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Concept

Phase-change memory

Related publications (32)

All-Dielectric Nanophotonic via Glass Fluid Instabilities

Pierre-Luc Eloi Piveteau

In this thesis work, we propose to exploit an innovative micro/nano-fabrication process, based on controlled fluid instabilities of a thin viscous film of chalcogenide glass. Amorphous selenium and arsenic triselenide were used in this thesis work, and com ...

EPFL2024

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Zhenyu Wang

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

EPFL2024

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Guilherme Migliato Marega

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

EPFL2023

Femtosecond laser-induced modifications and self-organization in complex glass systems

Gözden Torun

Ultrashort laser pulses, i.e., pulses emitted shorter than a picosecond, can tailor material properties by introducing permanent modifications locally in three dimensions. Remarkably, under a certain exposure condition, these modifications are accompanied ...

EPFL2023

Impact of Interface Traps in Floating-Gate Memory Based on Monolayer MoS2

Andras Kis, Guilherme Migliato Marega

Two-dimensional materials (2DMs) have found potential applications in many areas of electronics, such as sensing, memory systems, optoelectronics, and power. Despite an intense experimental work, the literature is lacking of accurate modeling of nonvolatil ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Exploring ferroelectricity in HfO2-based thin films by tackling application-relevant challenges

Matteo Cavalieri

Ferroelectric materials offer a broad range of application-relevant properties, including spontaneous polarization switchable by electric field. Archetypical representatives of this class of materials are perovskites, currently used in applications ranging ...

EPFL2021

Memory chip or memory array for wide-voltage range in-memory computing using bitline technology

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon

A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcells, a p ...

2021

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, 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

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara

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

Write Termination circuits for RRAM : A Holistic Approach From Technology to Application Considerations

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

While Resistive Random Access Memories (RRAM) are perceived nowadays as a promising solution for the future of computing, these technologies suffer from intrinsic variability regarding programming voltage, switching speed and achieved resistance values. Wr ...

IEEE2020

Improved thermal stability and fast phase change speed of Y-doped Sb7Se3 thin film for phase change memory applications

Xiao Zhou, Haitao Zhou

By combining experiments and first-principles calculations, Y-doped Sb7Se3 thin films were successfully fabricated and investigated. Our results suggest that Y is one promising dopant for simultaneously enhancing thermal stability, increasing phase change ...

ELSEVIER2020

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

Perovskite Flash Memory with a Single-Layer Nanofloating Gate

Mohammad Khaja Nazeeruddin, Peng Gao, Tom Theodorus Antonius Lummen, Peng Gao, Hobeom Kim

Here we use triple-cation metal-organic halide perovskite single crystals for the transistor channel of a flash memory device. Moreover, we design and demonstrate a 10 nm thick single-layer nanofloating gate. It consists of a ternary blend of two organic s ...

2020

Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Marco Antonio Rios

Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a "simple'' repl ...

IEEE2020

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

Irem Boybat Kara, 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

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

Switching event detection and self-termination programming circuit for energy efficient ReRAM memory arrays

Alexandre Sébastien Julien Levisse

Energy efficiency remains a challenge for the design of non-volatile resistive memories (ReRAMs) arrays. This memory technology suffers from intrinsic variability in switching speed, programming voltages and resistance levels. The programming conditions of ...

2019

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Pierre-Emmanuel Julien Marc Gaillardon, Marina Zapater Sancho, Xifan Tang, Yasir Mahmood Qureshi, Edouard Giacomin

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat Kara, Abu Sebastian

Non-volatile analog memory devices such as phase-change memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this in-memory computing approach, the analog conductance states of the ...

IEEE2019

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Abu Sebastian

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

IEEE2019