Reviewed by Lexie CornerNov 26 2024
A recent discovery published in ACS Applied Materials & Interfaces describes the creation of an optoelectronic photopolymeric human synapse developed by researchers from the Tokyo University of Science. This synapse is dye-sensitized, solar cell-based, and features a time constant regulated by the intensity of the input light.
Artificial intelligence (AI) is making it easier to predict catastrophic events such as heart attacks, natural disasters, and pipeline failures. Modern technologies capable of processing data quickly are essential for this task. One promising solution is reservoir computing, designed specifically for processing time-series data with low power consumption. Physical reservoir computing (PRC) is the most widely used framework for this purpose.
PRC, enhanced with optoelectronic artificial synapses (junctions that allow nerve cells to transmit electrical or chemical signals), is expected to achieve recognition and real-time processing capabilities similar to the human visual system.
However, current self-powered optoelectronic synaptic devices used in PRC cannot handle time-series data spanning multiple timescales, such as those found in signals used for monitoring infrastructure, the natural environment, and health-related issues.
In order to process time-series input optical data with various time scales, it is essential to fabricate devices according to the desired time scale. Inspired by the afterimage phenomenon of the eye, we came up with a novel optoelectronic human synaptic device that can serve as a computational framework for power-saving edge AI optical sensors.
Takashi Ikuno, Associate Professor, Tokyo University of Science
In addition to using dyes based on squarylium derivatives, the solar cell-based device integrates optical input, AI computation, analog output, and power supply functions at the material level.
It exhibits synaptic characteristics such as paired-pulse facilitation and paired-pulse depression, demonstrating synaptic plasticity in response to light intensity. The researchers found that varying light intensity, regardless of input light pulse width, leads to strong computational performance in time-series data processing applications.
Additionally, this device identified human movements such as bending, jumping, running, and walking with over 90 % accuracy when used as the reservoir layer in PRC. It consumed only 1 % of the power used by traditional systems, significantly reducing corresponding carbon emissions.
Dr. Ikuno added, “We have demonstrated for the first time in the world that the developed device can operate with very low power consumption and yet identify human motion with a high accuracy rate.”
Notably, the proposed device opens a new avenue for developing edge AI sensors for various time scales, with potential applications in health monitoring, car cameras, and surveillance cameras.
“This invention can be used as a massively popular edge AI optical sensor that can be attached to any object or person and can impact the cost involved in power consumption, such as car-mounted cameras and car-mounted computers. This device can function as a sensor that can identify human movement with low power consumption and thus has the potential to contribute to the improvement of vehicle power consumption. Furthermore, it is expected to be used as a low power consumption optical sensor in stand-alone smartwatches and medical devices, significantly reducing their costs to be comparable or even lower than that of current medical devices,” Dr. Ikuno stated.
This unique solar cell-based device has the potential to accelerate the development of energy-efficient edge AI sensors for various applications.
Journal Reference:
Komatsu, H. et. al. (2024) Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing. ACS Applied Materials & Interfaces. doi.org/10.1021/acsami.4c11061