In this work, a TaOx-based Metal-Insulator-Metal (MIM) diode is demonstrated. This Cu/TaOx/W diode is fabricated with sputtering method without any high temperature process (~ 400oC), which shows promising compatabiltiy with CMOS back-end process. The fabricated diode exhibits abrupt switching process (~3mV/dec) with small switching voltage (~ 0.7V), high on/off current ratio more than 106, low leakage current (~ pA). Current is limited when the diode is switched to the on-state to avoid the hard breakdown caused by the thermal run-away. On/off current ratio can be further improved to 108 by the limited current increase at the precondition of ensuring the safty of the diode. This MIM diode can be easily integrated into high density PCM or RRAM cross-point arrays due to its simple fabrication process. Its nearly ideal diode behavior with ultra low on-resistance can also be used as an ESD protection device or as a rectifying element in circuit.

In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method’s significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types.

Hydrogels hold substantial promise for artificial skin in bionic robotics and biomedical devices. However, few hydrogels have a combination of properties comparable to natural human skin. Here, we report a skin-like, all-in-one hydrogel based on a supramolecular entangled network reinforced with hydrogen-bond–associated nanodomains. Within this architecture, hydrogen-bond–associated self-assembled nanodomains, polymer-chain entanglement, dynamic electrostatic interactions, and reversible metal-ligand coordination operate cooperatively to endow the network with mechanical robustness and dynamic adaptability. Consequently, the hydrogel reconciles the intrinsic trade-offs among mechanical properties, multiple functions, and multimodal perception. Its integrated performance compares favorably with that of other advanced hydrogels and even biological skin. To demonstrate its potential applications, we constructed an artificial skin using our hydrogel, which facilitates interaction with the surrounding environment, including monitoring respiration, detecting motion, and manipulating robotic grippers. These results pave the way for versatile hydrogels that replicate the unparalleled combinatory properties of natural skin more precisely for diverse intelligent applications.