An Organic–Inorganic Hybrid Memristor with Trap-Assisted Hopping for Synaptic Plasticity and Artificial Nociceptor

Neuromorphic hardware demands artificial synaptic devices that mimic biological synapses. Achieving multifunctional emulation of synaptic plasticity and nociceptive behavior in a single, highly stable, low-power device remains a major challenge. In this report, zinc oxide nanoparticles (ZnONP) are synthesized via a surfactant-assisted sol-gel method. These are incorporated into an organic small-molecule matrix of 6,13-bis(triisopropylsilylethynyl)pentacene (TP) to fabricate a solution-processed organic-inorganic hybrid memristor. The as-fabricated hybrid memristor mimicked artificial synaptic behavior at a low operating voltage (<±1 V) while demonstrating high stability with retention exceeding 105 s in ambient atmosphere. The TP/ZnONP-based memristor has illustrated both short-term and long-term synaptic plasticity, facilitating the exemplification of learning and memory functions of biological synapses. The memristor has been found to exhibit pain-perception-related nociceptive behavior, including protective responses to excessive stimulation and self-recovery, closely resembling that of biological nociceptors. The device exhibits neurapraxia-like responses with a recovery time of only 40 s. The experimentally obtained synaptic characteristics are further employed in a device-aware fully connected artificial neural network, yielding a handwritten digit recognition accuracy of 97.9%. Various investigations on the dynamics of charge transport have revealed trap-assisted hopping through transient localization, which is induced by shallow and deep traps. The integration of artificial nociception and pattern recognition in a highly stable low-power synaptic device demonstrates the potential of the hybrid memristor for multifunctional neuromorphic hardware.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-10-26
DOI
https://doi.org/10.1021/acsami.6c16763
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

An Organic–Inorganic Hybrid Memristor with Trap-Assisted Hopping for Synaptic Plasticity and Artificial Nociceptor

Ayash Kanto Mukherjee, Nikhitha Rajan
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

An Organic–Inorganic Hybrid Memristor with Trap-Assisted Hopping for Synaptic Plasticity and Artificial Nociceptor

Ayash Kanto Mukherjee, Nikhitha Rajan
article en

Abstract

Neuromorphic hardware demands artificial synaptic devices that mimic biological synapses. Achieving multifunctional emulation of synaptic plasticity and nociceptive behavior in a single, highly stable, low-power device remains a major challenge. In this report, zinc oxide nanoparticles (ZnONP) are synthesized via a surfactant-assisted sol-gel method. These are incorporated into an organic small-molecule matrix of 6,13-bis(triisopropylsilylethynyl)pentacene (TP) to fabricate a solution-processed organic-inorganic hybrid memristor. The as-fabricated hybrid memristor mimicked artificial synaptic behavior at a low operating voltage (<±1 V) while demonstrating high stability with retention exceeding 105 s in ambient atmosphere. The TP/ZnONP-based memristor has illustrated both short-term and long-term synaptic plasticity, facilitating the exemplification of learning and memory functions of biological synapses. The memristor has been found to exhibit pain-perception-related nociceptive behavior, including protective responses to excessive stimulation and self-recovery, closely resembling that of biological nociceptors. The device exhibits neurapraxia-like responses with a recovery time of only 40 s. The experimentally obtained synaptic characteristics are further employed in a device-aware fully connected artificial neural network, yielding a handwritten digit recognition accuracy of 97.9%. Various investigations on the dynamics of charge transport have revealed trap-assisted hopping through transient localization, which is induced by shallow and deep traps. The integration of artificial nociception and pattern recognition in a highly stable low-power synaptic device demonstrates the potential of the hybrid memristor for multifunctional neuromorphic hardware.

ACS Applied Materials & Interfaces
Indian Institute of Technology Patna (IN)
Indian Institute of Technology, Patna, Science and Engineering Research Board
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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