Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In‐memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first‐order dynamics. Here, a second‐order memristor is experimentally demonstrated using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second‐order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL‐based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second‐order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high‐efficiency, compact footprint, and hardware‐encoded plasticity.

Normally-off AlGaN/GaN MIS-HEMTs with a high threshold voltage ( $\text{V}_{\text {th}}$ ) more than 2.5 V and a low on- resistance of $5.5\Omega \cdot $ mm have been achieved by an improved regrowth technique with in-situ SiN _x passivation. A thin-barrier heterojunction was used to decrease the two-dimensional electron gas (2DEG) underneath the gate, then regrown Al _0.2 Ga _0.8 N and in-situ SiN _x were applied to recover 2DEG at the access regions and reduce contact resistance. The O ₃ -based Al ₂ O ₃ and HfO ₂ were employed to cover the recessed-gate with low channel sheet resistances by atomic layer deposition. The other hybrid MIS-HEMT with in-situ SiN _x gate interlayer also enabled a normally-off operation with a $\text{V}_{\text {th}}$ hysteresis lower than 30 mV. The damage-free recessed-gate structures with in- situ SiN _x as passivation and gate dielectric contribute to reducing surface scattering and interface states, resulting in a high $\text{V}_{\text {th}}$ uniformity and channel mobility, low on- resistance and _th hysteresis in normally-off GaN-based MIS-HEMTs.

This paper provides a comprehensive review of recent progress in GaN-based gas sensors with a focus on their dynamic-related responses, mechanisms and applications, which are important for sensing performance, efficiency, and signal processing.

We present a novel p-gallium nitride (GaN) gate HEMT structure with reduced hole concentration near the Schottky interface by doping engineering in metal-organic chemical vapor deposition (MOCVD), which aims at lowering the electric field across the gate. By employing the additional unintentionally doped GaN (u-GaN) layer, the gate leakage current is suppressed and the gate breakdown voltage is boosted from 10.6 to 14.6 V with negligible influence on the threshold voltage and ON-resistance. A reduced Mg concentration in the u-GaN layer was confirmed by secondary-ion mass spectrometry. Time-dependent gate breakdown measurements reveal that the maximum gate drive voltage increases from 6.2 to 10.6 V for a ten-year lifetime with a 1% gate failure rate, which effectively expands the operating voltage margin of the p-GaN gate HEMTs without any other additional process step.

In this work, we studied the gate breakdown (BD) mechanisms of p-GaN gate AlGaN/GaN HEMTs by a novel multiple-gate-sweep-based method. For the first time, three different BD mechanisms were observed and identified separately in the same devices: the metal/ p-GaN junction BD, the p-GaN/AlGaN/GaN junction BD, and the passivation-related BD. This method is an effective method to determine the BD mechanisms. The different BD mechanisms were further confirmed by scanning electron microscopy (SEM). Finally, the temperature dependences of the three BD mechanisms were measured and compared. This analysis method was also employed in the devices with a different passivation material and showed its applicability.

With the slowdown of Moore’s law, many emerging electronic devices and computing architectures have been proposed to sustain the performance advancement of computing. Among them, the Ising machine is a non-von-Neumann solver that has received wide attention in recent years. It is capable of solving intractable combinatorial optimization (CO) problems, which are difficult to be solve using conventional digital computers. In fact, many CO problems can be mapped to finding the corresponding ground states of Ising model. At present, Ising machine prototypes based on different physical principles, such as emerging memristive oscillators, have been demonstrated, among which the Ising Hamiltonian solver based on the coupled oscillator network simultaneously holds the advantages of room-temperature operation, compact footprint, low power consumption, and fast speed to solution. This paper comprehensively surveys the recent developments in this important field, including the types of oscillators, the implementation principle of the Ising model, and the solver’s performance. Finally, methods to further improve the performance have also been suggested.