Here, rational design electrodes are fabricated by mixing MXene with an aqueous solution of chloroauric acid (HAuCl₄). In order to prevent MXene from self-restacking, the groups of -OH on the surface of Ti₃C₂T_x nanosheets underwent a one-step simultaneous self-reduction from AuCl₄-, generating spaces for rapid ion transit. Additionally, by using this procedure, MXene’s surface oxidation can be decreased while preserving its physio-chemical properties. The interlayered MX/Au NPs that have been obtained are combined into a conducting network structure that offers more active electrochemical sites and improved mass transfer at the electrode–electrolyte interface, both of which promote quick electron transfer during electrochemical reactions and excellent structural durability. The Ti₃C₂T_x-AuNPs film thus demonstrated a rate performance that was preferable to that of pure Ti₃C₂T_x film. According to the results of the characterization, the AuNPs effectively adorn the MXene nanosheets. Due to the renowned pseudocapacitance charge storage mechanism, MXene-based electrode materials also work well as supercapacitors in sulfuric acid, which is why MXene AuNPs electrodes have been tested in 3 M and 1 M H₂SO₄. The symmetric supercapacitors made of MXene and AuNPs have shown exceptional specific capacitance of 696.67 Fg⁻¹ at 5 mVs⁻¹ in 3 M H₂SO₄ electrolyte, and they can sustain 90% of their original capacitance for 5000 cycles. The highest energy and power density of this device, which operates within a 1.2 V potential window, are 138.4 Wh kg⁻¹ and 2076 W kg⁻¹, respectively. These findings offer a productive method for creating high-performance metal oxide-based symmetric capacitors and a straightforward, workable approach for improving MXene-based electrode designs, which can be applied to other electro-chemical systems that are ion transport-restricted, such as metal ion batteries and catalysis.

In this work, a novel GaN/AlN dual-Schottky junction varactor based on the recess electrode structure of a metal-2DEG-metal contact is prepared, and the capacitance and series resistance are significantly reduced due to the removal of the barrier between the metal and 2DEG. Moreover, the devices show strong nonlinear capacitive behavior and a large nonlinear capacitive dynamic range due to the use of dual-Schottky junctions, demonstrating significant advantages in the field of terahertz (THz) multiplier circuits over single-Schottky junctions. For the dual-Schottky varactor with an electrode width of $5~\mu $ m and an electrode distance of $2~\mu $ m, a 6.17 fF junction capacitance at zero bias ( $\mathrm {C}_{\mathrm {j0}}$ ) and a $17~\Omega $ series resistance ( $\mathrm {R}_{\mathrm {s}}$ ) are achieved after de-embedding the parasitic component, resulting in an extremely high cutoff frequency of 1.5 THz for GaN/AlN-based varactors. Additionally, we propose a new device model based on the dual-Schottky junction varactor by using neural network training (ANN with a long short-term memory (LSTM) architecture) for the first time. LSTM networks include input, forget, and output gates, which it to selectively retain essential information from prior data points, making it especially effective at capturing the complex, nonlinear relationships in the I-V and C-V characteristics of our varactor. The results show that the model precision for the I-V and C-V curves is very high (RMSE <0.5%) compared with test data. Hence, the proposed novel GaN/AlN dual-Schottky varactor and its device model have great application potential in the field of THz sources based on the multiplier technology route.

Modulating electrical characteristics of zinc oxide (ZnO) thin-film transistors (TFTs) through gate dielectric thickness is expected to compensate for short-channel effects (SCEs) for driving Micro-LEDs. Here, we first design the ZnO TFTs with thinner dielectric to relieve threshold voltage ( ${V}_{\text {TH}}{)}$ roll-off and subthreshold swing (SS) degradation as the channel length scales down to a few micrometers. The proposed compensation method was then demonstrated and verified by fabricating ZnO TFTs with various gate dielectric thicknesses. The ${V}_{\text {TH}}$ roll-off and SS degradation can be avoided, and ${I}_{\text {ON}} / {I}_{\text {OFF}}$ ratio can be improved by an order of magnitude, which is of particular interest for shorter channel ZnO TFTs. By solving the current density equation, it has been found that a higher electric field and a higher current density form near ZnO/Al2O3 interface when the gate dielectric thickness decreases. Moreover, the first-principle calculations reveal that localization of transmission eigenstates at the semiconductor–insulation interface with thinner dielectric layer provides a better conductive channel for TFTs. This work offers deep insights into compensating for the SCEs through the multiscale simulation and experimental verification.

This paper presents a generalized multi-granularity design of Millimeter-wave (mmWave) and terahertz (THz) on-chip antennas based on improved reinforcement learning (RL). The proposed framework takes the advantages of the modeling strategy, enhanced RL, and multi-granularity method, enabling high-degree-of-freedom on-chip antenna design with low-dimensional modeling parameters. Firstly, a parametric modeling strategy is introduced to achieve a pixelation-like design effect but circumventing high data quality and quantity requirements typically associated with conventional pixel-based methods. Secondly, the enhanced RL uses a Maximum Entropy Actor-Critic (MEAC) algorithm to improve convergence speed, robustness, and efficiency. Thirdly, field theory analysis is incorporated to decompose the optimization process into coarse and fine granularities, effectively minimizing redundant modeling in non-essential regions. This method is applied to D-band on-chip antenna design for the commercial1 65nm CMOS process and W-band for the commercial2 55nm CMOS process. The experimental results show that the D-band on-chip antenna achieves a relative bandwidth of 50.9% (100-177 GHz), a peak gain of 2.4 dBi, and a radiation efficiency of 62%, respectively, while the W-band achieves a relative bandwidth of 30.6% (69-95GHz), peak gain of 6.4dBi, and radiation efficiency of 72%. Compared with existing studies, the multi-granularity with enhanced reinforcement learning significantly improves bandwidth, gain, and radiation efficiency, with strong generalizability across different frequency bands and fabrication processes.

This paper presents a complete modeling flow for GaN Schottky barrier diodes (SBDs), encompassing parasitic parameter de-embedding, small-signal equivalent circuit extraction, and DC characteristic analysis. The open/short de-embedding method is adopted to extract the parasitic parameters of the Ground–Signal–Ground (GSG) pads. A bias-partitioning strategy is employed to extract the intrinsic small-signal parameters, and the depletion capacitance model is used to physically fit the C-V characteristics. The forward Direct Current (DC) conduction current is described by the thermionic emission model, while the reverse leakage is modeled using a piecewise approach: the Poole–Frenkel (PF) trap-assisted emission model is applied in the low-bias region, and a double-exponential decay empirical model is introduced in the high-bias region, achieving high-precision fitting over the full bias range (−100 V~3 V). More importantly, this paper identifies a significant discrepancy between the series resistance extracted from DC measurements and that from Radio Frequency (RF) measurements, and attributes it to the frequency dispersion effect induced by trap states. The study demonstrates that combining DC and high-frequency characterization not only enables the construction of an accurate modeling framework, but also reveals the trap-related physical mechanisms within the device.