Ferroelectric scandium-doped aluminum nitride (Al_1-xSc_xN) is of considerable research interest because of its superior ferroelectricity. Studies indicate that Al_1-xSc_xN may suffer from a high leakage current, which can hinder further thickness scaling and long-term reliability. In this work, we systematically investigate the origin of the leakage current in Al_0.7Sc_0.3N films via experiments and theoretical calculations. The results reveal that the leakage may originate from the nitrogen vacancies with positively charged states and fits well with the trap-assisted Poole-Frenkel (P-F) emission. Moreover, we examine the cycling behavior of ferroelectric Al_0.7Sc_0.3N-based FeRAM devices. We observe that the leakage current substantially increases when the device undergoes bipolar cycling with a pulse amplitude larger than the coercive electric field. Our analysis shows that the increased leakage current in bipolar cycling is caused by the monotonously reduced trap energy level by monitoring the direct current (DC) leakage under different temperatures and the P-F emission fitting.

Convolution is one of the key operations in signal processing and machine learning applications. In this paper, we propose a novel convolution computing paradigm based on the NOR Flash array (NFA) that is capable of executing the 2-D convolution computing in one clock cycle. In order to demonstrate the feasibility and efficiency of the proposed convolution computing paradigm, the feature extraction task on an image with the size of 20x 20 is executed using the NFA structure. We also prove the NOR Flash-driven convolution computing is capable of processing the image with a larger size. This paper presents a new approach to realize convolution computing with high speed and energy efficiency for the signal processing and convolution neural network.

Energy-efficient compact alternatives to fully digital computing strategies could be achieved by implementations of artificial neural networks (ANNs) that borrow analog techniques. In-memory computing based on crossbar device architectures with memristive materials systems that execute, in an analog way, multiply-and-accumulate operations prevalent in ANN is a notable example. Ferroelectric (FE) materials are promising candidates for achieving ANN thanks to their excellent down-scalability, improved electrical control, and high energy efficiency. However, it remains challenging to develop a crossbar device architecture using FE materials. The difficulty stems from decreasing the leakage current of FE hardware and, simultaneously, reducing the film thickness for achieving compact systems. Here, we have performed density-functional-theory calculations to investigate the electronic, energy-based, and structural signatures of wurtzite FE material Al_0.75Sc_0.25N with a nitrogen vacancy (V_N) in different charge states. We find that V_N can introduce two defect states, viz., the singlet state above the valence band maximum (VBM) and a triplet state below the conduction band minimum in wurtzite AlScN models. The calculations reveal that the group of transition levels E^3+/2+/E^2+/1+ with small formation energies occur at ∼0.78/1.03 eV above the VBM in the wurtzite AlScN with a relaxed configuration, which may shift by a large degree to lower energy levels if atoms surrounding the defect are not fully relaxed. Theoretical studies elucidate the vacancy-enhanced increase in the leakage current utilizing large AlScN supercells. These findings render atomistic insights that can provide a path forward for the design of next-generation portable low-power electronic systems.

A physics-based compact model for retention behaviors of conductive-bridge random access memory (CBRAM) is developed by modeling: 1) the material-dependent metal atom transport from the conductive filament into the electrolyte; and 2) the conduction percolation controlled by the atom concentration. Considering the material properties of the metal and electrolyte, this compact model can well reproduce the retention behaviors of the CBRAM in both low resistance state and high resistance state (HRS) for various material stacks under various operating temperatures. Two types of HRS resistance shift can be reproduced by accurately modeling the percolation paths. The compact model can enable the reliability projection of cells in a crossbar array.

This study presents an investigation into the stress effect on the leakage current in ferroelectric Al0.7Sc0.3N films by experiments and density functional theory (DFT) computations. The experiments are based on 8-in. 100 nm Al0.7Sc0.3N films obtained from pulsed DC co-sputter deposition technology, which exhibit non-uniform compressive in-plane stress across the wafers and similar distributions of leakage current, suggesting close dependence between each other. DFT computations revealed that stress affects leakage current in two ways: the level of traps introduced by nitrogen vacancy and the formation energy of nitrogen vacancy in Al0.7Sc0.3N. By considering both factors, the leakage current of Al0.7Sc0.3N films increases with larger compressive in-plane stress, as observed in the experimental results. Additionally, the DFT calculation results indicated that the leakage current is more sensitive to compressive stress compared to the tensile, and the minimum leakage current can be obtained with neutral in-plane stress. These findings provide a guideline for stress engineering to optimize the AlScN-based ferroelectric devices.