Materials Strategies for Organic Neuromorphic Devices
| Autor: | Alberto Salleo, Armantas Melianas, Scott T. Keene, Aristide Gumyusenge |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Bioelectronics
Artificial neural network business.industry Computer science 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Resistive random-access memory symbols.namesake Neuromorphic engineering symbols General Materials Science Artificial intelligence 0210 nano-technology business Von Neumann architecture |
| Zdroj: | Annual Review of Materials Research. 51:47-71 |
| ISSN: | 1545-4118 1531-7331 |
| Popis: | Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics. |
| Databáze: | OpenAIRE |
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