2D Materials-Based Static Random-Access Memory.

2D transition-metal dichalcogenide semiconductors, such as MoS2 and WSe2 , with adequate bandgaps are promising channel materials for ultrascaled logic transistors. This scalability study of 2D material (2DM)-based field-effect transistor (FET) and static random-access memory (SRAM) cells analyzing the impact of layer thickness reveals that the monolayer 2DM FET with superior electrostatics is beneficial for its ability to mitigate the read-write conflict in an SRAM cell at scaled technology nodes (1-2.1 nm). Moreover, the monolayer 2DM SRAM exhibits lower cell read access time and write time than the bilayer and trilayer 2DM SRAM cells at fixed leakage power. This simulation predicts that the optimization of 2DM SRAM designed with state-of-the-art contact resistance, mobility, and equivalent oxide thickness leads to excellent stability and operation speed at the 1-nm node. Applying the nanosheet (NS) gate-all-around (GAA) structure to 2DM further reduces cell read access time and write time and improves the area density of the SRAM cells, demonstrating a feasible scaling path beyond Si technology using 2DM NSFETs. In addition to the device design, the process challenges for 2DM NSFETs, including the cost-effective stacking of 2DM layers, formation of electrical contacts, suspended 2DM channels, and GAA structures, are also discussed.

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.