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Solution-processed colloidal semiconductor nanocrystals (NCs) have become attractive materials for the development of optoelectronic and photonic devices due to their inexpensive synthesis and excellent optical properties. Recently, CdSe NCs with different dimensions and structures have achieved significant progress in photonic integrated circuits (PICs), including light generation (laser), guiding (waveguide), modulation, and detection on a chip. This article summarizes the development of CdSe NCs–based lasers and discusses the challenges and opportunities for the application of CdSe NCs in PICs. Firstly, an overview of the optical properties of CdSe-based NCs with different dimensions is presented, with emphasis on the amplified stimulated emission and laser properties. Then, the nanophotonic devices and PICs based on CdSe NCs are introduced and discussed. Finally, the prospects for PICs are addressed.

The development of large-scale quantum computing has boosted an urgent desire for the advancement of cryogenic CMOS (cryo-CMOS), which is a promising scalable solution for the control and read-out interface of quantum bits. In the current work, 180 nm CMOS transistors were characterized and modeled down to 4 K, and the impact of low-temperature transistor performance variations on circuit design was also analyzed. Based on the proposed cryogenic model, a 180 nm CMOS-based 450 to 850 MHz clock generator operating at 4 K for quantum computing applications was presented. At the output frequency of 600 MHz, it achieved < 4.8 ps RMS jitter with 30 mW power consumption (with test buffer), corresponding to a −211.6 dB jitter-power FOM, which is suitable for providing a stable clock signal for the control and readout electronics of scalable quantum computers.

This paper provides a comprehensive review of advanced radio frequency (RF) filter technologies available in miniature chip or integrated circuit (IC) form for wireless communication applications. The RF filter technologies were organized according to the timeline of their introduction, in conjunction with each generation of wireless (cellular) communication standards (1G to 5G). This approach enabled a clear explanation of the corresponding invention history, working principles, typical applications and future development trends. The article covered commercially successful acoustic filter technologies, including the widely used surface acoustic wave (SAW) and bulk acoustic wave (BAW) filters, as well as electromagnetic filter technologies based on low-temperature co-fired ceramic (LTCC) and integrated passive device (IPD). Additionally, emerging filter technologies such as IHP-SAW, suspended thin-film lithium niobate (LiNbO₃ or LN) resonant devices and hybrid were also discussed. In order to achieve higher performance, smaller form factor and lower cost for the wireless communication industry, it is believed that fundamental breakthroughs in materials and fabrication techniques are necessary for the future development of RF filters.

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.

With the advent of the “Big Data Era”, improving data storage density and computation speed has become more and more urgent due to the rapid growth in different types of data. Flash memory with a floating gate (FG) structure is attracting great attention owing to its advantages of miniaturization, low power consumption and reliable data storage, which is very effective in solving the problems of large data capacity and high integration density. Meanwhile, the FG memory with charge storage principle can simulate synaptic plasticity perfectly, breaking the traditional von Neumann computing architecture and can be used as an artificial synapse for neuromorphic computations inspired by the human brain. Among many candidate materials for manufacturing devices, van der Waals (vdW) materials have attracted widespread attention due to their atomic thickness, high mobility, and sustainable miniaturization properties. Owing to the arbitrary stacking ability, vdW heterostructure combines rich physics and potential 3D integration, opening up various possibilities for new functional integrated devices with low power consumption and flexible applications. This paper provides a comprehensive review of memory devices based on vdW materials with FG structure, including the working principles and typical structures of FG structure devices, with a focus on the introduction of various high-performance FG memories and their versatile applications in neuromorphic computing. Finally, the challenges of neuromorphic devices based on FG structures are also discussed. This review will shed light on the design and fabrication of vdW material-based memory devices with FG engineering, helping to promote the development of practical and promising neuromorphic computing.

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.