Imagine a camera that can see through thick fog and darkness as clearly as a human eye, but uses almost no battery. Instead of sending images to a power-hungry computer chip, it uses light-based processing to 'think' instantly. It's like replacing a slow, heavy engine with a lightweight, lightning-fast electric motor for how machines see the world.
Current computer vision systems are too power-hungry and fail in adverse weather like fog or extreme light, limiting their use in autonomous transport and wearables.
A wide-spectrum image sensor using non-toxic quantum dots and an optical neural network (ONN) chip for low-power processing.
If you are an autonomous vehicle manufacturer dealing with sensor failure in fog or bright sunlight — this project developed a wide-spectrum image sensor and optical neural network that provides 30x lower power consumption and better pedestrian detection in adverse weather.
If you are a headset developer dealing with bulky hardware and short battery life — this project developed a vision system with a form factor 1000x lower than current solutions, allowing for sleeker, lighter wearable devices.
If you are a robot producer dealing with high operational costs and energy drain — this project developed a system that is 1000x lower in cost and 30x lower in power consumption for real-time environment mapping.
The project objectives state that the resulting vision systems will be 1000x lower in cost compared to current solutions.
Yes, the project developed waferscale back-end-of-line (BEOL) integration processes that are CMOS-compatible, specifically to enable compatibility with high-volume markets.
Based on available project data, the project has resulted in new process flows, materials, and device architectures, but specific licensing terms are not provided.
The technology uses CMOS-compatible fabrication and includes an FPGA-based control system for real-time image processing.
The project runs until 2026-09-30, with full system-level demonstration expected in the next reporting period.
The consortium is heavily industry-weighted with a 62% industry ratio, comprising 5 industrial partners (including 2 SMEs) across 4 countries. This strong commercial presence, combined with 1 university and 2 research centers, suggests the project is driven by market application rather than pure academic curiosity.
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