Design and Application of Algae Light Sensing Circuit Based on Memristor

Visual perception systems are of great significance in AI, robotics, and IoT. Despite the satisfactory results achieved by existing photography and camera equipment, issues such as high cost, slow sensing speed, complex structures, and high power consumption remain due to the limitations of their circuit structures. Over hundreds of millions of years of evolution, algae have developed a photosensitive system with a simple structure but high sensitivity and fast response speed. In recent years, significant progress has also been made in the study of algal photosensitivity. Inspired by the photosensitive system of algae, this paper presents an algal photoreceptors model and designs an algal light sensing circuit (ALSC) based on memristors. The ALSC mimics the photosensitive neurons of green algae to perceive optical intensity and accomplishes the high-speed conversion of light signals to spike signals. ALSC has characteristics like a simple structure, high robustness, high nonlinearity, and low power consumption. To evaluate the performance of the proposed ALSC, we implemented it in the spiking camera optic perception module. The simulation and experimental results indicate that, in comparison with the traditional spiking cameras, the structure of the spiking camera based on ALSC is simpler, the conversion speed is increased by 200%, the power consumption is reduced by 50%, and it has the characteristics of high robustness and sensitivity. In addition, the circuit structure of visual neurons has high universality. It can be applied to various sensor devices, including touch, smell, temperature, and others, which is significant for the manufacturing of new sensor technologies.