Fiber-optic spectrum monitoring of wavelength-division-multiplexed telecommunication signals with MHz update rates.

We propose a novel (to our knowledge) and simple real-time optical monitoring (RTOM) system for dynamic spectral analysis of telecommunication signals, involving electro-optic (EO) temporal sampling followed by dispersion-induced frequency-to-time mapping and high-speed photodetection. This system enables tracking of the presence and relative intensity of multiple wavelength-division-multiplexed (WDM) data streams that span over a broad frequency band with high resolution, accuracy, and fast measurement update rates. We derive the design conditions and trade-offs of the proposed scheme and report proof-of-concept experiments and a numerical result that demonstrate successful spectral monitoring of dense-WDM signals with different modulation formats and bit rates, over the full C-band, with the needed resolution to discern channels separated by a few tens of GHz, and with an unprecedented fast measurement update rate in the MHz range.

Compact optical convolution processing unit based on multimode interference.

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations. Although the convolution kernels are interrelated, ten-class classification of handwritten digits from the MNIST database is experimentally demonstrated. The linear scalability of the proposed design with respect to computational size translates into a solid potential for large-scale integration.

All-fibre phase filters with 1-GHz resolution for high-speed passive optical logic processing.

Photonic-based implementation of advanced computing tasks is a potential alternative to mitigate the bandwidth limitations of electronics. Despite the inherent advantage of a large bandwidth, photonic systems are generally bulky and power-hungry. In this respect, all-pass spectral phase filters enable simultaneous ultrahigh speed operation and minimal power consumption for a wide range of signal processing functionalities. Yet, phase filters offering GHz to sub-GHz frequency resolution in practical, integrated platforms have remained elusive. We report a fibre Bragg grating-based phase filter with a record frequency resolution of 1 GHz, at least 10× improvement compared to a conventional optical waveshaper. The all-fibre phase filter is employed to experimentally realize high-speed fully passive NOT and XNOR logic operations. We demonstrate inversion of a 45-Gbps 127-bit random sequence with an energy consumption of ~34 fJ/bit, and XNOR logic at a bit rate of 10.25 Gbps consuming ~425 fJ/bit. The scalable implementation of phase filters provides a promising path towards widespread deployment of compact, low-energy-consuming signal processors.

Ultra-compact silicon photonics highly dispersive elements for low-latency signal processing.

On-chip optical group-velocity dispersion (GVD) is highly desired for a wide range of signal processing applications, including low-latency and low-power-consumption dispersion compensation of telecommunication data signals. However, present technologies, such as linearly chirped waveguide Bragg gratings (LCWBGs), employ spectral phase accumulation along the frequency spectrum. To achieve the needed specifications in most applications, this strategy requires device lengths that are not compatible with on-chip integration while incurring in relatively long processing latencies. Here, we demonstrate a novel design strategy that utilizes a discretized and bounded spectral phase filtering process to emulate the continuous spectral phase variation of a target GVD line. This leads to a significant reduction of the resulting device length, enabling on-chip integration and ultra-low latencies. In experiments, we show GVD compensation of both NRZ and PAM4 data signals with baud rates up to 24 GBd over a 31.12-km fibre-optic link using a 4.1-mm WBG-based on-chip phase filter in a silicon-on-insulator (SOI) platform, at least 5× shorter compared to an equivalent LCWBG, reducing the processing latency down to ∼ 100 ps. The bandwidth of the mm-long device can be further extended to the THz range by employing a simple and highly efficient phase-only sampling of the grating profile. The proposed solution provides a promising route toward a true on-chip realization of a host of GVD-based all-optical analog signal processing functionalities.

Broadly tunable and ultra-highly selective detection of radio frequency signals enabled by a silicon photonic chip.

Precise and agile detection of radio frequency (RF) signals over an ultra-wide frequency range is a key functionality in modern communication, radar, and surveillance systems, as well as for radio astronomy and laboratory testing. However, current microwave solutions are inadequate for achieving the needed high performance in a chip-scale format, with the desired reduced cost, size, weight, and power. Photonics-based technologies have been identified as a potential solution but the need to compensate for the inherent noise of the involved laser sources have prevented on-chip realization of wideband RF signal detection systems. Here, we report an approach for ultra-wide range, highly-accurate detection of RF signals using a conceptually novel feed-forward laser's noise cancelling architecture integrated on chip. The technique is applied to realization of an RF scanning receiver as well as a complete radar transceiver integrated on a CMOS-compatible silicon-photonics chip, offering an unprecedented selectivity > 80 dB, spectral resolution < 1 kHz, and tunability in the full 0.5-35 GHz range. The reported work represents a significant step towards the development of integrated system-on-chip platforms for signal detection, analysis and processing in cognitive communication and radar network applications.

Versatile metal-wire waveguides for broadband terahertz signal processing and multiplexing.

Waveguides play a pivotal role in the full deployment of terahertz communication systems. Besides signal transporting, innovative terahertz waveguides are required to provide versatile signal-processing functionalities. Despite fundamental components, such as Bragg gratings, have been recently realized, they typically rely on complex hybridization, in turn making it extremely challenging to go beyond the most elementary functions. Here, we propose a universal approach, in which multiscale-structured Bragg gratings can be directly etched on metal-wires. Such an approach, in combination with diverse waveguide designs, allows for the realization of a unique platform with remarkable structural simplicity, yet featuring unprecedented signal-processing capabilities. As an example, we introduce a four-wire waveguide geometry, amenable to support the low-loss and low-dispersion propagation of polarization-division multiplexed terahertz signals. Furthermore, by engraving on the wires judiciously designed Bragg gratings based on multiscale structures, it is possible to independently manipulate two polarization-division multiplexed terahertz signals. This platform opens up new exciting perspectives for exploiting the polarization degree of freedom and ultimately boosting the capacity and spectral efficiency of future terahertz networks.

Group-velocity dispersion emulator using a time lens.

We report a novel method to continuously track the temporal evolution of an arbitrary complex waveform as it propagates through a group-velocity dispersion medium by using a single-frequency-driven phase modulator. The proposed method exploits the fact that the frequency spectrum of a given (input) waveform, following a suitable sinusoidal temporal phase modulation, exhibits the same shape as that of a dispersed version of the same temporal waveform after propagation through a prescribed amount of dispersion. In experiments, we track the dispersion-induced temporal evolution of different optical picosecond pulsed waveforms by tuning the frequency and/or amplitude of the phase modulation signal and observing the resulting shapes in the optical frequency domain. A good agreement is obtained between the measured spectra and predicted temporal shapes of the propagating waveform for different amounts of dispersion. Moreover, the method is successfully applied on a chirped optical pulse to find the optimal pulse compression conditions.

High-speed serial deep learning through temporal optical neurons.

Deep learning is able to functionally mimic the human brain and thus, it has attracted considerable recent interest. Optics-assisted deep learning is a promising approach to improve forward-propagation speed and reduce the power consumption of electronic-assisted techniques. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with the serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a sequential optical deep learning concept that is specifically designed to directly process high-speed serial data. By utilizing ultra-short optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 79.2% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII codes of English letters at a data rate of 12 gigabits per second. This concept represents a novel one-dimensional realization of artificial neural networks, enabling a direct application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for temporal signal processing.

Full recovery of ultrafast waveforms lost under noise.

The ability to detect ultrafast waveforms arising from randomly occurring events is essential to such diverse fields as bioimaging, spectroscopy, radio-astronomy, sensing and telecommunications. However, noise remains a significant challenge to recover the information carried by such waveforms, which are often too weak for detection. The key issue is that most of the undesired noise is contained within the broad frequency band of the ultrafast waveform, such that it cannot be alleviated through conventional methods. In spite of intensive research efforts, no technique can retrieve the complete description of a noise-dominated ultrafast waveform of unknown parameters. Here, we propose a signal denoising concept involving passive enhancement of the coherent content of the signal frequency spectrum, which enables the full recovery of arbitrary ultrafast waveforms buried under noise, in a real-time and single-shot fashion. We experimentally demonstrate the retrieval of picosecond-resolution waveforms that are over an order of magnitude weaker than the in-band noise. By granting access to previously undetectable information, this concept shows promise for advancing various fields dealing with weak or noise-dominated broadband waveforms.

Frequency-domain ultrafast passive logic: NOT and XNOR gates.

Electronic Boolean logic gates, the foundation of current computation and digital information processing, are reaching final limits in processing power. The primary obstacle is energy consumption which becomes impractically large, > 0.1 fJ/bit per gate, for signal speeds just over several GHz. Unfortunately, current solutions offer either high-speed operation or low-energy consumption. We propose a design for Boolean logic that can achieve both simultaneously (high speed and low consumption), here demonstrated for NOT and XNOR gates. Our method works by passively modifying the phase relationships among the different frequencies of an input data signal to redistribute its energy into the desired logical output pattern. We experimentally demonstrate a passive NOT gate with an energy dissipation of ~1 fJ/bit at 640 Gb/s and use it as a building block for an XNOR gate. This approach is applicable to any system that can propagate coherent waves, such as electromagnetic, acoustic, plasmonic, mechanical, or quantum.

Information transfer via temporal convolution in nonlinear optics.

Nonlinear parametric processes involving ultrashort pulses are typically carried out in time domain, which mathematically corresponds to a convolution of their frequency spectra. In contrast, this spectral convolution changes into a multiplication operation when performing the nonlinear interaction in frequency domain. Here, we extend the scope of frequency-domain nonlinear optics by demonstrating its ability to perform a temporal convolution. Through this approach, nonlinear optical operations that are inaccessible in time domain can be realised: specific optical information can be coherently advanced by picoseconds within a pulse sequence-a newly generated second harmonic pulse carries the amplitude and phase information of two input pulses. This central pulse is isolated when using an input field consisting of two cross-polarized input pulses in combination with type-II second harmonic generation. The effects of nonlinear temporal convolution can be viewed from the aspect of signal processing and pulse shaping, where the nonlinear interaction in the parametric crystal plays the role of a dynamic linear optical filter-in contrast to conventional static filters-with a shaping mask instantaneously adapting to the laser field.

On-chip dispersive phase filters for optical processing of periodic signals.

We develop a dispersive phase filter design framework suitable for compact integration using waveguide Bragg gratings (WBGs) in silicon. Our proposal is to utilize an equivalent "discrete" spectral phase filtering process, in which the original continuous quadratic spectral phase function of a group velocity dispersion (GVD) line is discretized and bounded in a modulo 2π basis. Through this strategy, we avoid the phase accumulation of the GVD line, leading to a significant reduction in device footprint (length) as compared to conventional GVD devices (e.g., using a linearly chirped WBG). The proposed design is validated through numerical simulations and proof-of-concept experiments. Specifically, using the proposed methodology, we demonstrate 2× pulse repetition-rate multiplication of a 10 GHz picosecond pulse train by dispersion-induced Talbot effect on a silicon chip.

Nonlinear time-lens with improved power efficiency through a discrete multilevel pump: publisher's note.

This publisher's note contains corrections to Opt. Lett.45, 3557 (2020).OPLEDP0146-959210.1364/OL.396342.

Nonlinear time-lens with improved power efficiency through a discrete multilevel pump.

We report a novel, to the best of our knowledge, all-optical discrete multilevel time-lens (DM-TL) design based on cross-phase modulation (XPM). In this approach, the pump is synthesized such as the quadratic phase modulation is applied to the probe in constant-level time-bins with a maximum phase excursion of 2π. As a result, a considerable reduction in the required pump power is achieved in comparison to the conventional approach based on a parabolic pump. To illustrate the concept, the proposed DM-TL is here applied to the energy-preserving conversion of a continuous-wave (CW) signal into a train of pulses according to the theory of temporal Talbot array illuminators. We demonstrate CW-to-pulse conversion gains up to 12 at repetition rates exceeding 16 GHz, with a power saving with respect to the conventional parabolic TL that is more significant for increasing conversion gains.

Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing.

Real-time tracking of a waveform frequency content is essential for detection and analysis of fast rare events in communications, radar, radio astronomy, spectroscopy, sensing etc. This requires a method that can provide real-time spectrum analysis (RT-SA) of high-speed waveforms in a continuous and gap-free fashion. Digital signal processing is inefficient to perform RT-SA over instantaneous frequency bandwidths above the sub-GHz range and/or to track spectral changes faster than a few microseconds. Analog dispersion-induced frequency-to-time mapping enables RT-SA of short isolated pulse-like signals but cannot be extended to continuous waveforms. Here, we propose a universal analog processing approach for time-mapping a gap-free spectrogram -the prime method for dynamic frequency analysis- of an incoming arbitrary waveform, based on a simple sampling and dispersive delay scheme. In experiments, the spectrograms of GHz-bandwidth microwave signals are captured at a speed of ~5×109 Fourier transforms per second, allowing to intercept nanosecond-duration frequency transients in real time. This method opens new opportunities for dynamic frequency analysis and processing of high-speed waveforms.

Expanding the phenotypes of congenital hemangiomas.

Congenital hemangiomas (CH) are benign vascular tumors that are present at birth and do not stain for the marker Glut-1. Herein, we describe five cases of CH with atypical presentations: 3 with late growth, 1 with slow involution, and 1 that partially involuted rapidly then manifested late growth.

Generalized Talbot self-healing and noise mitigation of faulty periodic images.

Obtaining high-quality images from physical systems, objects, and processes is fundamental for a myriad of areas of science and technology. However, in many situations, the measured images contain defects and/or are accompanied by noise, degrading the quality of the measurement. Recently, a variant of the well-known Talbot self-imaging effect has been shown to redistribute the energy of a spatially periodic collection of images, obtaining output images with increased energy with respect to the input ones. In this work we experimentally demonstrate that such an energy redistribution method has the unique capabilities of increasing the coherent energy level of a periodic set of images over that of the incoherent noise, even allowing images completely buried under noise to be recovered. We further demonstrate that the process can mitigate potential faults of the periodic image structure, including blocked images, spatial jitter, and coherent noise, offering important enhancements (e.g., in regards to the quality of the recovered individual images) in the self-healing capabilities of Talbot self-imaging.

Real-time measurement of complex fast signals by bandwidth compression in frequency shifting loops.

We report coherent time-to-frequency mapping in frequency shifting loops (FSLs). We show that when seeded by a temporal signal shorter than the inverse of the frequency shift per roundtrip, the optical spectrum at the FSL output consists of a periodic replica of the input waveform, whose temporal amplitude and phase profiles are mapped into the frequency domain. We provide an experimental demonstration of this phenomenon and show how this simple setup enables real-time measurement of fast non-repetitive input RF signals with a detection chain two orders of magnitude slower than the input signal.