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QUEFORMAL

Financiado

Quantum Engineering for Machine Learning

We propose the radical vision of a new integrated circuit technology for machine learning where low-voltage field-effect transistors and non-volatile memories are integrated next to each other exploiting quantum engineering of het... ver más

31/12/2022

UNIPI

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

UNIVERSITA DI PISA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 7 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-12-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

FETOPEN-01-2018-2019-2020: FET-Open Challenging Current Thinking

I+D

Cerrada hace 6 años

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7 Participantes

UNIPI

658.00K€ | Lider

MS ENERTECH

340.72K€ | Participante

EPFL

587.79K€ | Participante

GESELLSCHAFT FUR ANGEWANDTE...

587.50K€ | Participante

Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 48 meses Fecha Inicio: 2018-12-18
Fecha Fin: 2022-12-31

Líder del proyecto

UNIVERSITA DI PISA

TRL 4-5

Presupuesto del proyecto 3M€

Descripción del proyecto We propose the radical vision of a new integrated circuit technology for machine learning where low-voltage field-effect transistors and non-volatile memories are integrated next to each other exploiting quantum engineering of heterostructures of two-dimensional materials (2DMs), i.e. the atom-by-atom design and fabrication of devices which combine vertical and lateral heterostructures (VH and LH, respectively) of 2DMs. QUEFORMAL pursues a very risky and original proposed solution, with the extremely high potential gain of advancing a science-enabled technology for the fabrication of integrated circuits for machine learning, in a field in which Europe has a strong basic-science leadership, thanks to the pioneering breakthroughs on graphene and 2D materials. The overall objective and targeted breakthrough of QUEFORMAL is to experimentally demonstrate the fabrication and operation of devices based on LH and VH of 2DMs for logic-in-memory integrated circuits and to show the potential of this technology for the fabrication of integrated circuits for machine learning. Devices include i) lateral heterostructure FETs (LH-FETs) operating at low voltage (0.6 V) fabricated in close vicinity to ii) floating-gate non-volatile memories based on VHs for the gate stack and LHs for the channel (LVH-NVMs), that can be programmed at low voltage (<5 V) with retention time larger than 1 month. The QUEFORMAL consortium consists of six partners and has unique advantages: Consortium members have proposed and patented the LH-FET concept and have experimentally demonstrated the floating gate non-volatile memory concept using 2D materials.

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