Artificial vision technologies are driving innovation in fields like self-driving cars and security systems, but their high energy consumption and environmental impact are raising concerns. To address these challenges, an international team of researchers, led by the University of Glasgow, has developed a groundbreaking approach: a more sustainable artificial vision system inspired by the human brain. This innovative device, called the Electrolyte-Gated Organic Field-Effect Transistor (EGOFET), promises to reduce both energy use and electronic waste, offering a greener alternative for next-generation technologies.
Traditional artificial vision systems rely heavily on silicon-based technology, which consumes substantial power and generates significant electronic waste. The new EGOFET device, however, is designed to be energy-efficient and environmentally friendly. By mimicking the way the human brain processes visual data, this device is capable of sensing light, processing information, and even storing memories—all within a compact unit.
“This device’s ultra-low power consumption and use of sustainable materials could pave the way for eco-friendly, scalable artificial vision systems in the years to come,” explained Theodoros Serghiou from the University of Glasgow’s James Watt School of Engineering.
One of the key innovations of the EGOFET is its ability to retain information even after the power is turned off—a feature known as non-volatility. This makes it particularly promising for real-time applications like self-flying drones and advanced security systems, where speed and memory retention are crucial.
Unlike conventional systems that rely on separate processing and memory units, which can lead to delays, the EGOFET processes and stores data simultaneously, much like the synapses in the human brain. This unique structure allows for faster and more efficient processing of visual information, solving the bottleneck issues that occur in traditional systems.
The EGOFET is built using a combination of eco-friendly materials, including a glass base, gold electrodes, a light-sensitive organic layer made of perylene, and—surprisingly—honey, which acts as the device’s electrolyte. These biodegradable and recyclable materials help reduce the environmental footprint of artificial vision technologies.
The device operates by generating electrical signals, or “spikes,” that vary according to light’s color and brightness. These spikes program memory states, allowing the system to “remember” information even after it is powered off. The system also emulates essential synaptic behaviors, such as short-term and long-term plasticity, with high fidelity, providing a more advanced form of artificial vision.
“Our device can emulate key synaptic behaviors like short-term and long-term plasticity and spike-time dependent plasticity with high precision,” said Jeff Kettle, the corresponding author of the study. “Our next step is to scale this prototype into arrays, enhancing its image recognition capabilities.”
Beyond its impressive functionality, the EGOFET’s end-of-life sustainability is a significant advantage. The glass and gold components of the device can be recycled, while the organic materials are designed to biodegrade, reducing e-waste in the long run.
“We aim to scale this single-device prototype into arrays for enhanced image recognition capabilities, while ensuring the system remains environmentally friendly at the end of its lifecycle,” Kettle added.
With its ultra-low power requirements and sustainable materials, the EGOFET could be the key to developing more eco-friendly, scalable artificial vision systems for the future—helping to minimize the environmental impact of cutting-edge technologies while advancing their capabilities.
By Impact Lab
