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Graphene has been widely used in silicon-based optical modulators for its ultra-broadband light absorption and ultrafast optoelectronic response. By incorporating graphene and slow-light silicon photonic crystal waveguide (PhCW), here we propose and experimentally demonstrate a unique double-layer graphene electro-absorption modulator in telecommunication applications. The modulator exhibits a modulation depth of 0.5 dB/μm with a bandwidth of 13.6 GHz, while graphene coverage length is only 1.2 μm in simulations. We also fabricated the graphene modulator on silicon platform, and the device achieved a modulation bandwidth at 12 GHz. The proposed graphene-PhCW modulator may have potentials in the applications of on-chip interconnections.

With the great developments in optical communication technology and large-scale optical integration technology, it is imperative to realize the traditional functions of polarization processing on an integration platform. Most of the existing polarization devices, such as polarization multiplexers/demultiplexers, polarization controllers, polarization analyzers, etc., perform only a single function. Definitely, integrating all these polarization functions on a chip will increase function flexibility and integration density and also cut the cost. In this article, we demonstrate an all-in-one chip-scale polarization processor based on a linear optical network. The polarization functions can be configured by tuning the array of phase shifters on the chip. We demonstrate multiple polarization processing functions, including those of a multiple-input-multiple-output polarization descrambler, polarization controller, and polarization analyzer, which are the basic building blocks of polarization processing. More functions can be realized by using an additional two-dimensional output grating. A numerical gradient descent algorithm is employed to self-configure and self-optimize these functions. Our demonstration suggests great potential for chip-scale, reconfigurable, and fully programmable photonic polarization processors with the artificial intelligence algorithm.

Thermo‐optic (TO) phase shifters are very fundamental units in large‐scale active silicon photonic integrated circuits (PICs). However, due to the limitation of microheater materials with a trade‐off between heating efficiency and absorption loss, designs reported so far typically suffer from slow response time, high power consumption, low yields, and so on. Here, an energy‐efficient, fast‐responding, low‐loss TO phase shifter is demonstrated by introducing hydrogen‐doped indium oxide (IHO) films as the microheater, and the optimized electron concentration with enhanced mobility endows the IHO with high conductivity as well as high near‐infrared (NIR) transparency, which allow it to directly contact the silicon waveguide without any insulating layer for efficient tuning and fast response. The TO phase shifter achieves a sub‐microsecond response time (970 ns/980 ns) with a π phase shift power consumption of 9.6 mW, and the insertion loss introduced by the IHO microheater is ≈0.5 dB. The proposed IHO‐based microheaters with compatible processing technology illustrate the great potential of such material in the application of large‐scale silicon PICs. [ABSTRACT FROM AUTHOR]

The great success of artificial intelligence (AI) calls for higher-performance computing accelerators, and optical neural networks (ONNs) with the advantages of high speed and low power consumption have become competitive candidates. However, most of the reported ONN architectures have demonstrated simple MNIST handwritten digit classification tasks due to relatively low precision. A microring resonator (MRR) weight bank can achieve a high-precision weight matrix and can increase computing density with the assistance of wavelength division multiplexing (WDM) technology offered by dissipative Kerr soliton (DKS) microcomb sources. Here, we implement a car plate recognition task based on an optical convolutional neural network (CNN). An integrated DKS microcomb was used to drive an MRR weight-bank-based photonic processor, and the computing precision of one optical convolution operation could reach 7 bits. The first convolutional layer was realized in the optical domain, and the remaining layers were performed in the electrical domain. Totally, the optoelectronic computing system (OCS) could achieve a comparable performance with a 64-bit digital computer for character classification. The error distribution obtained from the experiment was used to emulate the optical convolution operation of other layers. The probabilities of the softmax layer were slightly degraded, and the robustness of the CNN was reduced, but the recognition results were still acceptable. This work explores an MRR weight-bank-based OCS driven by a soliton microcomb to realize a real-life neural network task for the first time and provides a promising computational acceleration scheme for complex AI tasks. [ABSTRACT FROM AUTHOR]

Frequency measurement plays a crucial role in radar, communication, and various applications. The photonic-assisted frequency measurement method offers several advantages, including resistance to electromagnetic interference, broad bandwidth, and low power consumption. Notably, frequency-to-time mapping enables the measurement of various microwave signal types, such as single-frequency, multiple-frequency, frequency hopping, and chirped signals. However, the accuracy of this method is currently limited due to the absence of resonant devices with high-quality factors, which are essential for achieving higher-precision measurements. In this work, a frequency measurement method based on an ultrahigh-quality-factor microdisk is proposed. By establishing a correlation between the time difference and the frequency to be measured, a reduction in measurement error to below 10 MHz within a frequency measurement range of 3 GHz is realized. Our work introduces a new approach to frequency measurement using optical devices, opening new possibilities in this field. [ABSTRACT FROM AUTHOR]