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Kwangwoong Kim

Publications and source records attributed to Kwangwoong Kim.

13 recordsLinked to original sources
arXiv2025

Chip-scale modulation-free laser stabilization using vacuum-gap micro-Fabry-Pérot cavity

Narrow-linewidth lasers are vital for a broad range of scientific and technological applications, including atomic clocks and precision sensing. Achieving high frequency stability is often as critical as ensuring scalability, portability, and cost-effectiveness in the development of low noise laser systems. Conventional electro-optic stabilization techniques, such as Pound-Drever-Hall locking to ultra-high-finesse resonators held in a vacuum chamber, provide excellent performance but remain challenging to scale. Here, we propose and experimentally demonstrate a cavity-coupled interferometric laser stabilization technique implemented on a silicon photonic chip and integrated with a compact, scalable micro-Fabry-Pérot cavity. The vacuum-gap optical cavity operates in air, achieving a quality factor of approximately $2.0\times 10^9$ and a fractional frequency instability of $1.45\times 10^{-12}$ at one-second averaging time. Integration of the proposed technique with the compact cavity yields more than 38-fold reduction in the laser's integrated linewidth and nearly three orders of magnitude suppression of frequency noise at 10 Hz offset frequency. The hybrid-integration of the proposed photonic chip with the micro-Fabry-Pérot cavity establishes a scalable and portable route toward chip-integrated ultra-stable lasers, paving the way for precision optical systems deployable beyond laboratory environments.

physics.optics↗
arXiv2025

Integrated photonic deep neural network with end-to-end on-chip backpropagation training

Integrated photonic neural networks (PNNs) have demonstrated significant potential to complement the digital electronic counterparts [1-3]. Nevertheless, robust and repeatable performance of scalable integrated PNNs is directly tied to the quality of their training. Error backpropagation (BP), which relies on nonlinear activation gradient computation, is the mainstream algorithm to train digital neural networks due to its scalability, versatility, and implementation efficiency [4]. Consequently, its adoption is highly desirable for the training of scalable PNNs. Despite such benefits and due to the lack of scalable on-chip activation gradient [5], PNNs have mostly been trained using a digital computer to run BP, which is inadequate in addressing device variations, or through gradient-free algorithms that do not fully benefit from the versatility of BP training. Here, we report the demonstration of an integrated photonic deep neural network with end-to-end on-chip gradient-descent BP training. All linear and nonlinear computations are performed on a single photonic chip, leading to scalable and robust training despite the considerable--but typical--fabrication-induced device variations. Two nonlinear data classification tasks are demonstrated in which the chip performance matches that of the ideal digital model, both in accuracy and robustness. Integrating the advantages of BP training with PNNs allows for generalization to various PNN architectures, paving the way for scalable and reliable next-generation photonic computing systems.

physics.optics↗
arXiv2025

A Comb-based Colorless Coherent WDM Transmitter

We propose a comb-based WDM transmitter capable of modulating independent signals to comb lines without demultiplexing them and prove its concept and potential scalability in a WDM transmitter consisting of a Kerr microcomb and a silicon I/Q modulator array.

physics.optics↗
arXiv2025

Terabit-class coherent communications enabled by an integrated photonics erbium doped amplifier

Coherent technologies have revolutionized optical communications, driving the capacity per fiber to multi-terabit per second (Tb/s) in combination with wavelength division multiplexing (WDM). With an ever-increasing deployment density of coherent systems, the demand for highly integrated WDM coherent transceivers has been rising. While tremendous progress has been made on silicon photonics compatible high-speed modulation and photodetection on chip, a solution for monolithically integrable amplifier with high gain and output power remains a challenge. Recently, an erbium doped waveguide amplifier based on ultra-low loss silicon nitride waveguides has demonstrated gain and output power levels potentially suitable for Terabit class coherent communications. Here, we demonstrate a WDM coherent system enabled by this integrated photonic amplification solution. The system uses the waveguide amplifier as a booster amplifier of 16 WDM signals each carrying a net data rate of 1.6 Tb/s, achieving 25.6-Tb/s net capacity over 81-km fiber transmission. Our results highlight a fully integrated solution for highly parallel coherent transceivers including amplification, that has the potential to transform future optical communications.

physics.optics↗
arXiv2024

Digital-analog hybrid matrix multiplication processor for optical neural networks

The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. However, current ONNs face various challenges,most significantly a limited calculation precision (typically around 4 bits) and the requirement for high-resolution signal format converters (digital-to-analogue conversions (DACs) and analogue-to-digital conversions (ADCs)). These challenges are inherent to their analog computing nature and pose significant obstacles in practical implementation. Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words. By introducing the logic levels and decisions based on thresholding, the calculation precision can be significantly enhanced. The DACs for input data can be removed and the resolution of the ADCs can be greatly reduced. This can increase the operating speed at a high calculation precision and facilitate the compatibility with microelectronics. To validate our approach, we have fabricated a proof-of-concept photonic chip and built up a hybrid optical processor (HOP) system for neural network applications. We have demonstrated an unprecedented 16-bit calculation precision for high-definition image processing, with a pixel error rate (PER) as low as $1.8\times10^{-3}$ at an signal-to-noise ratio (SNR) of 18.2 dB. We have also implemented a convolutional neural network for handwritten digit recognition that shows the same accuracy as the one achieved by a desktop computer. The concept of the digital-analog hybrid optical computing architecture offers a methodology that could potentially be applied to various ONN implementations and may intrigue new research into efficient and accurate domain-specific optical computing architectures for neural networks.

cs.NE↗
arXiv2023

Dual-Polarization Phase Retrieval Receiver in Silicon Photonics

We demonstrate a silicon photonic dual-polarization phase retrieval receiver. The receiver recovers phase from intensity-only measurements without a local oscillator or transmitted carrier. We design silicon waveguides providing long delays and microring resonators with large dispersion to enable symbol-to-symbol interference and dispersive projection in the phase retrieval algorithm. We retrieve the full field of a polarization-division multiplexed 30-GBd QPSK and 20-GBd 8QAM signals over 80 km of SSMF.

physics.optics↗
arXiv2023

Silicon Photonic Direct-Detection Phase Retrieval Receiver

We demonstrate a direct-detection phase retrieval receiver based on silicon photonics. The receiver implements strong dispersion and delay lines on a compact chip. We retrieve the full field of a 30-GBd QPSK signal without a carrier or local oscillator.

physics.optics↗
arXiv2023

Unveiling the origins of quasi-phase matching spectral imperfections in thin-film lithium niobate frequency doublers

Thin-film lithium niobate (TFLN) based frequency doublers have been widely recognized as essential components for both classical and quantum optical communications. Nonetheless, the efficiency of these devices is hindered by imperfections present in the quasi-phase matching (QPM) spectrum. In this study, we present a thorough analysis of the spectral imperfections in TFLN frequency doublers with varying lengths, ranging from 5 mm to 15 mm. Employing a non-destructive diagnostic method based on scattered light imaging, we identify the sources and waveguide sections that contribute to the imperfections in the QPM spectrum. Furthermore, by mapping the TFLN film thickness across the entire waveguiding regions, we successfully reproduce the QPM spectra numerically, thus confirming the prominent influence of film thickness variations on the observed spectral imperfections. This comprehensive investigation provides valuable insights into the identification and mitigation of spectral imperfections in TFLN-based frequency doublers, paving the way toward the realization of nonlinear optical devices with enhanced efficiency and improved spectral fidelity.

physics.optics↗
arXiv2023

Modulation-free Laser Stabilization Technique Using Integrated Cavity-Coupled Mach-Zehnder Interferometer

Stable narrow-linewidth light sources play a significant role in many precision optical systems. Electro-optic laser frequency stabilization systems, such as the well-known Pound-Drever-Hall (PDH) technique, have been key components of stable laser systems for decades. These control loops utilize an optical frequency noise discriminator (OFND) to measure frequency noise and convert it to an electronic servo signal. Despite their excellent performance, there has been a trade-off between complexity, scalability, power consumption, and noise measurement sensitivity. Here, we propose and experimentally demonstrate a modulation-free laser stabilization technique using an integrated cavity-coupled Mach-Zehnder interferometer (MZI) as an OFND. The proposed architecture maintains the sensitivity and performance of the PDH architecture without the need for any modulation. This significantly improves overall power consumption, simplifies the architecture, and makes it easier to miniaturize into an integrated photonic platform. An on-chip microring resonator with a loaded quality factor of 2.5 million is used as the frequency reference. The implemented chip suppresses the frequency noise of a semiconductor laser by 4 orders of magnitude. The integral linewidth of the free-running laser is suppressed from 6.1 MHz to 695 KHz. The passive implemented photonic integrated circuit occupies an area of 0.456 mm$^2$ and is integrated on AIM Photonics 180 nm silicon-on-insulator process.

physics.optics↗
arXiv2018

Laguerre-Gaussian mode sorter

Light's spatial properties represent an infinite state space, making it attractive for applications requiring high dimensionality, such as quantum mechanics and classical telecommunications, but also inherently spatial applications such as imaging and sensing. However, there is no demultiplexing device in the spatial domain comparable to a grating or calcite for the wavelength and polarisation domains respectively. Specifically, a simple device capable of splitting a finite beam into a large number of discrete spatially separated spots each containing a single orthogonal spatial component. We demonstrate a device capable of decomposing a beam into a Cartesian grid of identical Gaussian spots each containing a single Laguerre-Gaussian component. This is the first device capable of decomposing the azimuthal and radial components simultaneously, and is based on a single spatial light modulator and mirror. We demonstrate over 210 spatial components, meaning it is also the highest dimensionality mode multiplexer of any kind.

physics.optics↗