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•Charge trapping memory devices based on HfAlO thin film were deposited.•The memory devices based on HfAlO thin film had good charge trapping capability.•Intensive mixing of two ...high-k materials can improve the charge trapping capability.•The RTA-treated sample exhibited good program/erase and endurance performances.•About 21% of the trapped charges were remained after 10-years retention time.
HfAlO composite oxide was prepared as the charge trapping layer to improve the performance of our memory devices. The results showed that the device with HfAlO charge trapping layer had excellent charge trapping capability. The RTA treatment can make HfO2 and Al2O3 mixing more uniformly, and enlarge the inter-diffusion of the two materials, leading to the enhancement of the charge trapping capability. Meanwhile, the sample with RTA treatment exhibited fast program/erase speed, good retention and endurance performance, indicating that the memory device with high-k composite oxide charge trapping layer could be a promising candidate for future memory applications.
Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Translation (SLT), as a ...possible way for bridging communication gap between the deaf and the hearing people, has attracted widespread academic attention. We found that the current mainstream end-to-end neural SLT models, which tries to learning language knowledge in a weakly supervised manner, could not mine enough semantic information under the condition of low data resources. Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models. Concretely, we propose a novel neural SLT model with multi-modal feature fusion based on the dynamic graph, in which the cross-modal information, i.e. text and video, is first assembled as a dynamic graph according to their correlation, and then the graph is processed by a multi-modal graph encoder to generate the multi-modal embeddings for further usage in the subsequent neural translation models. To the best of our knowledge, we are the first to introduce graph neural networks, for fusing multi-modal information, into neural sign language translation models. Moreover, we conducted experiments on a publicly available popular SLT dataset RWTH-PHOENIX-Weather-2014T. and the quantitative experiments show that our method can improve the model.
The trusted execution environment (TEE) can prevent malicious programs from accessing the sensitive memory of application software, thereby ensuring the security of terminal devices in the process of ...collecting and transmitting data in the Internet of Things (IoT). The RISC-V architecture provides hardware support for the construction of a TEE, however, terminal devices based on RISC-V CPU face security challenges. Based on the mechanism of RISC-V hardware extension, this paper constructs a lightweight TEE scheme, which makes the trusted operating system kernel and embedded real-time operating system kernel run in the Machine (M) mode of higher CPU privilege in an isolated manner, and the application program runs in the User (U) mode, so the security operation of the terminal device in the IoT is ensured. In addition, an interrupt handling process for this scheme is also designed. This solution can be used as a reference design for a lightweight TEE for embedded systems whose CPU is based on RISC-V but only have M/U modes.
