Incorporation of various functions of a biological nervous system into electronic devices is an intriguing challenge in the realization of a human-like recognition and response system. Emerging artificial synaptic devices capable of processing electronic signals through neuromorphic functions operate such biomimetic systems similarly to biological nervous systems. Here, an oxygen-sensitive artificial synaptic device that simultaneously detects oxygen concentration and generates a synaptic signal is demonstrated. The device successfully achieves an interconversion between the excitatory and inhibitory modes of the synaptic current at various oxygen concentrations by virtue of an oxygen-sensitive trilayered organic double heterojunction. The oxygen-induced traps in the organic layer modulate the majority charge carrier from holes to electrons, and this modulation induces an interconversion between the excitatory and inhibitory modes according to the environmental oxygen condition. Finally, the proposed synaptic device is applied to the realization of a negative feedback system for regulation of oxygen homeostasis, which mimics the human autonomic nervous system. The oxygen-sensitive synaptic device proposed in this study is expected to open up new possibilities for the development of a biomimetic neural system that can respond appropriately to various environmental changes.
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http://dx.doi.org/10.1002/adma.202002653 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
School of Materials and Energy, Lanzhou University (LZU), Lanzhou 730000, China.
Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping.
View Article and Find Full Text PDFNat Nanotechnol
January 2025
State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Interfacial ferroelectricity emerges in non-centrosymmetric heterostructures consisting of non-polar van der Waals (vdW) layers. Ferroelectricity with concomitant Coulomb screening can switch topological currents or superconductivity and simulate synaptic response. So far, it has only been realized in bilayer graphene moiré superlattices, posing stringent requirements to constituent materials and twist angles.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Information Science and Technology, Fudan University, Shanghai 200433, China.
To date, various kinds of memristors have been proposed as artificial neurons and synapses for neuromorphic computing to overcome the so-called von Neumann bottleneck in conventional computing architectures. However, related working principles are mostly ascribed to randomly distributed conductive filaments or traps, which usually lead to high stochasticity and poor uniformity. In this work, a heterostructure with a two-dimensional WS monolayer and a ferroelectric PZT film were demonstrated for memristors and artificial synapses, triggered by in-plane ferroelectric polarization.
View Article and Find Full Text PDFMicrosyst Nanoeng
January 2025
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Materials Science and Engineering, Gyeongsang National University, Jinju, Gyeongsangnam-do 52828, Republic of Korea.
Advances in the semiconductor industry have been limited owing to the constraints imposed by silicon-based CMOS technology; hence, innovative device design approaches are necessary. This study focuses on "more than Moore" approaches, specifically in neuromorphic computing. Although MoS devices have attracted attention as neuromorphic computing candidates, their performances have been limited due to environment-induced perturbations to carrier dynamics and the formation of defect states.
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