Researchers at Oregon State University have developed a novel brain-inspired phototransistor that merges optical sensing and data storage within a single component, a breakthrough that could substantially lower the energy demands of artificial intelligence systems. The findings were recently published in Advanced Functional Materials. Traditional AI hardware separates sensing, memory, and signal processing into distinct modules, forcing data to travel between components and consuming excessive power. To overcome this inefficiency, the Oregon State University team engineered a phototransistor that processes information directly at the sensor level. The device combines an oxide semiconductor channel with an organic photosensitive layer. When exposed to light, the organic material generates electrical charges. Some of these charges become trapped, effectively recording optical signals even after illumination ceases. A defining innovation of the design is its programmable memory lifetime. By applying a precise electrical gate voltage, researchers can manipulate the position of the trapped charges relative to the semiconductor channel. Bringing the charges closer amplifies their electrical influence and extends memory retention, while moving them farther away accelerates signal decay. This mechanism closely mirrors how biological neural networks regulate synaptic strength and forgetting. The ability to dynamically tune how long visual or sensor data persists enables more efficient in-sensor computing architectures. According to Larry Cheng, professor of electrical engineering and computer science at Oregon State University, this tunable time window allows artificial intelligence systems to process dynamic information locally, eliminating the latency and power overhead associated with data transfer to centralized processors. The development marks a significant step toward practical neuromorphic computing, which seeks to replicate the parallel, low-power architecture of biological brains. By consolidating multiple hardware functions and introducing adaptive memory decay, the phototransistor lays groundwork for next-generation vision systems, autonomous sensors, and edge artificial intelligence applications that prioritize energy efficiency and rapid response. The research team includes contributors from both the Oregon State University College of Engineering and College of Science, reflecting a multidisciplinary approach to advancing hardware-level innovation. As computational demands for machine learning continue to escalate, hardware solutions that natively integrate perception and storage will be critical to sustaining scalable and sustainable artificial intelligence deployment.