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Alessandro S. Spinelli
Researcher at Polytechnic University of Milan
Publications - 226
Citations - 5163
Alessandro S. Spinelli is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Flash memory & NAND gate. The author has an hindex of 37, co-authored 220 publications receiving 4595 citations. Previous affiliations of Alessandro S. Spinelli include STMicroelectronics & Sapienza University of Rome.
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Physics and numerical simulation of single photon avalanche diodes
TL;DR: In this paper, the authors present results of the numerical simulation of the transient behavior of shallow junction single photon avalanche diodes (SPADs) and develop a bidimensional model for above breakdown simulations and show that the initially photogenerated charge density builds up locally by an avalanche multiplication process and then spreads over the entire detector area by a diffusion-assisted process.
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Comprehensive Analysis of Random Telegraph Noise Instability and Its Scaling in Deca–Nanometer Flash Memories
TL;DR: In this paper, a comprehensive investigation of random telegraph noise (RTN) in deca-nanometer Flash memories, considering both the nor and the nand architecture, is presented, evidencing that the slope of its exponential tails is the critical parameter determining the scaling trend for RTN.
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Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.
Wei Wang,Giacomo Pedretti,Valerio Milo,Roberto Carboni,Alessandro Calderoni,Nirmal Ramaswamy,Alessandro S. Spinelli,Daniele Ielmini +7 more
TL;DR: It is shown that, due to the sensitivity to precise spike timing, the spatiotemporal neural network is able to mimic the sound azimuth detection of the human brain.
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Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
Giacomo Pedretti,Valerio Milo,Stefano Ambrogio,Roberto Carboni,S. Bianchi,Alessandro Calderoni,Nirmal Ramaswamy,Alessandro S. Spinelli,Daniele Ielmini +8 more
TL;DR: Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.
Journal ArticleDOI
Statistical Model for Random Telegraph Noise in Flash Memories
C. Monzio Compagnoni,R. Gusmeroli,Alessandro S. Spinelli,Andrea L. Lacaita,M. Bonanomi,Angelo Visconti +5 more
TL;DR: In this article, a probabilistic superposition of elementary Markov processes describing the trapping/detrapping events taking place in the cell tunnel oxide was proposed to explain the main features of the random telegraph noise threshold-voltage instability.