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.

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

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.

Complementary metal–oxide–semiconductor (CMOS)-based neural architectures and memristive devices containing many artificial synapses are promising technologies that are being developed for pattern recognition and machine learning. However, the volatility and design complexity of traditional CMOS architectures, and the trade-off between the operating time and power consumption of conventional memristive devices, have tended to impede the path to achieve the interconnectivity/compactness and information density of the brain using either approach. Here, by developing a nanoscale deposit-only-metal-electrode-fabrication-based uniform-partial-state-transition-facilitated approach, we demonstrate a fast artificial synapse with a Rapid-operating-time, Intermediate-bias-range, Multiple-states, and Several-synaptic-functions (RIMS) synapse, implemented using deposit-only, nanopillar-based Ge₂Sb₂Te₅-type memristive devices. A previously unconsidered, fast, paired-pulse facilitation/depression using ∼50 ns spikes with an ∼1 µs inter-spike interval within an ∼1 V range and with a low-energy consumption of ∼1.8 pJ per paired-spike as well as a previously inaccessible multi-state, rapid long-term potentiation/depression with ∼15 distinct states using ∼50 ns spikes within a 0.7/1.4 V range was achieved. Fast spike-timing-dependent plasticity using ∼50 ns spikes with an ∼1 µs inter-spike interval within a 1.3 V range was also achieved. Electro-thermal simulations reveal a uniform-partial-state-transition-facilitated variation in conductance states. This artificial synapse, equipped with a nanoscale deposit-only-metal-electrode-fabrication-based uniform-partial-state-transition-facilitated framework, shows the potential for a substantial overall performance improvement in artificial-intelligence tasks.