Abstract Herein, the significant impact of the spin‐coated Cr2O3 interface layer on the electrical properties and performance characteristics of Au/undoped‐InP (Au/InP) Schottky diodes (SD) is reported. The material characterization of spin‐coated Cr2O3 films using a wide variety of analytical techniques, namely, atomic force microscopy, field emission scanning electron microscope, X‐ray diffraction, Fourier transform infrared spectroscopy, and Raman spectroscopy, indicate the formation of hexagonal phase, nanocrystalline, and stoichiometric Cr2O3 on InP. Optical absorption measurements reveal a bandgap of ≈3.5 eV. In‐depth analyses and detailed measurements of current‐voltage (I–V) and capacitance‐voltage (C‐V) employed to assess the interface characteristics and electrical performance of the Au/InP (SD) versus Au/Cr2O3/InP (MIS) devices. Compared to SD, MIS revealed superior rectifying properties. Indicating that the Cr2O3 interface layer significantly influences the barrier height (ΦBH) of SD, the estimated ΦBH (0.64 eV (I–V)/0.86 eV (C‐V)) is higher than that of SD (0.57 eV (I–V)/0.67 eV (C‐V)). In addition, Cheungs and Nordes' methods are used to obtain the ΦBH, ideality factor (n), and series resistance (RS). The equivalent ΦBH values obtained from current–voltage, Cheungs, and Nordes methods demonstrate stability and dependability in addition to validating their superior characteristics of MIS devices. The interface state density (NSS) for MIS is lower than the SD's, indicating that the effectiveness of Cr2O3 layer significantly reduces NSS. Analyses to probe the mechanism demonstrate that, in SD and MIS, the Schottky emission controls the higher bias area, while the Poole‐Frenkel emission dominates the reverse conduction mechanism at the lower bias region. The present work convincingly demonstrates the potential application of the Cr2O3 interfacial layer in delivering the enhanced performance and contributes to the progression of electrical devices for emerging electronics and energy‐related applications.

Silver Nanowire Networks In article number 2400744, Joondong Kim, Dong-Wook Kim, and co-workers show that the integration of Ag nanowires with a MoS2/ZnO heterojunction boosts the surface photovoltage fourfold, reaching 200 mV under visible light, highlighting significantly enhanced interfacial photocarrier transfer driven by the synergistic optical and electrical benefits of the Ag nanowire networks.

Abstract Binary oxide ferroelectrics like doped HfO2, compatible with complementary metal‐oxide‐semiconductor (CMOS) platforms, have gained significant interest for energy efficient, scalable, high‐performance non‐volatile memory and neuromorphic technologies. However, there is no single model for doped hafnia systems that can explain physical properties of the system while being circuit simulation compatible and computationally efficient. It is presented physics‐based compact modelling based on the Jiles‐Atherton equations to reproduce experimentally measured polarization switching in ferroelectric thin film capacitors under different electric field and temperature conditions. Additionally, device‐to‐device variation effect on the model parameters is presented, which will enable large‐scale integration of the FE components to complex functional circuits. Due to increasing interest in cryogenic electronics for quantum computing and space technologies, effect of temperatures on polarization switching is investigated down to 4 K. It is shown the model can reproduced the experimental polarization‐voltage relation of Hafnium Zirconium Oxide capacitors with nearly 100% accuracy, for different electric fields and temperatures down to 4 K, including analog switching. It is find cooling the devices below 100 K increases polarization update linearity and symmetry significantly. This results represent an important advancement toward application of ferroelectric capacitors for large‐scale memory and neuromorphic circuits operating down to deep cryogenic temperatures.

Abstract Understanding the microscopic mechanism of the irradiation damage in silicon carbide (SiC) is of great importance for improving the irradiation resistance and the ion implantation processes of SiC‐based devices. Currently, the atomic‐scale simulations of the cascade collisions caused by irradiation in SiC are bottlenecked by the low accuracy of molecular dynamics (MD) with classical interatomic potentials and the low efficiency of ab initio MD (AIMD). In this study, a neural network potential (NNP) is constructed for the simulations of irradiation damage in 4H‐SiC using the stochastic surface walking (SSW) for the potential energy surface (PES) exploration. This potential is not only able to provide accurate structural and elastic properties, but also capable of predicting the defect properties and threshold displacement energies (TDEs) that well agree with the first‐principles results. More importantly, using this NNP, the directional dependence of the TDEs can be determined based on a set of high throughput calculations, and the minimal TDEs and the corresponding collision directions for Si and C can be predicted, which are in good agreement with the experimental results. This potential provides an efficient and accurate tool to accurately simulate the cascade collisions and gain fundamental understanding of the irradiation damage mechanisms of 4H‐SiC.

Abstract Spintronic devices based on DWss and skyrmions have shown significant potential for applications in energy‐efficient data storage and beyond CMOS computing architectures. Based on the ferromagnetic multilayer spintronic devices, a magnetic field‐gated and current‐controlled spintronic Leaky Integrate‐and‐Fire (LIF) neuron with memtransistor properties is showcased. The memtransistor property allows for tuning firing characteristics through external magnetic fields and current pulses. A LIF neuron model is developed based on measured characteristics to integrate the device into system‐level Spiking Neural Networks (SNNs). The scalability of the neuron device is confirmed with the micromagnetic simulations in a domain‐wall magnetic tunnel junction device. When integrated into SNN and convolutional SNN frameworks, the device achieves classification precision above 96%. The study highlights the device's potential as a neuron in hardware SNN architecture‐based neuromorphic computing applications, combining memtransistor properties of the device and high pattern classification accuracy. The results demonstrate a promising path toward developing energy‐efficient and scalable neural networks.

Abstract In organic electronics, conductivity doping is used primarily to eliminate charge injection barriers in organic light‐emitting diodes, organic photovoltaics and other electronic devices. Therefore, research on conductivity doping is primarily focused on understanding and enhancing the properties of these doped layers. In contrast, this work shifts the focus from optimizing doped layers to leveraging the doping process as a tool for investigating fundamental material properties. Specifically, the dopant is used as an “agent” to enable the measurement of three critical parameters‐ ionization potential (IP), electron affinity (EA), and Coulomb interaction energy (VC) – that govern dopant ionization and play central roles in organic electronic devices in general. While these parameters can be measured experimentally, conventional approaches often involve intricate or indirect methods, such as spectral deconvolution, which may introduce ambiguities or fail to represent bulk properties. Here it is shown how consolidating the experimental data and simulations on the dopant ionization fraction and doped‐induced conductivity can be used to estimate the mean IP or EA of the embedded organic molecule, and VC of the embedded charge‐transfer complex. These results illustrate how measuring and simulating doped materials can provide access to the fundamental design parameters of organic electronic devices.

Abstract This work develops unique physical models for AlGaN/GaN‐based Schottky barrier diodes (SBDs) grown on silicon (Si) substrates. The carrier transport and impact ionization processes are different from those of devices grown on sapphire substrates. Defects in the GaN epitaxial layer generate abundant leakage current and the impact ionization coefficients for the GaN layer shall be revised. The revised physical models are utilized to design SBDs with metal/Al₂O₃/GaN‐based (MIS) Schottky contact. Both numerically calculated and experimentally measured results prove the benefits of the passivation effect by the Al₂O₃ thin layer. The increased effective energy barrier height suppresses the image‐force‐caused energy band‐lowering effect. As a result, the reverse leakage current is reduced by 3 orders of magnitude when compared with the reference SBD. The revised physical models predict a ≈1100 V breakdown voltage (BV) for the MIS SBD with a specific ON‐resistance (Ron,sp) of ≈3.98 mΩ cm2, which numbers are consistent with measured results. The revised physical models are also able to precisely study the electrical stress reliability such that the MIS‐based Schottky contact can significantly reduce the surface trapping effect for electrons. This is proven by experimentally observing that the MIS SBD presents much stabler Ron,sp and turn‐on voltage (Von) in different electrical‐stress conditions.

Conductive Composite Elastomers Conducting polymer composites can be fashioned into novel sensing applications such as robotic skins. However, such composites naturally form an insulating surface layer, and the fidelity of such sensors depends on having reliable ohmic contact through this insulating surface to the underlying touch-sensing layer. Explicit studies of this surface layer are therefore needed; in article number 2400848, Noa Lachman, Matthew A. Grayson, and co-workers not only demonstrate the characterization of this insulating surface layer and the challenges it poses to making good electrical contact, but also provide prescriptive tools for fabricating and testing such contacts for proper ohmic behavior. Robotic image courtesy of the HAND-Engineering Research Center lab of Northwestern University.