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Direct current (DC) converters play an essential role in electronic circuits. Conventional high-efficiency DC voltage converters, especially step-up type, rely on switch-mode operation, where energy is periodically stored within and released from inductors and/or capacitors connected in a variety of circuit topologies. However, since these energy storage components, especially inductors, are difficult to scale down, miniaturization of switching converters for on-chip or in-package electronics faces fundamental challenges. Furthermore, the resulting switching currents produce electromagnetic noise, which can cause interference problems in nearby circuits, and even acoustic noise and mechanical vibrations that deteriorate the environment. In order to overcome the limitations of switch-mode converters, photonic transformers, where voltage conversion is achieved through the use of light emission and detection processes, have been demonstrated. However, the demonstrated efficiency is significantly below that of the switch-mode converter. Here we perform a theoretical analysis based on detailed balance, which shows that with a monolithically integrated design that enables efficient photon transport, the photonic transformer can operate with a near-unity conversion efficiency and high voltage conversion ratio. We validate the theoretical analysis with an experiment on a transformer constructed with off-the-shelf discrete components. Our experiment showcases near noiseless operation, as well as a voltage conversion ratio that is significantly higher than obtained in previous photonic transformer works. Our finding points to a high-performance optical solution to miniaturizing DC power converters for electronics and improving the electromagnetic compatibility and quality of electrical power.
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Silicon nitride (SiN) waveguides with ultra-low optical loss enable integrated photonic applications including low noise, narrow linewidth lasers, chip-scale nonlinear photonics, and microwave photonics. Lasers are key components to SiN photonic integrated circuits (PICs), but are difficult to fully integrate with low-index SiN waveguides due to their large mismatch with the high-index III-V gain materials. The recent demonstration of multilayer heterogeneous integration provides a practical solution and enabled the first-generation of lasers fully integrated with SiN waveguides. However a laser with high device yield and high output power at telecommunication wavelengths, where photonics applications are clustered, is still missing, hindered by large mode transition loss, nonoptimized cavity design, and a complicated fabrication process. Here, we report high-performance lasers on SiN with tens of milliwatts output through the SiN waveguide and sub-kHz fundamental linewidth, addressing all of the aforementioned issues. We also show Hertz-level linewidth lasers are achievable with the developed integration techniques. These lasers, together with high-$Q$ SiN resonators, mark a milestone towards a fully-integrated low-noise silicon nitride photonics platform. This laser should find potential applications in LIDAR, microwave photonics and coherent optical communications.
Resonance is instrumental in modern optics and photonics for novel phenomena such as cavity quantum electrodynamics and electric-field-induced transparency. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time (spanning from several hours to several weeks) and substantial computational resources. Such requirements significantly limit their applicability in understanding and inverse designing structures with desired resonance performances. Recently, the introduction of artificial intelligence allows for faster predictions of resonance with less demanding computational requirements. However, current end-to-end deep learning approaches generally fail to predict resonances with high quality-factors (Q-factor). Here, we introduce a universal deep learning strategy that can predict ultra-high Q-factor resonances by decomposing spectra with an adaptive data acquisition (ADA) method while incorporating resonance information. We exploit bound states in the continuum (BICs) with an infinite Q-factor to testify this resonance-informed deep learning (RIDL) strategy. The trained RIDL strategy achieves high-accuracy prediction of reflection spectra and photonic band structures while using a considerably small training dataset. We further develop an inverse design algorithm based on the RIDL strategy for a symmetry-protected BIC on a suspended silicon nitride photonic crystal (PhC) slab. The predicted and measured angle-resolved band structures show minimum differences. We expect the RIDL strategy to apply to many other physical phenomena which exhibit Gaussian, Lorentzian, and Fano resonances.
With the growing demand for massive amounts of data processing transmission and storage it is becoming more challenging to optimize the trade off between high speed and energy consumption in current optoelectronic devices. Heterogeneous material integration into Silicon and Nitride photonics has demonstrated high speed potential but with millimeter to centimeter large footprints. The search for an electro optic modulator that combines high speed with energy efficiency and compactness to enable high component density on chip is yet ongoing. Here we demonstrate a 60 GHz fast (3dB roll off) micrometer compact and 4 fJ per bit efficient Graphene based modulator integrated on Silicon photonics platform. Two dual Graphene layers are capacitively biased into modulating the waveguide modes optical effective index via Pauli blocking mechanism. The electro optic response which is further enhanced by a vertical distributed Bragg reflector cavity thus reducing the drive voltage by about 40 times while preserving an adequate modulation depth (10 dB). Compact efficient and fast modulators enable high photonic chip density and performance with key applications in signal processing sensor platforms and analog and neuromorphic photonic processors.
Breakdowns may occur in high-voltage applications even in ultrahigh vacuum conditions. Previously, we showed that it is important to pay attention to the post-breakdown voltage recovery in order to limit the appearance of secondary breakdowns associated with the primary ones. This can improve the overall efficiency of the high-voltage device. In this study, we focus on the optimization of the linear post-breakdown voltage recovery, with the principle aim of alleviating the problem of the secondary breakdowns. We investigate voltage recovery scenarios with different starting voltages and slopes of linear voltage increase by using a pulsed dc system. We find that a higher number of pulses during the voltage recovery produces fewer secondary BDs and a lower overall breakdown rate. Lowering the number of pulses led to more dramatic voltage recovery resulting in higher breakdown rates. A steeper voltage increase rate lead to a more localized occurrence of the secondary breakdowns near the end of the voltage recovery period. It was also found that the peak BD probability is regularly observed around 1 s after the end of the ramping period and that its value decreases exponentially with the amount of energy put into the system during the ramping. The value also decays exponentially with a half-life of (1.4$pm$0.3) ms if the voltage only increased between the voltage recovery steps.
The measurements of the high - temperature current - voltage characteristics of MoS2 thin - film transistors show that the devices remain functional to temperatures of at least as high as 500 K. The temperature increase results in decreased threshold voltage and mobility. The comparison of the DC and pulse measurements shows that the DC sub - linear and super - linear output characteristics of MoS2 thin - films devices result from the Joule heating and the interplay of the threshold voltage and mobility temperature dependences. At temperatures above 450 K, an intriguing phenomenon of the memory step - a kink in the drain current - occurs at zero gate voltage irrespective of the threshold voltage value. The memory step effect was attributed to the slow relaxation processes in thin films similar to those in graphene and electron glasses. The obtained results suggest new applications for MoS2 thin - film transistors in extreme - temperature electronics and sensors.
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