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Two-dimensional ferromagnetic (2DFM) semiconductors (metals, half-metals, and so on) are important materials for next-generation nano-electronic and nano-spintronic devices. However, these kinds of materials remain scarce, “trial and error” experiments and calculations are both time-consuming and expensive. In the present work, in order to obtain the optimal 2DFM materials with strong magnetization, a machine learning (ML) framework was established to search the 2D material space containing over 2417 samples and identified 615 compounds whose magnetic orders were then determined via high-throughput first-principles calculations. With the adoption of ML algorithms, two classification models and a regression model were trained. The interpretability of the regression model was evaluated through Shapley Additive exPlanations (SHAP) analysis. Unexpectedly, it is found that Cr₂NF₂ is a potential antiferromagnetic ferroelectric 2D multiferroic material. More importantly, 60 novel 2DFM candidates were predicted, and among them, 13 candidates have magnetic moments of > 7μ_B. Os₂Cl₈, Fe₃GeSe₂, and Mn₄N₃S₂ were predicted to be novel 2DFM semiconductors, metals, and half-metals, respectively. With the adoption of the ML approach in the current work, the prediction of 2DFM materials with strong magnetization can be accelerated, and the computation time can be drastically reduced by more than one order of magnitude.

Exceptional points (EPs), which are typically defined as the degeneracy points of a non-Hermitian Hamiltonian, have been investigated in various physical systems such as photonic systems. In particular, the intriguing topological structures around EPs have given rise to novel strategies for manipulating photons and the underlying mechanism is especially useful for on-chip photonic applications. Although some on-chip experiments with the adoption of lasers have been reported, EP-based photonic chips working in the quantum regime largely remain elusive. In the current work, a single-photon experiment was proposed to dynamically encircle an EP in on-chip photonic waveguides possessing passive anti-parity-time symmetry. Photon coincidences measurement reveals a chiral feature of transporting single photons, which can act as a building block for on-chip quantum devices that require asymmetric transmissions. The findings in the current work pave the way for on-chip experimental study on the physics of EPs as well as inspiring applications for on-chip non-Hermitian quantum devices.

Wide-bandgap semiconductors exhibit much larger energy bandgaps than traditional semiconductors such as silicon, rendering them very promising to be applied in the fields of electronics and optoelectronics. Prominent examples of semiconductors include SiC, GaN, ZnO, and diamond, which exhibit distinctive characteristics such as elevated mobility and thermal conductivity. These characteristics facilitate the operation of a wide range of devices, including energy-efficient bipolar junction transistors (BJTs) and metal-oxide-semiconductor field-effect transistors (MOSFETs), as well as high-frequency high-electron-mobility transistors (HEMTs) and optoelectronic components such as light-emitting diodes (LEDs) and lasers. These semiconductors are used in building integrated circuits (ICs) to facilitate the operation of power electronics, computer devices, RF systems, and other optoelectronic advancements. These breakthroughs include various applications such as imaging, optical communication, and sensing. Among them, the field of power electronics has witnessed tremendous progress in recent years with the development of wide bandgap (WBG) semiconductor devices, which is capable of switching large currents and voltages rapidly with low losses. However, it has been proven challenging to integrate these devices with silicon complementary metal oxide semiconductor (CMOS) logic circuits required for complex control functions. The monolithic integration of silicon CMOS with WBG devices increases the complexity of fabricating monolithically integrated smart integrated circuits (ICs). This review article proposes implementing CMOS logic directly on the WBG platform as a solution. However, achieving the CMOS functionalities with the adoption of WBG materials still remains a significant hurdle. This article summarizes the research progress in the fabrication of integrated circuits adopting various WBG materials ranging from SiC to diamond, with the goal of building future smart power ICs.

In the pursuit for scalable quantum processors, significant effort has been devoted to the development of cryogenic classical hardware for the control and readout of a growing number of qubits. The current work presented a novel approach called impedancemetry that is suitable for measuring the quantum capacitance of semiconductor qubits connected to a resonant LC-circuit. The impedancemetry circuit exploits the integration of a complementary metal-oxide-semiconductor (CMOS) active inductor in the resonator with tunable resonance frequency and quality factor, enabling the optimization of readout sensitivity for quantum devices. The realized cryogenic circuit allows fast impedance detection with a measured capacitance resolution down to 10 aF and an input-referred noise of 3.7 aF/$\sqrt{Hz}$. At 4.2 K, the power consumption of the active inductor amounts to 120 μW, with an additional dissipation for on-chip current excitation (0.15 μW) and voltage amplification (2.9 mW) of the impedance measurement. Compared to the commonly used schemes based on dispersive RF reflectometry which require millimeter-scale passive inductors, the circuit exhibits a notably reduced footprint (50 μm × 60 μm), facilitating its integration in a scalable quantum-classical architecture. The impedancemetry method has been applied at 4.2 K to the detection of quantum effects in the gate capacitance of on-chip nanometric CMOS transistors that are individually addressed via multiplexing.

This paper demonstrated the fabrication, characterization, data-driven modeling, and practical application of a 1D SnO₂ nanofiber-based memristor, in which a 1D SnO₂ active layer was sandwiched between silver (Ag) and aluminum (Al) electrodes. This device yielded a very high R_OFF : R_ON of ∼10⁴ (I_ON : I_OFF of ∼10⁵) with an excellent activation slope of 10 mV/dec, low set voltage of V_SET ∼ 1.14 V and good repeatability. This paper physically explained the conduction mechanism in the layered SnO₂ nanofiber-based memristor. The conductive network was composed of nanofibers that play a vital role in the memristive action, since more conductive paths could facilitate the hopping of electron carriers. Energy band structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claims reported in this paper. An machine learning (ML)–assisted, data-driven model of the fabricated memristor was also developed employing different popular algorithms such as polynomial regression, support vector regression, k nearest neighbors, and artificial neural network (ANN) to model the data of the fabricated device. We have proposed two types of ANN models (type I and type II) algorithms, illustrated with a detailed flowchart, to model the fabricated memristor. Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the best mean absolute percentage error of 0.0175 with a 98% R² score. The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adopting the same fabrication recipe, which gave satisfactory predictions. Lastly, the ANN type II model was applied to design and implement simple AND & OR logic functionalities adopting the fabricated memristors with expected, near-ideal characteristics.

The mitigation of dephasing poses a significant challenge to improving the performance of error-prone superconducting quantum computing systems. Here, the dephasing of a transmon qubit in a dispersive readout regime was investigated by adopting a Josephson traveling-wave parametric amplifier as the preamplifier. Our findings reveal that the potent pump leakage from the preamplifier may lead to severe dephasing. This could be attributed to a mixture of measurement-induced dephasing, ac Stark effect, and heating. It is showed that pulse-mode readout is a promising measurement scheme to mitigate qubit dephasing while minimizing the need for bulky circulators. Our work provides key insights into mitigating decoherence from microwave-pumped preamplifiers, which will be critical for advancing large-scale quantum computers.

The multi-Fano interference, which is obtained through the simultaneous acquisition of bright and dark states in different phase transitions of Eu³⁺ : BiPO₄ (7 : 1, 6 : 1, 1 : 1, and 0.5 : 1) and Eu³⁺ : NaYF₄ (1 : 1/4) crystals, were reported in this work. Multidressed spontaneous four-wave mixing and multidressed fluorescence (multiorder) were adopted to optimize the strong photon–phonon nested dressing effect, which results in more obvious multi-Fano interference. Firstly, the multi-Fano is produced through interference in continuous and multibound states. Secondly, five multi-Fano dips are originated from the nested five dressings (one photon and four phonons) under symmetrical splitting of ⁷F₁ energy level. It is found that the pure H-phase (0.5 : 1) sample exhibits the strongest photon–phonon dressed effect (five Fano dips). Further, high-order non-Hermitian exceptional points in multi-Fano interference were investigated by adjusting the ratio of Rabi frequency to dephase rate through nested photon and phonon dressing. The experimental results are validated by theoretical simulations, which may be applied to designing optoelectronic devices such as non-Hermitian multi-Fano interferences (multichannel) router.

With major signal analytical elements situated away from the measurement environment, extended gate (EG) ion-sensitive field-effect transistors (ISFETs) offer prospects for whole chip circuit design and system integration of chemical sensors. In this work, a highly sensitive and power-efficient ISFET was proposed based on a metal–ferroelectric–insulator gate stack with negative capacitance–induced super-steep subthreshold swing and ferroelectric memory function. Along with a remotely connected EG electrode, the architecture facilitates diverse sensing functions for future establishment of smart biochemical sensor platforms.

Moore's Law has been the driving force behind the semiconductor industry for several decades, but as silicon-based transistors approach their physical limits, researchers are searching for new materials to sustain this exponential growth. Two-dimensional transition metal dichalcogenides (TMDs), with their atomically thin structure and enticing physical properties, have emerged as the most promising candidates for downsizing and improving device integration. Emboldened by the direction of achieving large-area and high-quality TMDs growth, wafer-scale TMDs growth strategies have been continuously developed, suggesting that TMDs are poised to become a new platform for next-generation electronic devices. In this review, advanced synthesis routes and inherent properties of wafer-scale TMDs were critically assessed. In addition, the performance in electronic devices was also discussed, providing an outlook on the opportunities and challenges that lie ahead in their development.