. J. Yang, Q. Xia\u00a0and <\/em>A. J. Kenyon<\/em> \u201cSilicon Oxide (SiOx): A Promising Material for Resistance Switching?\u201d, Advanced Materials 30, 43 (2018). DOI:\u00a010.1002\/adma.201801187<\/em><\/i><\/span><\/span><\/p>\n

\u00a0<\/em>[19] D. Ielmini and H.-S.P. Wong \u201c<\/em>In-memory computing with resistive switching devices<\/em>\u201d, Nature Electronics 1 333-343 (2018) DOI:\u00a00.1038\/s41928-018-0092-2<\/em><\/i><\/span><\/span><\/p>\n

\u00a0[18] Y. Ren, V. Milo, Z. Wang, H. Xu, D. Ielmini, X. Zhao, Y. Liu, \u201cAnalytical modeling of organic\u2212inorganic CH3NH3PbI3 perovskite resistive switching and its application for neuromorphic recognition,\u201d Advanced Theoretical Simulations 1,4 (2018) DOI:\u00a010.1002\/adts.201700035<\/em><\/i><\/span><\/span><\/p>\n

\u00a0<\/em>[17] R. Carboni, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu and D. Ielmini, \u201c<\/em>Random number generation by differential read of stochastic switching in spin-transfer torque memory<\/em>,\u201d IEEE Electron Device Letters 39, 7 (2018) DOI:\u00a010.1109\/LED.2018.2833543<\/em><\/i><\/span><\/span><\/p>\n

[16] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, \u201cA 4-transistors\/one-resistor hybrid synapse based on resistive switching memory (RRAM) capable of spike-rate dependent plasticity (SRDP),\u201d IEEE Trans. VLSI 26, 12 (2018). DOI:\u00a010.1109\/TVLSI.2018.2818978<\/em><\/i><\/span><\/span><\/p>\n

[15] D. Ielmini, \u201cBrain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks,\u201d Microelectron. Eng. 190, 44-53 (2018). DOI:10.1016\/j.mee.2018.01.009<\/em><\/i><\/span><\/span><\/p>\n

\u00a0[14] R. Carboni, S. Ambrogio, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu and D. Ielmini, \u201cModeling of breakdown-limited endurance in spin-transfer torque (STT) magnetic memory under pulsed cycling regime,\u201d IEEE Trans. Electron Devices 65, 2470-2478 (2018). DOI:\u00a010.1109\/TED.2018.2822343<\/em><\/i><\/span><\/span><\/p>\n

[13] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, \u201cResistive switching device technology based on silicon oxide for improved on-off ratio \u2013 Part I: Memory devices,\u201d IEEE Trans. Electron Devices 65, 115-121 (2018). DOI: 10.1109\/TED.2017.2777986.<\/em><\/i><\/span><\/span><\/p>\n

[12] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, \u201cResistive switching device technology based on silicon oxide for improved on-off ratio \u2013 Part II: Select devices,\u201d IEEE Trans. Electron Devices 65, 122-128 (2018). DOI: 10.1109\/TED.2017.2776085.<\/em><\/i><\/span><\/span><\/p>\n

[11] D. Ielmini and V. Milo, \u201cPhysics-based modeling approaches of resistive switching devices for memory and in-memory computing applications,\u201d J. Computation. Electron. 16(4), 1121-1143 (2017). DOI: 10.1007\/s10825-017-1101-9<\/em><\/i><\/span><\/span><\/p>\n

[10] G. Pedretti, V. Milo, S. Ambrogio, R. Carboni, S. Bianchi, A. Calderoni, N. Ramaswamy, A. S. Spinelli, D. Ielmini, \u201cStochastic learning in neuromorphic hardware via spike timing dependent plasticity with RRAM synapses,\u201d IEEE J. Emerging Topics in Circuits and Systems (JETCAS) (2018). DOI: 10.1109\/JETCAS.2017.2773124<\/em><\/i><\/span><\/span><\/p>\n

[9] G. Pedretti, V. Milo, S. Ambrogio, R. Carboni, S. Bianchi, A. Calderoni, N. Ramaswamy, A. S. Spinelli, D. Ielmini, \u201cMemristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity,\u201d Sci. Rep. 7:5288 (2017). DOI: 10.1038\/s41598-017-05480-0<\/em><\/i><\/span><\/span><\/p>\n

[8] S. Balatti, S. Ambrogio, R. Carboni, V. Milo, Z.-Q. Wang, A. Calderoni, N. Ramaswamy, and D. Ielmini, \u201cPhysical unbiased generation of random numbers with coupled resistive switching devices,\u201d IEEE Trans. Electron Devices 63, 2029-2035 (2016). DOI: 10.1109\/TED.2016.2537792.<\/em><\/i><\/span><\/span><\/p>\n

\u00a0<\/em>[7] S. Ambrogio, S. Balatti, V. Milo, R. Carboni, Z. Wang, A. Calderoni, N. Ramaswamy, and D. Ielmini, \u201cNeuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM,\u201d IEEE Trans. Electron Devices 63, 1508-1515 (2016). DOI: 10.1109\/TED.2016.2526647.<\/em><\/i><\/span><\/span><\/p>\n

[6] D. Ielmini, \u201cResistive Switching Memories based on Metal Oxides: Mechanisms, Reliability and Scaling,\u201d Semicond. Sci. Technol. 31, 063002 (2016). DOI: 10.1088\/0268-1242\/31\/6\/063002<\/em><\/i><\/span><\/span><\/p>\n

[5] D. Ielmini, \u201cPhysical Models of Program and Read Fluctuations in Metal Oxide Resistive RAM,\u201d ECS Trans. 75, 4, 19-26 (2016). DOI: 10.1149\/07505.0019ecst<\/em><\/i><\/span><\/span><\/p>\n

[4] S. Ambrogio, N. Ciocchini, M. Laudato, V. Milo, A. Pirovano, P. Fantini and D. Ielmini, \u201cUnsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses,\u201d Front.\u00a0<\/em>Neurosci. 10:56 (2016). DOI: 10.3389\/fnins.2016.00056<\/em><\/i><\/span><\/span><\/p>\n

[3] N. Ciocchini, M. Laudato, M. Boniardi, E. Varesi, P. Fantini, A. L. Lacaita, and D. Ielmini, \u201cBipolar switching in chalcogenide phase change memory,\u201d Sci.\u00a0<\/em>Rep. 6, 29162 (2016). DOI: 10.1038\/srep29162<\/em><\/i><\/span><\/span><\/p>\n

[2] S. Ambrogio V. Milo, Z. Wang, S. Balatti, and D. Ielmini, \u201cAnalytical modeling of current overshoot in oxide-based resistive switching memory (RRAM),\u201d IEEE Electron Device Lett. 37, 1268-1271 (2016). DOI: 10.1109\/LED.2016.2600574<\/em><\/i><\/span><\/span><\/p>\n

[1] Z. Wang, S. Ambrogio, S. Balatti, S. Sills, A. Calderoni, N. Ramaswamy, D. Ielmini, \u201cPost-cycling degradation in metal-oxide bipolar resistive switching memory (RRAM),\u201d IEEE Trans. Electron Devices 63, 4279-4287 (2016). DOI: 10.1109\/TED.2016.2604370<\/em><\/i><\/span><\/span><\/p>\n

\u00a0<\/i><\/span><\/span><\/p>\n

\u00a0\"\"<\/a>Conference proceedings<\/span><\/h4>\n

[17] G. Pedretti, V. Milo, S. Hashemkhani, V. Milo, O. Melnic, E. Chicca and D. Ielmini, “A Spiking Recurrent Neural Network with Phase-Change Memory Synapses for decision making”, 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, 2020, pp. 1-5, DOI:10.1109\/ISCAS45731.2020.9180513<\/em><\/i><\/span><\/span><\/p>\n

[16] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli and D. Ielmini, “In-memory Pagerank using a Crosspoint Array of Resistive Switching Memory (RRAM) devices”, 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova, Italy (2020), pp. 26-30, DOI: 10.1109\/AICAS48895.2020.9073964<\/em><\/i><\/span><\/span><\/p>\n

[15] Z. Sun, G. Pedretti, and D. Ielmini, “Fast Solution of Linear Systems with Analog Resistive Switching Memory (RRAM)”, 2019 IEEE International Conference on Rebooting Computing (ICRC), San Mateo, CA, USA, (2019), DOI:10.1109\/ICRC.2019.8914709<\/em><\/i><\/span><\/span><\/p>\n

[14] V. Milo, C. Zambelli, P. Olivo, E. P\u00e9rez, M. K. Mahadevaiah, O. G. Ossorio, Ch. Wenger and D. Ielmini, “Low-energy inference machine with multilevel HfO2 RRAM arrays,”\u00a0ESSDERC 2019 – 49th European Solid-State Device Research Conference (ESSDERC), Cracow, Poland pp. 174-177 (2019)
\nDOI: 10.1109\/ESSDERC.2019.8901818<\/em><\/i><\/span><\/span><\/p>\n

[13] S. Bianchi, I. Mu\u00f1oz-Martin, G. Pedretti, O. Melnic, S. Ambrogio and D. Ielmini, “Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapses,” 2019 Symposium on VLSI Technology, Kyoto, Japan, pp. T172-T173. (2019)DOI: 10.23919\/VLSIT.2019.8776559<\/em><\/i><\/span><\/span><\/p>\n

[12] E. Ambrosi, A. Bricalli, M. Laudato and D. Ielmini, Impact of oxide and electrode materials on the switching characteristics of oxide ReRAM devices<\/em>\u201c <\/em>2018 RSC Faraday Discussions (2018). DOI:\u00a010.1039\/C8FD00097B<\/em><\/i><\/span><\/span><\/p>\n

[11] W. Wang, G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli and D. Ielmini, \u201cComputing of Temporal Information among Spikes using ReRAM Synapse<\/em>\u201c 2018 RSC Faraday Discussions (2018). DOI:\u00a010.1039\/C8FD00097B<\/em><\/i><\/span><\/span><\/p>\n

[10] V. Milo, E. Chicca, and D. Ielmini, \u201c<\/em>Brain-inspired recurrent neural network with plastic RRAM synapses<\/em>\u201c <\/em>2018 IEEE International Symposium Circuits and Systems (ISCAS), Firenze, Italy (2018).
\nDOI:\u00a010.1109\/ISCAS.2018.8351523<\/em><\/i><\/span><\/span><\/p>\n

[9] V. Milo, G. Pedretti, M. Laudato, A. Bricalli, E. Ambrosi, S. Bianchi, E. Chicca, and D. Ielmini, \u201cResistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks,\u201d 2018 IEEE International Symposium Circuits and Systems (ISCAS), Firenze, Italy (2018). DOI:\u00a0\u00a0<\/strong>10.1109\/ISCAS.2018.8351824<\/em><\/i><\/span><\/span><\/p>\n

[8] G. Pedretti, S. Bianchi, V. Milo, A. Calderoni, N. Ramaswamy, and D. Ielmini, \u201cModeling-based design of brain-inspired spiking neural networks with RRAM learning synapses,\u201d IEDM Tech. Dig. 653-656 (2017). DOI:\u00a010.1109\/IEDM.2017.8268467<\/em><\/i><\/span><\/span><\/p>\n

[7] V. Milo, D. Ielmini, and E. Chicca, \u201cAttractor networks and associative memories with STDP learning in RRAM synapses,\u201d IEDM Tech. Dig. 263-266 (2017). DOI:\u00a010.1109\/IEDM.2017.8268369<\/em><\/i><\/span><\/span><\/p>\n

[6] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, \u201cSiO<\/em>x<\/sub><\/em>-based resistive switching memory (RRAM) for crossbar storage\/select elements with high on\/off ratio,\u201d IEDM Tech. Dig. 87 (2016).\u00a0 DOI:\u00a010.1109\/IEDM.2016.7838344<\/em><\/i><\/span><\/span><\/p>\n

[5] R. Carboni, S. Ambrogio, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu, and D. Ielmini, \u201cUnderstanding cycling endurance in perpendicular spin-transfer torque (p-STT) magnetic memory,\u201d IEDM Tech. Dig. 572 (2016). DOI:\u00a010.1109\/IEDM.2016.7838468<\/em><\/i><\/span><\/span><\/p>\n

\u00a0[4] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, \u201cDemonstration of hybrid CMOS\/RRAM neural networks with spike time\/rate-dependent plasticity,\u201d IEDM Tech. Dig. 440 (2016). DOI:\u00a010.1109\/IEDM.2016.7838435<\/em><\/i><\/span><\/span><\/p>\n

[3] S. Ambrogio, S. Balatti, V. Milo, R. Carboni, Z. Wang, A. Calderoni, N. Ramaswamy, D. Ielmini, \u201cNovel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning,\u201d Symp. VLSI Tech. Dig. (2016). DOI: 10.1109\/VLSIT.2016.7573432<\/em><\/i><\/span><\/span><\/p>\n

[2] D. Ielmini, S. Ambrogio, V. Milo, S. Balatti, and Z.-Q. Wang, \u201cNeuromorphic computing with hybrid memristive\/CMOS synapses for real-time learning,\u201d 2016 IEEE International Symposium Circuits and Systems (ISCAS), Montreal, Canada, May 22-25, 2016. DOI: 10.1109\/ISCAS.2016.7527508<\/em><\/i><\/span><\/span><\/p>\n

[1] N. Ciocchini, M. Laudato, A. L. Lacaita, D. Ielmini, M. Boniardi, E. Varesi, P. Fantini, \u201cBipolar-switching operated phase change memory (PCM) for improved high-temperature reliability,\u201d Proc. ESSDERC (2016). DOI:<\/em>\u00a010.1109\/ESSDERC.2016.7<\/em>.<\/i><\/span><\/span><\/p>\n

\u00a0<\/i><\/span><\/span><\/p>\n

\"\"<\/a>Patent applications<\/span><\/h4>\n

[2] D. Ielmini, Z. Sun, and G. Pedretti, \u201cCircuito di risoluzione di problemi matematici comprendente elementi resistivi\u201d, patent application n. 812017000124370, Oct. 31, 2017.<\/i><\/span><\/p>\n

[1] D. Ielmini, S. Balatti, and S. Ambrogio, \u2018Random number generation from multiple memory states,\u2019 WO 2017\/153875 A1.<\/i><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Journal articles [40] S. Bianchi, G. Pedretti, I, Mu\u00f1oz-Martin, A. Calderoni, N. Ramaswamy, S. Ambrogio and D. Ielmini, “A Compact Model for Stochastic Spike-Timing-Dependent-Plasticity (STDP) Based on Resistive Switching Memory (RRAM) Synapses, in IEEE Transaction on Electron Devices, vol. 67, no. 7 ,pp. 2800-2806, July 2020, DOI:10.1109\/TED.2020.2992386 [39] Z. Sun, G. Pedretti, P. Mannocci, E. … Continue reading Publications<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-14","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/14","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14"}],"version-history":[{"count":15,"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/14\/revisions"}],"predecessor-version":[{"id":426,"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/14\/revisions\/426"}],"wp:attachment":[{"href":"https:\/\/www.rescue.deib.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}