Chip最新文献

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.

Superconducting nanowire single-photon detectors (SNSPDs) have become a mainstream photon-counting technology that has been widely applied in various scenarios. So far, most multi-channel SNSPD systems, either reported in literature or commercially available, are polarization sensitive, that is, the system detection efficiency (SDE) of each channel is dependent on the state of polarization of the to-be-detected photons. Here, we reported an eight-channel system with fractal SNSPDs working in the wavelength range of 930 to 940 nm, which are all featured with low polarization sensitivity. In a close-cycled Gifford-McMahon cryocooler system with the base temperature of 2.2 K, we installed and compared the performance of two types of devices: (1) SNSPD, composed of a single, continuous nanowire and (2) superconducting nanowire avalanche photodetector (SNAP), composed of 16 cascaded units of two nanowires electrically connected in parallel. The highest SDE among the eight channels reaches ${96}_{-5}^{+4}$%, with the polarization sensitivity of 1.02 and a dark-count rate of 13 counts per second. The average SDE for eight channels for all states of polarization is estimated to be 90 ± 5%. It is concluded that both the SNSPDs and the SNAPs can reach saturated, high SDE at the wavelength of interest, and the SNSPDs show lower dark-count (false-count) rates, whereas the SNAPs show better properties in the time domain. With the adoption of this system, we showcased the measurements of the second-order photon-correlation functions of light emission from a single-photon source based on a semiconductor quantum dot and from a pulsed laser. It is believed that this work will provide new choices of systems with single-photon detectors combining the merits of high SDE, low polarization sensitivity, and low noise that can be tailored for different applications.

As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction of electronic devices owing to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods, and excellent electronic properties. Especially in the recent years, memristors based on carbon nanomaterials have flourished in the field of building non-volatile memory devices and neuromorphic applications. In the current work, the preparation methods and structural characteristics of carbon nanomaterials of different dimensions were systematically reviewed. Afterwards, in depth discussion on the structural characteristics and working mechanism of memristors based on carbon nanomaterials of different dimensions was conducted. Finally, the potential applications of carbon-based memristors in logic operations, neural network construction, artificial vision systems, artificial tactile systems, and multimodal perception systems were also introduced. It is believed that this paper will provide guidance for the future development of high-quality information storage, high-performance neuromorphic applications, and high-sensitivity bionic sensing based on carbon-based memristors.

Building communication links among multiple users in a scalable and robust way is a key objective in achieving large-scale quantum networks. In a realistic scenario, noise from the coexisting classical light is inevitable and can ultimately disrupt the entanglement. The previous significant fully connected multiuser entanglement distribution experiments are conducted using dark fiber links, and there is no explicit relation between the entanglement degradations induced by classical noise and its error rate. Here, a semiconductor chip with a high figure-of-merit modal overlap is fabricated to directly generate broadband polarization entanglement. The monolithic source maintains the polarization entanglement fidelity of above 96% for 42 nm bandwidth, with a brightness of 1.2 × 10⁷ Hz mW⁻¹. A continuously working quantum entanglement distribution are performed among three users coexisting with classical light. Under finite-key analysis, secure keys are established and images encryption are enabled as well as quantum secret sharing between users. This work paves the way for practical multiparty quantum communication with integrated photonic architecture compatible with real-world fiber optical communication network.

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.

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.