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Quantum Engineering for Machine Learning

Quantum Engineering for Machine Learning

Objective

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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Coordinator

UNIVERSITA DI PISA

Address

Lungarno Pacinotti 43/44
56126 Pisa

Italy

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 658 000

Participants (5)

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GESELLSCHAFT FUR ANGEWANDTE MIKRO UND OPTOELEKTRONIK MIT BESCHRANKTERHAFTUNG AMO GMBH

Germany

EU Contribution

€ 587 500

UNIVERSITAET DER BUNDESWEHR MUENCHEN

Germany

EU Contribution

€ 549 500

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

Switzerland

EU Contribution

€ 587 790

CONSIGLIO NAZIONALE DELLE RICERCHE

Italy

EU Contribution

€ 262 500

QUANTAVIS SRL

Italy

EU Contribution

€ 343 450

Project information

Grant agreement ID: 829035

Status

Ongoing project

  • Start date

    1 January 2019

  • End date

    31 December 2021

Funded under:

H2020-EU.1.2.1.

  • Overall budget:

    € 2 988 740

  • EU contribution

    € 2 988 740

Coordinated by:

UNIVERSITA DI PISA

Italy