Optoelectronic Neuromorphic AI Device

Illustration depicts a new phototransistor that integrates light sensing, memory and signal processing.
Image Credit: Courtesy of Oregon State University

Scientific Frontline: Extended "At a Glance" Summary: Programmable Optoelectronic Neuromorphic Device

The Core Concept: Researchers have developed a novel light-sensitive phototransistor that integrates sensing, memory, and signal processing into a single unit. Inspired by the human brain, the device uniquely controls how digital memories strengthen or fade over time.

Key Distinction/Mechanism: Unlike conventional AI hardware that separates sensing and memory components, this device processes information directly at the sensor level. It uses trapped electrical charges from absorbed light as memory and applies an electrical gate voltage to move these charges relative to the transistor channel, actively tuning memory lifetime and decay.

Major Frameworks/Components: 

• Oxide Semiconductor: Functions as the transistor channel to carry electrical current.
• Organic Photosensitive Material: Absorbs light, generates electrical charges, and traps them to form a memory of past optical signals.
• Tunable Charge Positioning: An applied electrical signal adjusts the physical proximity of trapped charges to the microscopic pathway, dictating the persistence or rapid decay of the memory.

Branch of Science: Neuromorphic Engineering, Optoelectronics, Materials Science, and Computer Science.

Future Application: Development of highly efficient artificial vision systems, advanced sensor-based AI technologies, and streamlined in-sensor computing networks.

Why It Matters: By eliminating the need to move information between separate processing and memory components, this technology promises to make artificial intelligence systems significantly faster and vastly more energy-efficient.

Inspired by the human brain, Oregon State University researchers have developed a new light-sensitive device that combines sensing and memory while controlling how digital memories strengthen or fade over time.

Technology that functions more like the human brain could enable artificial intelligence systems to work faster while consuming less electricity, said project leader Larry Cheng of the OSU College of Engineering.

The new device integrates light sensing, memory, and signal processing in a single phototransistor. Current AI hardware, Cheng explained, typically spreads these functions among different components; this requires information to move between them, which increases energy demands and reduces efficiency.

“Our optoelectronic device introduces a new hardware capability that may enable more efficient processing of information directly at the sensor level,” said Cheng, professor of electrical engineering and computer science. “Unlike conventional memory, which is designed to preserve information, our device can electronically control how memories strengthen or decay.”

In the new device, light creates stored electrical charges that act as memory. Similar to how chemical signals in the brain regulate memory strength and forgetting, a small electrical signal adjusts the influence of those stored charges, allowing memories to persist longer or fade more quickly—an important stepping stone toward neuromorphic computing systems, Cheng said.

Scientists are exploring neuromorphic computing, which is modeled after the structure and function of biological neural systems, and in-sensor computing for their potential to process dynamic information more efficiently.

“This light-sensitive memory with a programmable memory lifetime creates a tunable time window for processing visual and other sensor signals directly where they are detected, a capability that could enable more efficient vision systems and other sensor-based AI technologies,” Cheng said.

The device works by melding two different materials that perform distinct functions. An oxide semiconductor serves as the transistor channel that carries electrical current, while an organic photosensitive material on top absorbs light and generates electrical charges.

Some of those charges become trapped within the photosensitive layer. These trapped charges continue to influence the current flowing through the oxide semiconductor even after the light is removed, allowing the device to retain a memory of past optical signals.

“What makes this work unique is that the stored charges are not fixed in place,” Cheng said.

Applying an electrical gate voltage changes the position of the trapped charges relative to the transistor channel. Moving the charges closer to the transistor channel—the microscopic pathway through which electrons flow—strengthens their electrical influence and prolongs the memory effect, he said. Moving them farther away weakens that influence and speeds the loss of stored charges, causing the memory to fade more quickly.

Funding: The National Science Foundation supported the research.

Published in journal: Advanced Functional Materials

Title: Neuromodulator-Inspired Gate-Tunable Tetracene–Metal Oxide Phototransistor for Adaptive Optoelectronic Memory and Neuromorphic Computing

Authors: Ahasan Ullah, Roshell Lamug, Tasnim Sarker, Xueqiao Zhang, Andrew Ensinger, Lizhong Chen, Oksana Ostroverkhova, and Li-Jing Cheng

Source/Credit: Oregon State University | Steve Lundeberg

Edited by: Scientific Frontline

Reference Number: eng061726_01

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