Emulation of Synaptic Plasticity on Cobalt based Synaptic Transistor for Neuromorphic Computing

التفاصيل البيبلوغرافية
العنوان: Emulation of Synaptic Plasticity on Cobalt based Synaptic Transistor for Neuromorphic Computing
المؤلفون: Monalisha, P., Kumar, P. S. Anil, Wang, X. Renshaw, Piramanayagam, S. N.
سنة النشر: 2021
المجموعة: Computer Science
Condensed Matter
Physics (Other)
مصطلحات موضوعية: Condensed Matter - Materials Science, Computer Science - Emerging Technologies, Physics - Applied Physics
الوصف: Neuromorphic Computing (NC), which emulates neural activities of the human brain, is considered for low-power implementation of artificial intelligence. Towards realizing NC, fabrication, and investigations of hardware elements such as synaptic devices and neurons are essential. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using a wide variety of materials. However, studies on metallic channel based synaptic transistors remain vastly unexplored. Here, we have demonstrated a three-terminal cobalt-based synaptic transistor to emulate biological synapse. We realized gating controlled multilevel, nonvolatile conducting states in the proposed device. The device could successfully emulate essential synaptic functions demonstrating short-term and long-term plasticity. A transition from short-term memory to long-term memory has been realized by tuning gate pulse amplitude and duration. The crucial cognitive behavior viz., learning, forgetting, and relearning, has been emulated, showing resemblance to the human brain. Along with learning and memory, the device showed dynamic filtering behavior. These results provide an insight into the design of metallic channel based synaptic transistors for neuromorphic computing.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2109.09613
رقم الأكسشن: edsarx.2109.09613
قاعدة البيانات: arXiv