Xinhui Chen

Physical unclonable functions (PUFs) have attracted significant attention for high-security applications, particularly in the Internet of Things (IoT). While resistive random access memory (RRAM)-based PUF technology offers excellent energy efficiency and integration density, its reliability remains a major challenge. This work proposes a highly reliable RRAM-based PUF that utilizes the 4T2R cross-coupled PUF cell to extract the response. Additionally, an automatic write-back technique is introduced to enhance the initial resistance mismatch in RRAM, thereby improving the PUF reliability. Experimental results demonstrate that the proposed PUF achieves outstanding performance, with near-ideal uniqueness (50.02%), uniformity (50.11%), and robust randomness (passing NIST 800-22 tests). The design exhibits robust reliability across a wide operating range (from $- 40~^{\circ }$ C to $120~^{\circ }$ C and 0.7 to 1.4 V), achieving a bit error rate (BER) of $\lt 4.89\times 10^{-7}$ . Furthermore, the energy consumption per bit is optimized to 4.8 fJ at 1.1 V, $25~^{\circ }$ C, showing its suitability for energy-constrained applications.

With the rapid development of media, the role of video quality assessment (VQA) is becoming increasingly significant. VQA has applications in many domains. For example, in the field of remote medical diagnosis, it can enhance the quality of video communication between doctors and patients. Besides, in sports broadcasting, it can improve video clarity. Within VQA, the human visual system (HVS) is a crucial component that should be taken into consideration. Considering that attention is guided by goal‐driven and top‐down factors, such as anticipated locations or some attractive frames within the video, we propose a blind VQA algorithm based on spatial‐temporal attention model. Specifically, we first use two pretrained convolutional networks to extract low‐level static‐dynamic fusion features. Then, a spatial attention‐guided model is established to get more representative features of frame‐level quality perception. Next, through a temporal attention‐guided model, the video‐level features are obtained. Finally, the features are fed into a regression model to calculate the final video quality score. The experiments conducted on seven VQA databases reach the state‐of‐the‐art performance, demonstrating the effectiveness of our proposed method.

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO₄)]₂/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor. The selection of resistive switching layer materials is extremely important for fabricating of high performance memristors. As an organic-inorganic hybrid material, metal-organic frameworks (MOFs) have the advantages of both inorganic and organic materials, which makes the memristors using it as a resistive switching layer show the characteristics of fast erasing speed, outstanding cycling stability, conspicuous mechanical flexibility, good biocompatibility, etc. Herein, the recent advances of MOFs-based memristors in materials, devices, and applications are summarized, especially the potential applications of MOFs-based memristors in data storage and neuromorphic computing. There also are discussions and analyses of the challenges of the current research to provide valuable insights for the development of MOFs-based memristors.

Reservoir Computing (RC) is a network architecture inspired by biological neural systems that maps time-dimensional input features to a high-dimensional space for computation. The key to hardware implementation of the RC system is whether sufficient reservoir states can be generated. In this paper, a laboratory-prepared zinc oxide (ZnO) memristor is reported and modeled. The device is found to have nonlinear dynamic responses and characteristics of simulating neurosynaptic long-term potentiation (LTP) and long-term depression (LTD). Based on this, a novel two-level RC structure based on the ZnO memristor is proposed. Novel synaptic encoding is used to maintain stress activity based on the characteristics of after-discharge and proneness to fatigue during synaptic transmission. This greatly alleviates the limitations of the self-attenuating characteristic reservoir of the duration and interval of the input signal. This makes the reservoir, in combination with a fully connected neural network, an ideal system for time series classification. The experimental results show that the recognition rate for the complete MNIST dataset is 95.08% when 35 neurons are present as hidden layers while achieving low training consumption.