Flexible Ionic‐Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain‐Inspired Neuromorphic Computing

• Published in: Small (Weinheim an der Bergstrasse, Germany) Germany), 2017-08, Vol.13 (32), p.n/a
• Main Authors: John, Rohit Abraham, Ko, Jieun, Kulkarni, Mohit R., Tiwari, Naveen, Chien, Nguyen Anh, Ing, Ng Geok, Leong, Wei Lin, Mathews, Nripan
• Format: Article
• Language: English
• Subjects: Action Potentials; Biological computing; Brain; Brain - metabolism; Brain - physiology; Concurrent processing; Consolidation; excitatory postsynaptic current (EPSC); inhibitory postsynaptic currents (IPSC); Mechanical properties; Nanotechnology; neuromorphic; Neuromorphic computing; Neuronal Plasticity; Oxides - chemistry; paired pulse facilitation (PPF); Plastic properties; Semiconductor devices; spike‐duration‐dependent plasticity (SDDP); Substrates; Synapses; Transistors
• ISSN: 1613-6810; 1613-6829
• DOI: 10.1002/smll.201701193

Emulation of biological synapses is necessary for future brain‐inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic‐electronic hybrid oxide‐based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field‐effect mobility of ≈9 cm2 V−1 s−1 with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired‐pulse facilitation, excitatory and inhibitory postsynaptic currents, spike‐time‐dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next‐generation transparent neural circuits. Artificial synapses based on metal oxide transistors are envisioned as fundamental building blocks for future neuromorphic computational systems. Fully solution processable artificial synapses based on ionic‐electronic hybrid transistors on flexible polyimide substrates are demonstrated. Synaptic learning and forgetting rules like short‐term synaptic plasticity, short‐term memory to long‐term memory transition, dynamic filtering, and spatiotemporally correlated signal processing are presented.