Control of Resistive Switching in Mott Memories Based on TiN/AM4Q8/TiN MIM Devices

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

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 ...

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 ...

In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of th ...

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 ...

Developing facile approaches to prepare non-light-scattering ternary oxide thin film photoelectrodes is an important goal for solar water splitting tandem cells. Herein, a novel synthesis route is reported that employs ethylenediaminetetraacetic acid (EDTA ...

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 ...

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 ...

Transparent conductive materials (TCMs) with high p-type conductivity and broadband transparency have remained elusive for years. Despite decades of research, no p-type material has yet been found to match the performance of n-type TCMs. If developed, the ...

Resistive Random Access Memories (ReRAMs) have been researched intensively in the last past decades as a promising alternatives technology for the next-generation non-volatile memory (NVM) devices. ReRAMâ s excellent performance properties such as high sw ...

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 ...

Resistive-switching random access memory (ReRAM) technologies are nowadays a good candidate to overcome the bottleneck of Von Neumann architectures, taking advantage of their logic-in-memory capability and the ability to mimic biological synapse behavior. ...

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 ...

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. ...

The integration of the resistive random access memory (ReRAM) with CMOS logic circuitry provides a solution to scaling limitations, and offers promising candidates for use in next generation computing applications. It is challenging to realize a reliable, ...

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cos ...

Amorphous/crystalline silicon heterojunction (SHJ) solar cells technology is attracting tremendous attention in recent years due to its potential to achieve high power conversion efficiencies at low fabrication temperatures and using few process steps. How ...

Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computatio ...

With the continuous scaling of CMOS technology, integrating an embedded high-density non-volatile memory appears to be more and more costly and technologically challenging. Beyond floating-gate memory technologies, bipolar resistive random access memories ...

Control of Resistive Switching in Mott Memories Based on TiN/AM4Q8/TiN MIM Devices

Chattez avec Graph Search

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

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

Accurate deep neural network inference using computational phase-change memory

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Distributed Computing with Modern Shared Memory

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

Generalized Synthesis to Produce Transparent Thin Films of Ternary Metal Oxide Photoelectrodes

Multi-memristive synaptic architectures for training neural networks

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

Bridging the p-type transparent conductive materials gap: synthesis approaches for disperse valence band materials

Material and Electrical study of HfO2-Based Resistive Random Access Memories (ReRAMs)

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

Analog Control of Retainable Resistance Multistates in HfO2 Resistive-Switching Random Access Memories (ReRAMs)

ReRAM From Material Study to CMOS Co-integration

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

Performance improvement of chip-level CMOS-integrated ReRAM cells through material optimization

Fine-Grain Checkpointing with In-Cache-Line Logging

Impact of the Oxygen Content on the Optoelectronic Properties of the Indium-Tin-Oxide Based Transparent Electrodes for Silicon Heterojunction Solar Cells

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Resistive Switching Memory Architecture Based on Polarity Controllable Selectors