Ielmini, Daniele

In-memory computing with resistive switching devices

Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 2,298

Hardware implementation of memristor-based artificial neural networks

Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 564

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks

Milano, G., Pedretti, G., Montano, K., Ricci, S., Hashemkhani, S., Boarino, L., Ielmini, D., & Ricciardi, C. (2022) · Nature Materials

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 462

Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices

Wang, W., Wang, M., Ambrosi, E., Bricalli, A., Laudato, M., Sun, Z., Chen, X., & Ielmini, D. (2019) · Nature Communications

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 328

Study of Multilevel Programming in Programmable Metallization Cell (PMC) Memory

Russo, U., Kamalanathan, D., Ielmini, D., Lacaita, A. L., & Kozicki, M. N. (2009) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 319

Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM

Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Z.-Q., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 256

True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices

Balatti, S., Ambrogio, S., Wang, Z., & Ielmini, D. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Advanced Memory and Neural ComputingPhysical Unclonable Functions (PUFs) and Hardware Security

Cited by 147

Physical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices

Balatti, S., Ambrogio, S., Carboni, R., Milo, V., Wang, Z., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices

Physical Unclonable Functions (PUFs) and Hardware SecurityAdvanced Memory and Neural Computing

Cited by 127

Statistics of Resistance Drift Due to Structural Relaxation in Phase-Change Memory Arrays

Boniardi, M., Ielmini, D., Lavizzari, S., Lacaita, A. L., Redael, A., & Pirovano, A. (2010) · IEEE Transactions on Electron Devices

Phase-change materials and chalcogenidesAdvanced Memory and Neural Computing

Cited by 106

Reliability Impact of Chalcogenide-Structure Relaxation in Phase-Change Memory (PCM) Cells—Part II: Physics-Based Modeling

Lavizzari, S., Ielmini, D., Sharma, D., & Lacaita, A. L. (2009) · IEEE Transactions on Electron Devices

Phase-change materials and chalcogenidesAdvanced Memory and Neural Computing

Cited by 74

Conductance variations and their impact on the precision of in-memory computing with resistive switching memory (RRAM)

Pedretti, G., Ambrosi, E., & Ielmini, D. (2021) · 2021 IEEE International Reliability Physics Symposium (IRPS)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 39

2022 roadmap on neuromorphic computing and engineering

Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 0