Summary
Neuromorphic computing such as deep-learning algorithms arise as a promising solution to treat the exploding amount of data generated worldwide, but at the cost of expensive time and energy budget on conventional hardware. There is an urgent need for a low-power, compact neuromorphic chip that can support bio-inspired computing: a network of cells collocating storage (non-volatility) and computing (synaptic plasticity). This work proposes to achieve such cell using a ferroelectric resistor, down-scaled to nanometer thickness to allow direct electron tunneling through the ferroelectric barrier (ferroelectric tunnel junction): the learning functionality (i.e. synaptic plasticity) will be implemented through the control of the distribution of the (non-volatile) ferroelectric domains. The fellow will bring her expertise in ferroelectric tunnel junctions and will combine it with IBM’s expertise in device and circuits integration and characterization, making use of the state-of-the-art equipment offered by their research center. In order to accelerate the creation of an end-to-end neuromorphic device, she will lead a collaboration with ETH Zurich and will benefit from their expertise in predictive physics-based modeling. The research project will aim at: (i) the demonstration of a non-volatile and plastic ferroelectric “synapse” made of Hf0.5Zr0.5O2 – a fully CMOS-compatible material, (ii) the development of models of individual cells and of a neural network and (iii) providing design guidelines for neuromorphic hardware based on this technology. The outcome will be to evaluate performances not only of individual synapses but of a neural network as a whole. Through the Action, the fellow will not only aim at creating a novel technology; but also at leading an interdisciplinary research project uniting complementary actors for an innovative solution to a society challenge.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/840903 |
| Start date: | 01-06-2019 |
| End date: | 31-05-2021 |
| Total budget - Public funding: | 191 149,44 Euro - 191 149,00 Euro |
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Original description
Neuromorphic computing such as deep-learning algorithms arise as a promising solution to treat the exploding amount of data generated worldwide, but at the cost of expensive time and energy budget on conventional hardware. There is an urgent need for a low-power, compact neuromorphic chip that can support bio-inspired computing: a network of cells collocating storage (non-volatility) and computing (synaptic plasticity). This work proposes to achieve such cell using a ferroelectric resistor, down-scaled to nanometer thickness to allow direct electron tunneling through the ferroelectric barrier (ferroelectric tunnel junction): the learning functionality (i.e. synaptic plasticity) will be implemented through the control of the distribution of the (non-volatile) ferroelectric domains. The fellow will bring her expertise in ferroelectric tunnel junctions and will combine it with IBM’s expertise in device and circuits integration and characterization, making use of the state-of-the-art equipment offered by their research center. In order to accelerate the creation of an end-to-end neuromorphic device, she will lead a collaboration with ETH Zurich and will benefit from their expertise in predictive physics-based modeling. The research project will aim at: (i) the demonstration of a non-volatile and plastic ferroelectric “synapse” made of Hf0.5Zr0.5O2 – a fully CMOS-compatible material, (ii) the development of models of individual cells and of a neural network and (iii) providing design guidelines for neuromorphic hardware based on this technology. The outcome will be to evaluate performances not only of individual synapses but of a neural network as a whole. Through the Action, the fellow will not only aim at creating a novel technology; but also at leading an interdisciplinary research project uniting complementary actors for an innovative solution to a society challenge.Status
CLOSEDCall topic
MSCA-IF-2018Update Date
28-04-2024
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