Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.

Bandwidth and noise are fundamental considerations in all communication and signal processing systems. The group-velocity dispersion of optical fibers creates nulls in their frequency response, limiting the bandwidth and hence the temporal response of communication and signal processing systems. Intensity noise is often the dominant optical noise source for semiconductor lasers in data communication. In this paper, we propose and demonstrate a class of electrooptic modulators that is capable of mitigating both of these problems. The modulator, fabricated in thin-film lithium niobate, simultaneously achieves phase diversity and differential operations. The former compensates for the fiber's dispersion penalty, while the latter overcomes intensity noise and other common mode fluctuations. Applications of the so-called four-phase electrooptic modulator in time-stretch data acquisition and in optical communication are demonstrated.

Phase Diversity Electro-optic Sampling: A new approach to single-shot terahertz waveform recording.

Recording electric field evolution in single-shot with THz bandwidth is needed in science including spectroscopy, plasmas, biology, chemistry, Free-Electron Lasers, accelerators, and material inspection. However, the potential application range depends on the possibility to achieve sub-picosecond resolution over a long time window, which is a largely open problem for single-shot techniques. To solve this problem, we present a new conceptual approach for the so-called spectral decoding technique, where a chirped laser pulse interacts with a THz signal in a Pockels crystal, and is analyzed using a grating optical spectrum analyzer. By borrowing mathematical concepts from photonic time stretch theory and radio-frequency communication, we deduce a novel dual-output electro-optic sampling system, for which the input THz signal can be numerically retrieved-with unprecedented resolution-using the so-called phase diversity technique. We show numerically and experimentally that this approach enables the recording of THz waveforms in single-shot over much longer durations and/or higher bandwidth than previous spectral decoding techniques. We present and test the proposed DEOS (Diversity Electro-Optic Sampling) design for recording 1.5 THz bandwidth THz pulses, over 20 ps duration, in single-shot. Then we demonstrate the potential of DEOS in accelerator physics by recording, in two successive shots, the shape of 200 fs RMS relativistic electron bunches at European X-FEL, over 10 ps recording windows. The designs presented here can be used directly for accelerator diagnostics, characterization of THz sources, and single-shot Time-Domain Spectroscopy.

Neural network enabled time stretch spectral regression.

Spectral interferometry is utilized in a wide range of biomedical and scientific applications and metrology. Retrieving the magnitude and phase of the complex electric field from the interferogram is central to all its applications. We report a spectral interferometry system that utilizes a neural network to infer the magnitude and phase of femtosecond interferograms directly from the measured single-shot interference patterns and compare its performance with the widely used Hilbert transform. Our approach does not require apriori knowledge of the shear frequency, and achieves higher accuracy under our experimental conditions. To train the network, we introduce an experimental technique that generates a large number of femtosecond interferograms with known (labeled) phase and magnitude profiles. While the profiles for these pulses are digitally generated, they obey causality by satisfying the Kramer-Kronig relation. This technique is resilient against nonlinear optical distortions, quantization noise, and the sampling rate limit of the backend digitizer - valuable properties that relax instrument complexity and cost.

Chromo-modal dispersion for optical communication and time-stretch spectroscopy.

Dispersion management is critical in many optical applications, whether to reduce impairments in fiber optic communication or chirp pulse amplification, or to create time stretch instruments for single-shot continuous recording of fast phenomena. The most common solutions for achieving large dispersion with low loss include dispersion compensation fiber, fiber Bragg grating, and diffraction grating pairs. Such dispersive elements have finite operational bandwidth, limited total dispersion, or insufficient power handling. In this Letter, we demonstrate a new, to the best of our knowledge, implementation of the chromo-modal dispersion device based on a silicon waveguide slab that addresses these limitations. The device provides extremely large dispersion with a widely tunable spectrum. We also propose a new time-stretch spectrometer where the absorption cell simultaneously provides spectrum-to-time mapping for fast single-shot spectroscopy.

Spectral dynamics on saturable absorber in mode-locking with time stretch spectroscopy.

A mode-locked laser that can produce a broadband spectrum and ultrashort pulse has been applied for many applications in an extensive range of scientific fields. To obtain stable mode-locking during a long time alignment-free, a semiconductor saturable absorber is one of the most suitable devices. Dynamics from noise to a stable mode-locking state in the spectral-domain are known as complex and a non-repetitive phenomenon with the time scale from nanoseconds to milliseconds. Thus, a conventional spectrometer, which is composed of a grating and line sensor, cannot capture the spectral behavior from noise to stable mode-locking. As a powerful spectral measurement technique, a time-stretch dispersive Fourier transformation (TS-DFT) has been recently used to enable a successive single-shot spectral measurement over a couple of milliseconds time span. Here, we experimentally demonstrate real-time spectral evolution of femtosecond pulse build-up in a homemade passive mode-locked Yb fiber laser with a semiconductor saturable absorber mirror using TS-DFT. Capturing 700 consecutive spectra (~ 17 µs time window) in real-time using the time-stretch technique, we are able to resolve the transient dynamics that lead to stable mode-locking. Before setting stable mode-locking, an oscillating or shifting fringe pattern in the consecutive spectra was detected. This signature proves the existence of multiple pulses (including a soliton molecule) which is temporally separated with a different relative phase. The dynamics on multiple pulses is originated from a fast relaxation time of the saturable absorption effect. This study provides novel insights into understanding the pulse behavior during the birth of an ultrafast mode-locked laser pulse and the stable single-pulse operation which is highly stabilized.

Spectro-temporal encoded multiphoton microscopy and fluorescence lifetime imaging at kilohertz frame-rates.

Two-Photon Microscopy has become an invaluable tool for biological and medical research, providing high sensitivity, molecular specificity, inherent three-dimensional sub-cellular resolution and deep tissue penetration. In terms of imaging speeds, however, mechanical scanners still limit the acquisition rates to typically 10-100 frames per second. Here we present a high-speed non-linear microscope achieving kilohertz frame rates by employing pulse-modulated, rapidly wavelength-swept lasers and inertia-free beam steering through angular dispersion. In combination with a high bandwidth, single-photon sensitive detector, this enables recording of fluorescent lifetimes at speeds of 88 million pixels per second. We show high resolution, multi-modal - two-photon fluorescence and fluorescence lifetime (FLIM) - microscopy and imaging flow cytometry with a digitally reconfigurable laser, imaging system and data acquisition system. These high speeds should enable high-speed and high-throughput image-assisted cell sorting.

Tera-sample-per-second single-shot device analyzer.

With the ever-increasing need for bandwidth in data centers and 5G mobile communications, technologies for rapid characterization of wide-band devices are in high demand. We report an instrument for extremely fast characterization of the electronic and optoelectronic devices with 27 ns frequency-response acquisition time at the effective sampling rate of 2.5 Tera-sample/s and an ultra-low effective timing jitter of 5.4 fs. This instrument features automated digital signal processing algorithms including time-series segmentation and frame alignment, impulse localization and Tikhonov regularized deconvolution for single-shot impulse and frequency response measurements. The system is based on the photonic time-stretch and features phase diversity to eliminate frequency fading and extend the bandwidth of the instrument.

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.

Fourier-domain mode-locked laser combined with a master-oscillator power amplifier architecture.

Originally introduced in 2005 for high-speed optical coherence tomography, the rapidly wavelength-swept Fourier-domain mode-locked (FDML) laser still, to this day, enables highest imaging speeds through a very high-speed spectral tuning capability. The FDML laser achieves a tuning bandwidth of over 1/10th of its center wavelength and can sweep this entire bandwidth in less than a microsecond. Interestingly, even though it covers a very broad spectral range, instantaneously it has a narrow spectral linewidth that puts it in a unique space compared to other high-speed broadband laser sources, e.g., mode-locked lasers or supercontinuum sources. Although it has been applied for nonlinear Raman spectroscopy and imaging, a current drawback of this continuous wave laser is the relatively low instantaneous power of 10-100 mW. Here, we report the combination of an FDML laser with a master oscillator power amplifier (MOPA) architecture to increase the instantaneous power of the FDML for nonlinear optical interactions. The output of an FDML laser around 1060 nm is modulated to short pulses by using an electro-optic amplitude modulator and subsequently amplified using ytterbium-doped fiber amplifiers (YDFAs). This generates a spectral rainbow of 65 picosecond pulses, where each pulse has a distinct, monochromatic wavelength. The instantaneous power can be adjusted by the YDFAs to reach nonlinear optical excitation regimes. This wavelength-swept FDML-MOPA laser will have a vast range of applications in, e.g., nonlinear optics, spectroscopy, imaging, and sensing.

Frequency-doubled FDML-MOPA laser in the visible.

Wavelength-swept lasers enable high-speed measurements in absorption spectroscopy, Raman spectroscopy, nonlinear Raman hyperspectral microscopy, rapid confocal microscopy, short impulse generation, and most importantly for high-speed optical coherence tomography, with speeds up to video-rate volumetric imaging. Recently, we introduced a pulsed wavelength-swept laser based on the Fourier domain mode-locked (FDML) laser principle combined with a master-oscillator power amplifier (MOPA) architecture. The high peak powers reached with this laser enabled rapid two-photon microscopy and two-photon fluorescence lifetime microscopy and high-speed light detection and ranging measurements. Here, we present the extension of this laser into the visible wavelength range by frequency doubling the output from 1064 nm to 532 nm via second harmonic generation in a deuterated potassium dihydrogen phosphate crystal. The result is a wavelength-swept laser source around 532 nm that outputs a pulse train of distinct, almost monochromatic wavelengths at an 88 MHz pulse repetition rate and 342 kHz sweep repetition rate. This swept-source laser in the visible can open up new research applications in spectroscopy, metrology, sensing, and high-speed imaging.

Feature Enhancement in Visually Impaired Images.

One of the major open problems in computer vision is feature detection in visually impaired images. In this paper, we describe a potential solution using Phase Stretch Transform, a new computational approach for image analysis, edge detection and resolution enhancement that is inspired by the physics of the photonic time stretch technique. We mathematically derive the intrinsic nonlinear transfer function and demonstrate how it leads to (1) superior performance at low contrast levels and (2) a reconfigurable operator for hyper-dimensional classification. We prove that the Phase Stretch Transform equalizes the input image brightness across a range of intensities resulting in high dynamic range in visually impaired images. We also show further improvement in the dynamic range by combining our method with the conventional techniques. Finally, our results propose a new paradigm for the computation of mathematical derivatives via group delay dispersion operations.

Spectral periodicity in soliton explosions on a broadband mode-locked Yb fiber laser using time-stretch spectroscopy.

Experimentally, we demonstrate, to the best of our knowledge, the first observation of periodic spectrum changing via soliton explosion in a passively mode-locked fiber laser by a nonlinear polarization evolution. Using time stretch to capture 7220 consecutive single-shot spectra over a 100 µs time window in real time, the soliton explosions appear in a transition between two different mode-locking states. Simultaneous measurements of spectrum and pulse energy at three different output points in the laser cavity show that the soliton explosion's dynamics are related to residual dispersion. This study improves the understanding of pulse formation and evolution in the unstable mode-locking regime of lasers.

Matrix Analysis of Warped Stretch Imaging.

Sensitive and fast optical imaging is needed for scientific instruments, machine vision, and biomedical diagnostics. Many of the fundamental challenges are addressed with time stretch imaging, which has been used for ultrafast continuous imaging for a diverse range of applications, such as biomarker-free cell classification, the monitoring of laser ablation, and the inspection of flat panel displays. With frame rates exceeding a million scans per second, the firehose of data generated by the time stretch camera requires optical data compression. Warped stretch imaging technology utilizes nonuniform spectrotemporal optical operations to compress the image in a single-shot real-time fashion. Here, we present a matrix analysis method for the evaluation of these systems and quantify important design parameters and the spatial resolution. The key principles of the system include (1) time/warped stretch transformation and (2) the spatial dispersion of ultrashort optical pulse, which are traced with simple computation of ray-pulse matrix. Furthermore, a mathematical model is constructed for the simulation of imaging operations while considering the optical and electrical response of the system. The proposed analysis method was applied to an example time stretch imaging system via simulation and validated with experimental data.

Single-shot network analyzer for extremely fast measurements.

A new instrument for fast measurement of frequency response of high-bandwidth optical and electronic devices is reported. Single-shot frequency spectrum measurements are enabled by time-stretch technology. An extremely fast measurement time of 27 ns is reported for the instrument. The reported instrument enables single-shot impulse response measurements with a 40 GHz bandwidth, which could be extended to beyond 100 GHz by using a faster electro-optic modulator. An ultra-low jitter of 20.5 fs is reported for the proposed instrument. The impulse responses measured using this technique are shown to correspond consistently with the manufacturer's specifications for the device under test. The reported instrument makes possible high-speed network parameter measurements, thereby enabling high-speed production-level testing of high-bandwidth opto-electronic devices/circuits/subsystems/systems and complex permittivity measurement of dielectric materials at a much reduced test time, lowering the test costs in a production environment.

Phase stretch transform for super-resolution localization microscopy.

Super-resolution localization microscopy has revolutionized the observation of living structures at the cellular scale, by achieving a spatial resolution that is improved by more than an order of magnitude compared to the diffraction limit. These methods localize single events from isolated sources in repeated cycles in order to achieve super-resolution. The requirement for sparse distribution of simultaneously activated sources in the field of view dictates the acquisition of thousands of frames in order to construct the full super-resolution image. As a result, these methods have slow temporal resolution which is a major limitation when investigating live-cell dynamics. In this paper we present the use of a phase stretch transform for high-density super-resolution localization microscopy. This is a nonlinear frequency dependent transform that emulates the propagation of light through a physical medium with a specific warped diffractive property and applies a 2D phase function to the image in the frequency domain. By choosing properly the transform parameters and the phase kernel profile, the point spread function of each emitter can be sharpened and narrowed. This enables the localization of overlapping emitters, thus allowing a higher density of activated emitters as well as shorter data collection acquisition rates. The method is validated by numerical simulations and by experimental data obtained using a microtubule sample.

Context-Aware Image Compression.

We describe a physics-based data compression method inspired by the photonic time stretch wherein information-rich portions of the data are dilated in a process that emulates the effect of group velocity dispersion on temporal signals. With this coding operation, the data can be downsampled at a lower rate than without it. In contrast to previous implementation of the warped stretch compression, here the decoding can be performed without the need of phase recovery. We present rate-distortion analysis and show improvement in PSNR compared to compression via uniform downsampling.

Deep Learning in Label-free Cell Classification.

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Analog optical computing primitives in silicon photonics.

Optical computing accelerators help alleviate bandwidth and power consumption bottlenecks in electronics. We show an approach to implementing logarithmic-type analog co-processors in silicon photonics and use it to perform the exponentiation operation and the recovery of a signal in the presence of multiplicative distortion. The function is realized by exploiting nonlinear-absorption-enhanced Raman amplification saturation in a silicon waveguide.

Design of Warped Stretch Transform.

Time stretch dispersive Fourier transform enables real-time spectroscopy at the repetition rate of million scans per second. High-speed real-time instruments ranging from analog-to-digital converters to cameras and single-shot rare-phenomena capture equipment with record performance have been empowered by it. Its warped stretch variant, realized with nonlinear group delay dispersion, offers variable-rate spectral domain sampling, as well as the ability to engineer the time-bandwidth product of the signal's envelope to match that of the data acquisition systems. To be able to reconstruct the signal with low loss, the spectrotemporal distribution of the signal spectrum needs to be sparse. Here, for the first time, we show how to design the kernel of the transform and specifically, the nonlinear group delay profile dictated by the signal sparsity. Such a kernel leads to smart stretching with nonuniform spectral resolution, having direct utility in improvement of data acquisition rate, real-time data compression, and enhancement of ultrafast data capture accuracy. We also discuss the application of warped stretch transform in spectrotemporal analysis of continuous-time signals.

Edge detection in digital images using dispersive phase stretch transform.

We describe a new computational approach to edge detection and its application to biomedical images. Our digital algorithm transforms the image by emulating the propagation of light through a physical medium with specific warped diffractive property. We show that the output phase of the transform reveals transitions in image intensity and can be used for edge detection.