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Jean-Michel Portal

Researcher at Centre national de la recherche scientifique

Publications -  145
Citations -  2335

Jean-Michel Portal is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Resistive random-access memory & Artificial neural network. The author has an hindex of 25, co-authored 136 publications receiving 2047 citations. Previous affiliations of Jean-Michel Portal include Alternatives & Aix-Marseille University.

Papers
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Proceedings ArticleDOI

EEPROM memory: threshold voltage built in self diagnosis

TL;DR: A built in self-diagnosis of EEPROM memory cells, based on threshold voltage extraction is presented and complementary information is proposed to improve the classical memory diagnosis.
Journal ArticleDOI

EEPROM Diagnosis Based on Threshold Voltage Embedded Measurement

TL;DR: A built in self-diagnosis of EEPROM memory cells, based on threshold voltage extraction, is presented and complementary information is proposed to improve the classical memory diagnosis methodology.
Journal ArticleDOI

Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays.

TL;DR: This work proposes a strategy for implementing low-energy Binarized Neural Networks that employs brain-inspired concepts while retaining the energy benefits of digital electronics, and designs, fabricate, and test a memory array that is optimized for this in-memory computing scheme.
Journal ArticleDOI

Self-consistent physical modeling of set/reset operations in unipolar resistive-switching memories

TL;DR: In this article, a self-consistent physical model for set/reset operations involved in unipolar resistive switching memories integrating a transition metal oxide is presented, where set operation is described in terms of a local electrochemical reduction of the oxide leading to the formation of metallic conductive filaments.
Proceedings ArticleDOI

In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks

TL;DR: This work fabricated and tested a differential HfO2-based memory structure and its associated sense circuitry, which are ideal for in-memory computing and shows for the first time that this approach achieves the same reliability benefits as error correction, but without any CMOS overhead.