As artificial intelligence and smart devices continue to evolve, machine vision is taking an increasingly pivotal role as a key enabler of modern technologies. Unfortunately, despite much progress, machine vision systems still face a major problem: processing the enormous amounts of visual data generated every second requires substantial power, storage, and computational resources. This limitation makes it difficult to deploy visual recognition capabilities in edge devices—such as smartphones, drones, or autonomous vehicles.
Interestingly, the human visual system offers a compelling alternative model. Unlike conventional machine vision systems that have to capture and process every detail, our eyes and brain selectively filter information, allowing for higher efficiency in visual processing while consuming minimal power. Neuromorphic computing, which mimics the structure and function of biological neural systems, has thus emerged as a promising approach to overcome existing hurdles in computer vision. However, two major challenges have persisted. The first is achieving color recognition comparable to human vision, whereas the second is eliminating the need for external power sources to minimize energy consumption.
Against this backdrop, a research team led by Associate Professor Takashi Ikuno from the School of Advanced Engineering, Department of Electronic Systems Engineering, Tokyo University of Science (TUS), Japan, has developed a groundbreaking solution. Their paper, published in Volume 15 of the journal Scientific Reports on May 12, 2025, introduces a self-powered artificial synapse capable of distinguishing colors with remarkable precision. The study was co-authored by Mr. Hiroaki Komatsu and Ms. Norika Hosoda, also from TUS.
The researchers created their device by integrating two different dye-sensitized solar cells, which respond differently to various wavelengths of light. Unlike conventional optoelectronic artificial synapses that require external power sources, the proposed synapse generates its electricity via solar energy conversion. This self-powering capability makes it particularly suitable for edge computing applications, where energy efficiency is crucial.
As evidenced through extensive experiments, the resulting system can distinguish between colors with a resolution of 10 nanometers across the visible spectrum—a level of discrimination approaching that of the human eye. Moreover, the device also exhibited bipolar responses, producing positive voltage under blue light and negative voltage under red light. This makes it possible to perform complex logic operations that would typically require multiple conventional devices. “The results show great potential for the application of this next-generation optoelectronic device, which enables high-resolution color discrimination and logical operations simultaneously, to low-power artificial intelligence (AI) systems with visual recognition,” notes Dr. Ikuno.
To demonstrate a real-world application, the team used their device in a physical reservoir computing framework to recognize different human movements recorded in red, green, and blue. The system achieved an impressive 82% accuracy when classifying 18 different combinations of colors and movements using just a single device, rather than the multiple photodiodes needed in conventional systems.
The implications of this research extend across multiple industries. In autonomous vehicles, these devices could enable more efficient recognition of traffic lights, road signs, and obstacles. In healthcare, they could power wearable devices that monitor vital signs like blood oxygen levels with minimal battery drain. For consumer electronics, this technology could lead to smartphones and augmented/virtual reality headsets with dramatically improved battery life while maintaining sophisticated visual recognition capabilities. “We believe this technology will contribute to the realization of low-power machine vision systems with color discrimination capabilities close to those of the human eye, with applications in optical sensors for self-driving cars, low-power biometric sensors for medical use, and portable recognition devices,” remarks Dr. Ikuno.
Overall, this work represents a significant step toward bringing the wonders of computer vision to edge devices, enabling our everyday devices to see the world more like we do.
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Reference
DOI: 10.1038/s41598-025-00693-0
About The Tokyo University of Science
Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan's development in science through inculcating the love for science in researchers, technicians, and educators.
With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society," TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today's most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.
Website: https://www.tus.ac.jp/en/mediarelations/
About Associate Professor Takashi Ikuno from Tokyo University of Science
Dr. Takashi Ikuno received his Ph.D. degree from Osaka University, whereupon he worked at the Lawrence Berkeley National Laboratory and UC Berkeley, USA, as a postdoctoral researcher and later at Toyota Central R&D Labs as a senior researcher. He currently holds the position of Associate Professor in the Department of Applied Electronics at Tokyo University of Science. His research interests include developing electronic devices with nanocarbon and low-dimensional nanomaterials. Prof. Ikuno has received numerous awards, including the JSAP Poster Award and the AIP Advances Editor’s Pick, both in 2024.
Funding information
This work was partially supported by the JST and the establishment of university fellowships for the creation of science and technology innovation (Grant Number JPMJFS2144). Additional support was provided by the JST SPRING (Grant Number JPMJSP2151).
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