Low-Dimensional Ferroelectrics Nanomaterials and Devices for Brain-Inspired Neuromorphic Computing Applications

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanoelectronics, Nanosensors and Devices".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 671

Editor


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Guest Editor
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
Interests: design of new photoelectronic materials devices; integration application of neuromorphic systems

Special Issue Information

Dear Colleagues,

Low-dimensional ferroelectric materials have attracted growing attention due to their unique polarization behavior, enhanced electrostatic control, and strong potential for nanoscale device integration. The discovery of stable ferroelectricity in two-dimensional, quasi-one-dimensional, and zero-dimensional systems has opened new opportunities for advanced functional devices beyond the limitations of conventional bulk ferroelectrics.

At the same time, neuromorphic devices inspired by biological neural systems have emerged as a promising approach for achieving energy-efficient and parallel information processing. Low-dimensional ferroelectrics, with their intrinsic nonvolatility and tunable polarization, offer an ideal platform for realizing artificial synapses and neurons.

This Special Issue aims to present recent advances in low-dimensional ferroelectrics and their applications in neuromorphic devices, covering material design, ferroelectric mechanisms, device architectures, and brain-inspired computing functionalities.

Topics of Interest

1. Two-dimensional, quasi-one-dimensional, and zero-dimensional ferroelectric materials

2. Ferroelectric switching mechanisms and domain dynamics at reduced dimensionality

3. Ferroelectric memristors and synaptic devices

4. Artificial neurons and brain-inspired device architectures

5. Optoelectronic and photo-ferroelectric neuromorphic devices

6. Device modeling, simulation, and neuromorphic computing applications

7. Reliability, endurance, and scalability of ferroelectric neuromorphic devices

Dr. Hong Wang
Guest Editor

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Keywords

  • low-dimensional materials
  • optical perception
  • ferroelectric
  • in-memory computing
  • RRAM
  • memristors

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Published Papers (1 paper)

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Research

12 pages, 9413 KB  
Communication
Photosensing PUF from an Intrinsically Random SnTe Memristor for Image Encryption and Recognition
by Wendi Xu, Jia Zhang, Junjie Xie, Tianzhu Xu, Jia Wu and Hong Wang
Nanomaterials 2026, 16(12), 715; https://doi.org/10.3390/nano16120715 - 10 Jun 2026
Viewed by 434
Abstract
Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor [...] Read more.
Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In2O3:SnO2/SnTe/Nb:SrTiO3 structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation. Full article
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