Publications

Journal articles

[40] S. Bianchi, G. Pedretti, I, Muñoz-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. 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

[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

[37] S. Bianchi, I. Muñoz-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

[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

[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

[34] Z. Sun, G. Pedretti, A. Bricalli and D. Ielmini, “One-step regression and classification with cross-point resistive memory arrays” Science Advances 6 (5) eaay2378 (2020). DOI: 10.1126/sciadv.aay2378

[33] Z. Wang, T. Zeng, Y. Ren, Y. Lin, H. Xu, X. Zhao, Y. Liu and D. Ielmini, “Realization of a generalized Bienenstock–Cooper–Munro learning rule through triplet-STDP in memristors for spatiotemporal patterns,” Nature Communications 11:1510 (2020). DOI: 10.1038/s41467-020-15158-3

[32] R. Carboni and D. Ielmini, “Stochastic Memory Devices for Security and Computing”, Advanced Electronic Materials (2019)

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

[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—Part II: Compact Modeling,” in IEEE Transactions on Electron Devices, vol. 66, no. 9, pp. 3802-3808 (2019) DOI: 10.1109/TED.2019.2928888

[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

[28] V. Milo, C. Zambelli, P. Olivo, E. Pérez, 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

[27] I. Muñoz-Martín, 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

[26] M. Lanza, et. al., “Recommended methods to study resistive switching devices,” Advanced Electronic Materials 5, 1 (2019)

[25] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli, W. Wang and D. Ielmini, “Solving matrix equations in one step with cross-point resistive arrays,” Proceedings of the National Academy of Science PNAS (2019).DOI:10.1073/pnas.1815682116

[24] W. Wang, M. Wang, E. Ambrosi, A. Bricalli, M. Laudato, Z. Sun, X. Chen and D. Ielmini, “Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices,” Nature Communications 10, 81 (2019).DOI: 0.1038/s41467-018-07979-0

[23] W. Wang, G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli and D. Ielmini, “Learning of spatio-temporal patterns in a spiking neural network with resistive switching synapses,” Science Advances 4, 9(2018). DOI: 10.1126/sciadv.aat4752

[22] Z. Sun, E. Ambrosi, A. Bricalli and D. Ielmini, “Logic Computing with stateful neural networks of resistive switches,” Advanced Materials 30, 38 (2018). DOI: 10.1002/adma.201802554

[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, “Enhancing the Matrix Addressing of Flexible Sensory Arrays by a Highly Nonlinear threshold switch” Advanced Materials 30, 33 (2018). DOI: 10.1002/adma.201802516

[20] A. Mehonic ,A. L. Shluger ,D. Gao, I. Valov, E. Miranda, D. Ielmini , A. Bricalli, E. Ambrosi, C. Li, J. J. Yang, Q. Xia and A. J. Kenyon “Silicon Oxide (SiOx): A Promising Material for Resistance Switching?”, Advanced Materials 30, 43 (2018). DOI: 10.1002/adma.201801187

 [19] D. Ielmini and H.-S.P. Wong “In-memory computing with resistive switching devices”, Nature Electronics 1 333-343 (2018) DOI: 0.1038/s41928-018-0092-2

 [18] Y. Ren, V. Milo, Z. Wang, H. Xu, D. Ielmini, X. Zhao, Y. Liu, “Analytical modeling of organic−inorganic CH3NH3PbI3 perovskite resistive switching and its application for neuromorphic recognition,” Advanced Theoretical Simulations 1,4 (2018) DOI: 10.1002/adts.201700035

 [17] R. Carboni, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu and D. Ielmini, “Random number generation by differential read of stochastic switching in spin-transfer torque memory,” IEEE Electron Device Letters 39, 7 (2018) DOI: 10.1109/LED.2018.2833543

[16] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, “A 4-transistors/one-resistor hybrid synapse based on resistive switching memory (RRAM) capable of spike-rate dependent plasticity (SRDP),” IEEE Trans. VLSI 26, 12 (2018). DOI: 10.1109/TVLSI.2018.2818978

[15] D. Ielmini, “Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks,” Microelectron. Eng. 190, 44-53 (2018). DOI:10.1016/j.mee.2018.01.009

 [14] R. Carboni, S. Ambrogio, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu and D. Ielmini, “Modeling of breakdown-limited endurance in spin-transfer torque (STT) magnetic memory under pulsed cycling regime,” IEEE Trans. Electron Devices 65, 2470-2478 (2018). DOI: 10.1109/TED.2018.2822343

[13] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, “Resistive switching device technology based on silicon oxide for improved on-off ratio – Part I: Memory devices,” IEEE Trans. Electron Devices 65, 115-121 (2018). DOI: 10.1109/TED.2017.2777986.

[12] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, “Resistive switching device technology based on silicon oxide for improved on-off ratio – Part II: Select devices,” IEEE Trans. Electron Devices 65, 122-128 (2018). DOI: 10.1109/TED.2017.2776085.

[11] D. Ielmini and V. Milo, “Physics-based modeling approaches of resistive switching devices for memory and in-memory computing applications,” J. Computation. Electron. 16(4), 1121-1143 (2017). DOI: 10.1007/s10825-017-1101-9

[10] G. Pedretti, V. Milo, S. Ambrogio, R. Carboni, S. Bianchi, A. Calderoni, N. Ramaswamy, A. S. Spinelli, D. Ielmini, “Stochastic learning in neuromorphic hardware via spike timing dependent plasticity with RRAM synapses,” IEEE J. Emerging Topics in Circuits and Systems (JETCAS) (2018). DOI: 10.1109/JETCAS.2017.2773124

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

[8] S. Balatti, S. Ambrogio, R. Carboni, V. Milo, Z.-Q. Wang, A. Calderoni, N. Ramaswamy, and D. Ielmini, “Physical unbiased generation of random numbers with coupled resistive switching devices,” IEEE Trans. Electron Devices 63, 2029-2035 (2016). DOI: 10.1109/TED.2016.2537792.

 [7] S. Ambrogio, S. Balatti, V. Milo, R. Carboni, Z. Wang, A. Calderoni, N. Ramaswamy, and D. Ielmini, “Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM,” IEEE Trans. Electron Devices 63, 1508-1515 (2016). DOI: 10.1109/TED.2016.2526647.

[6] D. Ielmini, “Resistive Switching Memories based on Metal Oxides: Mechanisms, Reliability and Scaling,” Semicond. Sci. Technol. 31, 063002 (2016). DOI: 10.1088/0268-1242/31/6/063002

[5] D. Ielmini, “Physical Models of Program and Read Fluctuations in Metal Oxide Resistive RAM,” ECS Trans. 75, 4, 19-26 (2016). DOI: 10.1149/07505.0019ecst

[4] S. Ambrogio, N. Ciocchini, M. Laudato, V. Milo, A. Pirovano, P. Fantini and D. Ielmini, “Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses,” Front. Neurosci. 10:56 (2016). DOI: 10.3389/fnins.2016.00056

[3] N. Ciocchini, M. Laudato, M. Boniardi, E. Varesi, P. Fantini, A. L. Lacaita, and D. Ielmini, “Bipolar switching in chalcogenide phase change memory,” Sci. Rep. 6, 29162 (2016). DOI: 10.1038/srep29162

[2] S. Ambrogio V. Milo, Z. Wang, S. Balatti, and D. Ielmini, “Analytical modeling of current overshoot in oxide-based resistive switching memory (RRAM),” IEEE Electron Device Lett. 37, 1268-1271 (2016). DOI: 10.1109/LED.2016.2600574

[1] Z. Wang, S. Ambrogio, S. Balatti, S. Sills, A. Calderoni, N. Ramaswamy, D. Ielmini, “Post-cycling degradation in metal-oxide bipolar resistive switching memory (RRAM),” IEEE Trans. Electron Devices 63, 4279-4287 (2016). DOI: 10.1109/TED.2016.2604370

 

 Conference proceedings

[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

[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

[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

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

[13] S. Bianchi, I. Muñoz-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

[12] E. Ambrosi, A. Bricalli, M. Laudato and D. Ielmini, Impact of oxide and electrode materials on the switching characteristics of oxide ReRAM devices2018 RSC Faraday Discussions (2018). DOI: 10.1039/C8FD00097B

[11] W. Wang, G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli and D. Ielmini, “Computing of Temporal Information among Spikes using ReRAM Synapse“ 2018 RSC Faraday Discussions (2018). DOI: 10.1039/C8FD00097B

[10] V. Milo, E. Chicca, and D. Ielmini, “Brain-inspired recurrent neural network with plastic RRAM synapses2018 IEEE International Symposium Circuits and Systems (ISCAS), Firenze, Italy (2018).
DOI: 10.1109/ISCAS.2018.8351523

[9] V. Milo, G. Pedretti, M. Laudato, A. Bricalli, E. Ambrosi, S. Bianchi, E. Chicca, and D. Ielmini, “Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks,” 2018 IEEE International Symposium Circuits and Systems (ISCAS), Firenze, Italy (2018). DOI:  10.1109/ISCAS.2018.8351824

[8] G. Pedretti, S. Bianchi, V. Milo, A. Calderoni, N. Ramaswamy, and D. Ielmini, “Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses,” IEDM Tech. Dig. 653-656 (2017). DOI: 10.1109/IEDM.2017.8268467

[7] V. Milo, D. Ielmini, and E. Chicca, “Attractor networks and associative memories with STDP learning in RRAM synapses,” IEDM Tech. Dig. 263-266 (2017). DOI: 10.1109/IEDM.2017.8268369

[6] A. Bricalli, E. Ambrosi, M. Laudato, M. Maestro, R. Rodriguez, and D. Ielmini, “SiOx-based resistive switching memory (RRAM) for crossbar storage/select elements with high on/off ratio,” IEDM Tech. Dig. 87 (2016).  DOI: 10.1109/IEDM.2016.7838344

[5] R. Carboni, S. Ambrogio, W. Chen, M. Siddik, J. Harms, A. Lyle, W. Kula, G. Sandhu, and D. Ielmini, “Understanding cycling endurance in perpendicular spin-transfer torque (p-STT) magnetic memory,” IEDM Tech. Dig. 572 (2016). DOI: 10.1109/IEDM.2016.7838468

 [4] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, “Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity,” IEDM Tech. Dig. 440 (2016). DOI: 10.1109/IEDM.2016.7838435

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

[2] D. Ielmini, S. Ambrogio, V. Milo, S. Balatti, and Z.-Q. Wang, “Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning,” 2016 IEEE International Symposium Circuits and Systems (ISCAS), Montreal, Canada, May 22-25, 2016. DOI: 10.1109/ISCAS.2016.7527508

[1] N. Ciocchini, M. Laudato, A. L. Lacaita, D. Ielmini, M. Boniardi, E. Varesi, P. Fantini, “Bipolar-switching operated phase change memory (PCM) for improved high-temperature reliability,” Proc. ESSDERC (2016). DOI: 10.1109/ESSDERC.2016.7.

 

Patent applications

[2] D. Ielmini, Z. Sun, and G. Pedretti, “Circuito di risoluzione di problemi matematici comprendente elementi resistivi”, patent application n. 812017000124370, Oct. 31, 2017.

[1] D. Ielmini, S. Balatti, and S. Ambrogio, ‘Random number generation from multiple memory states,’ WO 2017/153875 A1.