Project description DEENESFRITPL Low-power non-volatile memory based on spin-orbit torque switching Most parts of modern computers are made with volatile devices such as transistors, which lose information when powered off. Requiring no power to maintain stored data, magnetoresistive random-access memory (MRAM) is an emerging class of non-volatile memory that opens the door to a new paradigm based on normally-off and instant-on operation. What is more, this type of memory reduces the gigantic energy losses associated with data storage and leakage power consumption. The EU-funded SOTMEM project aims to demonstrate advanced MRAM elements whose state will be controlled by spin-orbit torque magnetisation switching. Spin-orbit torque MRAM provides a route around the limited speed as well as reliability and degradation issues that spin-transfer torque MRAM faces. The project is set to accelerate the transition towards low-power and secure microprocessors with non-volatile memory. Show the project objective Hide the project objective Objective SOTMEM addresses the growing need for scalable ultrafast non-volatile memories (NVM) to improve the reliability of logic circuitry and to reduce the ever increasing power consumption and energy loss in microprocessors. Major technology actors and end-user companies are developing magnetic RAM (MRAM) as it is recognized as one of the most promising emerging NVMs. However, mainstream MRAM, relying on spin transfer torque, suffers from limited speed, and reliability and degradation issues. These three obstacles, which impede the widespread implementation of MRAM, can be mitigated using a novel device architecture based on spin-orbit torque (SOT). But even SOT-MRAM brings challenges in the form of too large writing current density and power dissipation . SOTMEM will validate SOT switches for SOT-MRAM using topological insulators embedded in a novel material stack that we have recently developed for optimal writing efficiency. Writing at ultralow power is therefore the key technical objective of this ERC Proof of Concept project. A successful outcome would represent a major breakthrough for SOT-MRAM commercialization. Therefore SOTMEM stands to have enormous impact in the transition towards low-power, power-fail protected microprocessors with non-volatile memories. Special efforts will be devoted to market analysis and patentability studies, as well as targeted industry engagement to allow the design of a nuanced business model aiming to deliver SOTMEM technologies and knowhow to potential end users. Fields of science engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarecomputer processorssocial scienceseconomics and businessbusiness and managementbusiness models Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2019-POC - ERC Proof of Concept Grant Call for proposal ERC-2019-PoC See other projects for this call Funding Scheme ERC-POC-LS - ERC Proof of Concept Lump Sum Pilot Coordinator FUNDACIO INSTITUT CATALA DE NANOCIENCIA I NANOTECNOLOGIA Net EU contribution € 150 000,00 Address Campus de la uab edifici q icn2 08193 Cerdanyola del valles Spain See on map Region Este Cataluña Barcelona Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window EU contribution No data Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all FUNDACIO INSTITUT CATALA DE NANOCIENCIA I NANOTECNOLOGIA Spain Net EU contribution € 150 000,00 Address Campus de la uab edifici q icn2 08193 Cerdanyola del valles See on map Region Este Cataluña Barcelona Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window EU contribution No data