Summary
This project aims to address the pressing issue of energy consumption in data transmission and processing by developing materials technology for efficient memory computing. Currently, data centers consume a significant amount of electricity, and traditional computing architectures are inefficient. Neuromorphic computing, inspired by the brain's functioning, offers a promising solution with ultra-fast communication and low energy consumption. Photonic processors, which use light for probing, are particularly attractive for achieving these goals.
The project focuses on integrating advanced materials into existing silicon photonic (SiPh) platforms to create efficient reconfigurable circuits. Refractive index tuning methods in Si are limited, and the project seeks to overcome them by integrating materials like Phase Change Materials (PCMs) and suitable 2D transition metal dichalcogenides (TMDs) to demonstrate optical memristors. These materials can emulate neural systems, offering control over switching, energy consumption, heat dissipation, and other crucial parameters.
The project's objectives include optimizing materials technology, designing optical memristors based on PCMs and 2D materials, exploring new configurations with PCM+MEMs, investigating memory functionality in VO2, and demonstrating neural response. The goal is to improve data processing speeds while reducing power consumption, ultimately leading to energy-efficient computing. Additionally, the project aims to assess the environmental impact of these advancements through empirical cost comparisons. The researcher envisions that this experience will empower them to become a high-quality academic researcher with a focus on materials photonics. Overall, the project addresses a critical need in the field of computing by developing materials technology for energy-efficient memory computing, aligning with the goals of the European Green Deal towards the pursuit of ultra-fast, low-energy data processing.
The project focuses on integrating advanced materials into existing silicon photonic (SiPh) platforms to create efficient reconfigurable circuits. Refractive index tuning methods in Si are limited, and the project seeks to overcome them by integrating materials like Phase Change Materials (PCMs) and suitable 2D transition metal dichalcogenides (TMDs) to demonstrate optical memristors. These materials can emulate neural systems, offering control over switching, energy consumption, heat dissipation, and other crucial parameters.
The project's objectives include optimizing materials technology, designing optical memristors based on PCMs and 2D materials, exploring new configurations with PCM+MEMs, investigating memory functionality in VO2, and demonstrating neural response. The goal is to improve data processing speeds while reducing power consumption, ultimately leading to energy-efficient computing. Additionally, the project aims to assess the environmental impact of these advancements through empirical cost comparisons. The researcher envisions that this experience will empower them to become a high-quality academic researcher with a focus on materials photonics. Overall, the project addresses a critical need in the field of computing by developing materials technology for energy-efficient memory computing, aligning with the goals of the European Green Deal towards the pursuit of ultra-fast, low-energy data processing.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101154728 |
| Start date: | 01-03-2025 |
| End date: | 28-02-2027 |
| Total budget - Public funding: | - 181 152,00 Euro |
Cordis data
Original description
This project aims to address the pressing issue of energy consumption in data transmission and processing by developing materials technology for efficient memory computing. Currently, data centers consume a significant amount of electricity, and traditional computing architectures are inefficient. Neuromorphic computing, inspired by the brain's functioning, offers a promising solution with ultra-fast communication and low energy consumption. Photonic processors, which use light for probing, are particularly attractive for achieving these goals.The project focuses on integrating advanced materials into existing silicon photonic (SiPh) platforms to create efficient reconfigurable circuits. Refractive index tuning methods in Si are limited, and the project seeks to overcome them by integrating materials like Phase Change Materials (PCMs) and suitable 2D transition metal dichalcogenides (TMDs) to demonstrate optical memristors. These materials can emulate neural systems, offering control over switching, energy consumption, heat dissipation, and other crucial parameters.
The project's objectives include optimizing materials technology, designing optical memristors based on PCMs and 2D materials, exploring new configurations with PCM+MEMs, investigating memory functionality in VO2, and demonstrating neural response. The goal is to improve data processing speeds while reducing power consumption, ultimately leading to energy-efficient computing. Additionally, the project aims to assess the environmental impact of these advancements through empirical cost comparisons. The researcher envisions that this experience will empower them to become a high-quality academic researcher with a focus on materials photonics. Overall, the project addresses a critical need in the field of computing by developing materials technology for energy-efficient memory computing, aligning with the goals of the European Green Deal towards the pursuit of ultra-fast, low-energy data processing.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
17-11-2024
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