Self-Powered Device Mimics Brain Function, Enabling Real-Time Adaptation for Enhanced Learning and Responsive Interactions.听
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Taking cues from the human brain, Khalifa University researchers have developed a self-powered device mimicking important brain functions, such as adapting its learning based on past experiences, achieving an impressive accuracy rate of 93%.听
This breakthrough enables the development of neuromorphic systems, such as learning and memory processing, without relying on external power sources. By harnessing self-powered technology, these systems efficiently process and store information using minimal energy, making them ideal for applications in energy-constrained environments like biomedical devices and portable electronics.
The research paper titled 鈥楿nidirectional Neuromorphic Resistive Memory Integrated with Piezoelectric Nanogenerator for Self鈥怭owered Electronics鈥� was in Advanced Functional Materials, a top 2% journal in the field of material science. The team at Khalifa University includes Professor Baker Mohammad, Dr. Moh’d Rezeq, Associate Professor, Dr. Anas Alazzam, Associate Professor, Dr. Yawar Abbas, Research Scientist, and Dr. Muhammad Umair Khan, Post-Doctoral Research Fellow.
Operating at low voltage current, the device combines an energy-generating system with a novel memory structure that retains and forgets information, similar to how the brain processes information. A special memory component in the device works like a brain cell, and it is built using layers of materials, including Indium tin oxide, Zinc Oxide and gold. It also features a sensitive piezoelectric energy generator that converts pressing movement into electrical signals, like a neuron.
鈥淜halifa University鈥檚 System on Chip Lab has developed a self-powered device that mimics the brain鈥檚 synapses, enabling real-time data processing without external power 鈥� for smarter, energy-efficient technology.鈥�
鈥� Dr. Muhammad Umair Khan, Post Doctoral Fellow, Computer and Information Engineering, Khalifa University
In the brain, synapses, or connections between nerve cells that transmit signals, can increase in strength, enhancing learning and memory 鈥� a process known as synaptic plasticity, which is crucial for developing artificial neural networks. In their device, the researchers found that applying electrical pulses can affect the device’s ability to adapt to new information, allowing it to strengthen or weaken connections in response to varying stimuli, much like how the brain learns and adjusts.听
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Such key functions like strengthening and weakening connections and adapting over time occur without needing additional components, showcasing the device’s potential for self-powered sensing systems. As the device mimics brain functions independently, it also makes for an efficient, advanced, energy-saving technology. Additionally, the device forgets information more quickly when the input signals are weaker, indicating it can rapidly adapt to new inputs. This ability to forget faster allows it to respond faster in changing environments that require quick updates.听
Dr. Muhammad Umair Khan said: 鈥淭he study 星空传媒 the Khalifa University System on Chip Lab’s leading role in advancing neuromorphic computing. Our research team has developed a self-powered device with great potential for self-powered electronics applications. Its ability to detect mechanical stimuli and store data makes it invaluable for self-powered electronics. This self-powered memory device not only represents a significant leap in neuromorphic computing but also holds the potential to revolutionize how future technologies interact with the world, offering smarter, more energy-efficient solutions across a broad spectrum of applications.鈥澨�
Alisha Roy
Science Writer
