Search Results - "Castellani, Niccolo'"
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In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling
Published in Nature electronics (01-02-2021)“…Resistive memory technologies could be used to create intelligent systems that learn locally at the edge. However, current approaches typically use learning…”
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Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
Published in Nature communications (20-11-2023)“…Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks,…”
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Improvement of Phase‐Change Memory Performance by Means of GeTe/Sb2Te3 Superlattices
Published in Physica status solidi. PSS-RRL. Rapid research letters (01-03-2021)“…GeTe/Sb2Te3 superlattices (SLs) obtained by sputtering are integrated in phase‐change memory (PCM) devices with a “wall structure”. The high structural quality…”
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Ex Situ Transfer of Bayesian Neural Networks to Resistive Memory‐Based Inference Hardware
Published in Advanced intelligent systems (01-08-2021)“…Neural networks cannot typically be trained locally in edge‐computing systems due to severe energy constraints. It has, therefore, become commonplace to train…”
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Innovative Low-Power Self-Nanoconfined Phase-Change Memory
Published in IEEE transactions on electron devices (01-02-2021)“…In this article, we demonstrate at array level and in industrial-like devices, the extreme scaling down to nanometric dimensions of the phase-change memory…”
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Investigation of the Turn-ON of T-RAM Cells Under Transient Conditions
Published in IEEE transactions on electron devices (01-04-2015)“…This paper presents an experimental investigation of the turn-ON of nanoscale T-RAM cells under the transient conditions given by the depletion of the p-base…”
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Experimental Demonstration of Multilevel Resistive Random Access Memory Programming for up to Two Months Stable Neural Networks Inference Accuracy
Published in Advanced intelligent systems (01-11-2022)“…Crossbars of resistive memories, or memristors, provide a road to reduce the energy consumption of artificial neural networks, by naturally implementing…”
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Working Principles of a DRAM Cell Based on Gated-Thyristor Bistability
Published in IEEE electron device letters (01-09-2014)“…This letter discusses the working principles of a memory cell exploiting the bistability of a single nanoscale gated-thyristor to achieve high-performance DRAM…”
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Implementation of binarized neural networks immune to device variation and voltage drop employing resistive random access memory bridges and capacitive neurons
Published in Communications engineering (18-06-2024)“…Resistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing…”
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10
Early versus late tracheostomy for traumatic brain injury: a systematic review and meta-analysis
Published in Minerva anestesiologica (01-05-2023)“…Tracheostomy is the most frequent bedside surgical procedure performed on patients with traumatic brain injury who require mechanical ventilation. To compare…”
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Dynamic Analysis of Current-Voltage Characteristics of Nanoscale Gated-Thyristors
Published in IEEE electron device letters (01-05-2013)“…This letter presents a detailed experimental investigation of the current-voltage characteristics of deca-nanometer gated-thyristors, highlighting that strong…”
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DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
Published in Nature communications (24-04-2024)“…Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic…”
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Neuromorphic object localization using resistive memories and ultrasonic transducers
Published in Nature communications (18-06-2022)“…Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing…”
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Author Correction: DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
Published in Nature communications (07-05-2024)Get full text
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Reliability investigation of T-RAM cells for DRAM applications
Published in 2014 IEEE International Reliability Physics Symposium (01-06-2014)“…In this work, we present a reliability investigation of T-RAM cells, considering their read failure, data retention and endurance. Experimental results on…”
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Conference Proceeding -
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Narrow Heater Bottom Electrode‐Based Phase Change Memory as a Bidirectional Artificial Synapse
Published in Advanced electronic materials (01-09-2018)“…Phase change memory can provide a remarkable artificial synapse for neuromorphic systems, as it features excellent reliability and can be used as an analog…”
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Multilayered Sb-Rich GeSbTe Phase-Change Memory for Best Endurance and Reduced Variability
Published in IEEE transactions on electron devices (01-08-2022)“…Sb-rich GeSbTe-based phase-change memories (PCMs) were studied in the past years for their high switching speed to target storage class memory (SCM)…”
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Three-Dimensional Electrostatics- and Atomistic Doping-Induced Variability of RTN Time Constants in Nanoscale MOS Devices-Part II: Spectroscopic Implications
Published in IEEE transactions on electron devices (01-09-2012)“…This paper investigates the impact of 3-D electrostatics and atomistic doping on the spectroscopic analysis of random telegraph noise (RTN) traps in nanoscale…”
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Three-Dimensional Electrostatics- and Atomistic Doping-Induced Variability of RTN Time Constants in Nanoscale MOS Devices-Part I: Physical Investigation
Published in IEEE transactions on electron devices (01-09-2012)“…This paper presents a detailed simulation analysis of the impact of 3-D electrostatics and atomistic doping on the variability of the random telegraph noise…”
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Accuracy and Issues of the Spectroscopic Analysis of RTN Traps in Nanoscale MOSFETs
Published in IEEE transactions on electron devices (01-02-2013)“…This paper investigates the limitations to the accuracy and the main issues of the spectroscopic analyses of random telegraph noise (RTN) traps in nanoscale…”
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