The nanowire gate-all-around structure with the ultimate channel electrostatic integrity exhibits the best immunity to short channel effects and improved scaling capability compared with other multigate structures. In this article, both the tunneling current and capacitance models are developed simultaneously for nanowire tunneling field-effect transistors (FETs). Based on the same surface potential model, the developed current model and capacitance model share the common parameters and therefore can be easily integrated as a complete model for circuit-level simulations. Moreover, there is no iterative process involved during the model derivation indicating the models would be efficient for circuit simulations. The proposed models are also implemented into a circuit simulator with SPICE net-list to simulate the inverter, NAND, and NOR gates. Correct circuit behaviors obtained validate the model compatibility with the SPICE platform and usefulness for the further investigation of nanowire-based tunnel FET (TFET) circuits.

In this paper, a novel transistor based on a hybrid conduction mechanism of band-to-band tunneling and drift-diffusion is proposed and investigated with the aid of TCAD tools. Besides the on and off states, the proposed device presents an additional intermediate state between the on and off states. Based on the tri-state behavior of the proposed TDFET (tunneling and drift-diffusion field-effect transistor), a ternary inverter is designed and its operation principle is studied in detail. It was found that this device achieves ternary logic with only two components, and its structure is simple. In addition, the influence of the supply voltage and the key device parameters are also investigated.

Rain streaks degrade the quality of optical images and seriously affect the effectiveness of subsequent vision-based algorithms. Although the applications of a convolutional neural network (CNN) and self-attention mechanism (SA) in single image deraining have shown great success, there are still unresolved issues regarding the deraining performance and the large computational load. The work in this paper fully coordinates and utilizes the advantages between CNN and SA and proposes a hybrid content-aware deraining network (CAD) to reduce complexity and generate high-quality results. Specifically, we construct the CADBlock, including the content-aware convolution and attention mixer module (CAMM) and the multi-scale double-gated feed-forward module (MDFM). In CAMM, the attention mechanism is used for intricate windows to generate abundant features and simple convolution is used for plain windows to reduce computational costs. In MDFM, multi-scale spatial features are double-gated fused to preserve local detail features and enhance image restoration capabilities. Furthermore, a four-token contextual attention module (FTCA) is introduced to explore the content information among neighbor keys to improve the representation ability. Both qualitative and quantitative validations on synthetic and real-world rain images demonstrate that the proposed CAD can achieve a competitive deraining performance.

Image degradation caused by rain streaks can seriously affect the accuracy of computer vision algorithms. The performance of the existing deraining algorithms still has room for improvement. In terms of the training dataset, many deraining methods only consider the rain layer. However, rain images are also affected by other factors, such as fog, light. In this work, we first synthesize a rain dataset based on standard optical model, and then a deraining network based on hybrid CNN and Transformer is proposed. Specially, a hybrid attention module is first introduced to generate atmospheric attention maps and transmission maps. Then, a multiscale integration attention module is used to produce rain streak maps. Finally, a refinement module is used to process preliminary deraining images generated by the three attention maps. Extensive experiments on both generated synthetic and real-world datasets have demonstrated the superiority and effectiveness of the proposed method.