Publications associées à Memristor

Emulating Multimemristive Behavior of Silicon Nanowire-Based Biosensors by Using CMOS-Based Implementations

Sandro Carrara, Junrui Chen, Kapil Bhardwaj

The research presented in this article draws inspiration from previous efforts aimed at replicating the functions of various solid-state memristors using a variety of materials. The memristor circuit emulator serves as a cost-effective tool for circuit des ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

Mohammad Khaja Nazeeruddin, Paul Joseph Dyson, Yong Ding, Li Tao, Yao Zhang, Jun Zhang, Bowen Jiang

Hybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable ...

WILEY2023

Exploring negative capacitance and neuromorphic devices based on CMOS-compatible ferroelectric HfO2

Carlotta Gastaldi

Ferroelectric materials are explored for numerous applications thanks to their properties associated with electrically switchable spontaneous polarization. Perovskites are an established class of ferroelectrics used for sensors and actuators. However, they ...

EPFL2022

Dielectric properties of 3-3 flexible composites by infiltration of elastomers into porous ceramic structures using cellulose scaffold

Frank Nüesch, Thomas Geiger

Elastomer composites are prepared by infiltrating polydimethylsiloxane (PDMS) into a porous ceramic structure of nanoparticles. This method differs from the conventional approach, where particles are dispersed into the polymer matrix, since here, the polym ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Hard and Soft Switching Losses in Power Converters: Role of Transistor Output Capacitance

Palliyage Srilak Nirmana Perera

To best utilize power converters, a sound understanding of the relationship between the circuit topology and the power-semiconductor-device characteristics is required. This is especially important in high-frequency switching, where device parasitics start ...

EPFL2022

The Birth of a New Field: Memristive Sensors. A Review

Sandro Carrara

Over the last 11 years, the field of Memristive Sensors abruptly emerged in literature. The present review paper has the aim to acknowledge such an exciting new field of research as well as to critically review its most significant outcomes. As usual in sc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Gate energy efficiency and negative capacitance in ferroelectric 2D/2D TFET from cryogenic to high temperatures

Mihai Adrian Ionescu, Ali Saeidi, Teodor Rosca, Matteo Cavalieri, Carlotta Gastaldi, Luca Capua, Sadegh Kamaei Bahmaei

We report the fabrication process and performance characterization of a fully integrated ferroelectric gate stack in a WSe2/SnSe2 Tunnel FETs (TFETs). The energy behavior of the gate stack during charging and discharging, together with the energy loss of a ...

NATURE PORTFOLIO2021

Intrinsic switching in Si-doped HfO2: A study of Curie-Weiss law and its implications for negative capacitance field-effect transistor

Mihai Adrian Ionescu, Igor Stolichnov, Ali Saeidi, Teodor Rosca, Eamon Patrick O'Connor, Matteo Cavalieri, Carlotta Gastaldi, Sadegh Kamaei Bahmaei

HfO2-based ferroelectrics are considered a promising class of materials for logic and memory applications due to their CMOS compatibility and ferroelectric figures of merit. A steep-slope field-effect-transistor (FET) switch is a device for logic applicati ...

AMER INST PHYSICS2021

Thermally-Compensated Magnetic Core Loss Model for Time-Domain Simulations of Electrical Circuits

Drazen Dujic, Min Luo, Nikolina Djekanovic

This paper introduces a thermally-compensated magnetic hysteresis model, capable of accurately determining core losses of ferrite materials while accounting for core temperature variations. The employed model is based on permeance-capacitance analogy and i ...

2021

20 Years of IEEE SENSORS JOURNAL

Sandro Carrara

The IEEE Sensors Journal was born on June 1, 2001, when its first ever issue was published. The journal has grown exponentially over the past 20 years to become one of the world's largest journals in sensor engineering and technology as well as one of the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

Intrinsic or nucleation-driven switching: An insight from nanoscopic analysis of negative capacitance Hf1-xZrxO2-based structures

Mihai Adrian Ionescu, Igor Stolichnov, Matteo Cavalieri, Carlotta Gastaldi

HfO2-based ferroelectrics have dramatically changed the application perspectives of polarization-switching materials for information processing and storage. Their CMOS compatibility and preservation of high reversible polarization down to a few nanometer t ...

AMER INST PHYSICS2020

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara, Evangelos Eleftheriou

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

IEEE2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

Nature Research2020

Output-Capacitance Hysteresis Losses of Field-Effect Transistors

Elison de Nazareth Matioli, Armin Jafari, Palliyage Srilak Nirmana Perera, Luca Nela, Georgios Kampitsis

Resonant-type power converters are supposed to generate zero switching losses during soft-switching operation. In recent research, unexpected switching losses were reported in these converters, which were attributed to a large-signal hysteresis observed in ...

IEEE2020

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventio ...

Nature Research2020

Permanent photodoping of plasmonic gallium-ZnO nanocrystals

Reinis Ignatans

Donor dopants in oxide semiconductors are compensated not only by valuable electrons but also by other point defects, leading to a decrease in electric conductivity and infrared absorption. We demonstrate that the electron compensation mechanism in Ga dope ...

ROYAL SOC CHEMISTRY2020

ReRAM From Material Study to CMOS Co-integration

Elmira Shahrabi

The revolution of information-technology owes to silicon-based complementary-metal-oxide (CMOS) technology. However, CMOS technology approaches its physical limitation hardening the further progress of memory devices as well as computing paradigm requiring ...

EPFL2019

Memristive Biosensors based on Full-size Antibodies and Antibody Fragments

Giovanni De Micheli, Ioulia Tzouvadaki, Sandro Carrara, Sébastien Naus

For the first time memristive biosensors developed by the incorporation of scAbs (single-chain antibody fragments) enabling label-free detection of free Prostate Specific Antigen (fPSA) for early-stage detection of prostate cancer (PC) are reported. In add ...

2019