Scientists Develop Miniature Sensor for Machine Vision

Scientists unveil a miniature photomemristor for machine vision systems.

An international team of scientists has unveiled a miniature photomemristor that mimics the adaptation of human vision to bright and dim light. The innovation could prove beneficial for machine vision systems in robots, drones, and cameras, according to a study in Nature Communications.

The device addresses the issue faced by machine vision systems, which lose accuracy during sudden changes in brightness. This is critical for drones and robots, which need to simultaneously distinguish objects in dark areas and bright light sources like oncoming car headlights.

The development is part of neuromorphic machine vision—a field where sensors not only capture images but also perform part of the signal processing. This approach is expected to reduce the load on computing systems and speed up reactions to changes in the frame.

Event-based cameras tackle a similar challenge. They do not capture each frame entirely but instead record changes in brightness at individual pixels, offering low latency, a high dynamic range, and reduced data volume. However, such systems require specialized algorithms and currently have their limitations.

The prototype is based on a miniature light-sensitive element approximately 0.5 mm in size. The key component of the system is a photomemristor made from TiO2 and PEDOT:PSS. Its operation relies on the materials’ reaction to humidity: under low light, the structure absorbs more water, increasing conductivity and photosensitivity. In bright light, moisture dissipates, reducing sensitivity.

In a demonstration setup, researchers used an array of 4 × 4 photomemristors and an artificial neural network. The system recognized letter patterns against backgrounds with varying brightness levels. According to the article, the accuracy was 91.3% under mixed lighting conditions, and the recognition process took 7.5 seconds.

Back in May, scientists introduced Qumus—an autonomous AI system for experiments with quantum materials.