arXiv subjects

Qixiang Cheng

Publications and source records attributed to Qixiang Cheng.

16 recordsLinked to original sources

arXiv2025

3D surface profiling via photonic integrated geometric sensor

Measurements of microscale surface patterns are essential for process and quality control in industries across semiconductors, micro-machining, and biomedicines. However, the development of miniaturized and intelligent profiling systems remains a longstanding challenge, primarily due to the complexity and bulkiness of existing benchtop systems required to scan large-area samples. A real-time, in-situ, and fast detection alternative is therefore highly desirable for predicting surface topography on the fly. In this paper, we present an ultracompact geometric profiler based on photonic integrated circuits, which directly encodes the optical reflectance of the sample and decodes it with a neural network. This platform is free of complex interferometric configurations and avoids time-consuming nonlinear fitting algorithms. We show that a silicon programmable circuit can generate pseudo-random kernels to project input data into higher dimensions, enabling efficient feature extraction via a lightweight one-dimensional convolutional neural network. Our device is capable of high-fidelity, fast-scanning-rate thickness identification for both smoothly varying samples and intricate 3D printed emblem structures, paving the way for a new class of compact geometric sensors.

physics.optics↗

arXiv2025

Wavelength-agnostic 3D-Nanoprinted coupler

We present a photonic coupler that exhibits effectively wavelength-agnostic performance for ultra-broadband optical interfacing. By incorporating a dual-ellipsoidal geometry, the design facilitates quasi-free-space optical propagation. We further propose a hybrid modelling workflow employs a matrix optics-based approach as an efficient pre-design tool, capturing critical geometry-to-mode mapping characteristics, significantly narrowing the parameter space required for subsequent full-vectorial finite-difference time-domain (FDTD) simulations. Our design achieves a 1 dB bandwidth exceeding 800 nm coupling from fibre to chip, with an insertion loss as low as 1.3 dB,to the best of our knowledge, a record for any reported photonic couplers. The additive manufacturing approach via 3D nano-printing enables flexible geometry customization and sub-micron integrated alignment features, facilitating seamless integration with photonic chips and optical fibers. Experimental validation demonstrates excellent stability and thermal robustness across diverse operational conditions, highlighting the design's suitability for integration into wide range of broadband photonic systems.

physics.optics↗

arXiv2025

Tri-layer SiN-on-Si 8x8 Optical Switches with Thermo-optic and Electro-optic Actuators

We present two spatial-multiplexed switch-and-select (S&S) 8x8 optical switches incorporating a tri-layer SiN-on-Si platform, one equipped with thermo-optic (T-O) and the other electro-optic (E-O) switching elements. To the best of our knowledge, the electro-optic switch fabric is the first-of-its-kind device assembled in such a multi-layer platform. The shuffle between the multiplexer and demultiplexer array is established via a tri-layer Si-SiN-SiN structure, creating a three-dimensional crossing-free photonic shuffle network. At the same time, the implementation of the S&S topology can effectively suppress the first-order crosstalk. The measured on-chip losses for the T-O switch range from 2.1 to 11.5 dB, with a 5.2 dB average, while the E-O device exhibits losses between 8.7 to 19.6 dB, with a 15.1 dB average. Both switches demonstrate ultra-low crosstalk, with measured ranges of 38.9 to 50.8 dB and 42.8 to 51.9 dB, for the T-O and E-O devices respectively. The switching times are 17.6 us for the T-O switch and 5.9 ns with the E-O actuated one. These performance metrics highlight the potential of these switches for next-generation data center applications.

physics.optics↗

arXiv2025

Optical Convolutional Spectrometer

Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and inadequate for metrological purposes. Here, we introduce a new class of spectrometer, leveraging the convolution theorem as its mathematical foundation. Our convolutional spectrometer offers unmatched performance for miniaturized systems and distinct structural and computational simplicity, featuring a centimeter-scale footprint for the fully packaged unit, low cost (~$10) and a 2400 cm-1 (approximately 500 nm) bandwidth. We achieve excellent precision in resolving complex spectra with sub-second sampling and processing time, demonstrating a wide range of applications from industrial and agricultural analysis to healthcare monitoring. Specifically, our spectrometer system classifies diverse solid samples, including plastics, pharmaceuticals, coffee, flour and tea, with 100% success rate, and quantifies concentrations of aqueous and organic solutions with detection accuracy surpassing commercial benchtop spectrometers. We also realize the non-invasive sensing of human biomarkers, such as skin moisture (mean absolute error; MAE = 2.49%), blood alcohol (1.70 mg/dL), blood lactate (0.81 mmol/L), and blood glucose (0.36 mmol/L), highlighting the potential of this new class of spectrometers for low-cost, high-precision, portable/wearable spectral metrology.

physics.optics↗

arXiv2024

Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI

The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes significant power and faces computation security risks due to the reliance on extensive data centers and servers in the cloud. Reducing power consumption while enhancing computational scale remains persistent challenges in cloud computing. Here, we propose and experimentally demonstrate an optical cloud computing system that can be seamlessly deployed across edge-metro network. By modulating inputs and models into light, a wide range of edge nodes can directly access the optical computing center via the edge-metro network. The experimental validations show an energy efficiency of 118.6 mW/TOPs (tera operations per second), reducing energy consumption by two orders of magnitude compared to traditional electronic-based cloud computing solutions. Furthermore, it is experimentally validated that this architecture can perform various complex generative AI models through parallel computing to achieve image generation tasks.

cs.DC↗

arXiv2024

Dilated space-and-wavelength selective crosspoint optical switch

Photonic integrated switches that are both space and wavelength selective are a highly promising technology for data-intensive applications as they benefit from multi-dimensional manipulation of optical signals. However, scaling these switches normally poses stringent challenges such as increased fabrication complexity and control difficulties, due to the growing number of switching elements. In this work, we propose a new type of dilated crosspoint topology, which efficiently handles both space and wavelength selective switching, while reducing the required switching element count by an order of magnitude compared to reported designs. To the best of our knowledge, our design requires the fewest switching elements for an equivalent routing paths number and it fully cancels the first-order in-band crosstalk. We demonstrate such an ultra-compact space-and-wavelength-selective switch (SWSS) at a scale of $4\times 4\times 4\lambda$ on the silicon-on-insulator (SOI) platform. Experimental results reveal that the switch achieves an insertion loss ranging from 2.3 dB to 8.6 dB and crosstalk levels in between -35.3 dB and -59.7 dB. The add-drop microring-resonators (MRRs) are equipped with micro-heaters, exhibiting a rise and fall time of 46 $\mu$s and 0.33 $\mu$s, respectively. These performance characteristics highlight the switch's ultra-low element count and crosstalk with low insertion loss, making it a promising candidate for advanced data center applications.

physics.optics↗

arXiv2024

Ultra-low-crosstalk Silicon Switches Driven Thermally and Electrically

Silicon photonic switches are widely considered as a cost-effective solution for addressing the ever-growing data traffic in datacenter networks, as they offer unique advantages such as low power consumption, low latency, small footprint and high bandwidth. Despite extensive research efforts, crosstalk in large-scale photonic circuits still poses a threat to the signal integrity. In this paper, we present two designs of silicon Mach-Zehnder Interferometer (MZI) switches achieving ultra-low-crosstalk, driven thermally and electrically. Each switch fabric is optimized at both the device and circuit level to suppress crosstalk and reduce system complexity. Notably, for the first time to the best of our knowledge, we harness the inherent self-heating effect in a carrier-injection-based MZI switch to create a pair of phase shifters that offer arbitrary phase differences. Such a pair of phase shifters induces matched insertion loss at each arm, thus minimizing crosstalk. Experimentally, an ultra-low crosstalk ratio below -40 dB is demonstrated for both thermo-optic (T-O) and electro-optic (E-O) switches. The T-O switch exhibits an on-chip loss of less than 5 dB with a switching time of 500 microseconds, whereas the E-O switch achieves an on-chip loss as low as 8.5 dB with a switching time of under 100 ns. In addition, data transmission of a 50 Gb/s on-off keying signal is demonstrated with high fidelity on the E-O switch, showing the great potential of the proposed switch designs.

physics.optics↗

arXiv2024

Chip-scale sensor for spectroscopic metrology

Miniaturized spectrometers hold great promise for in situ, in vitro, and even in vivo sensing applications. However, their size reduction imposes vital performance constraints in meeting the rigorous demands of spectroscopy, including fine resolution, high accuracy, and ultra-wide observation window. The prevailing view in the community holds that miniaturized spectrometers are most suitable for the coarse identification of signature peaks. In this paper, we present an integrated reconstructive spectrometer that enables near-infrared (NIR) spectroscopic metrology, and demonstrate a fully packaged sensor with auxiliary electronics. Such a sensor operates over a 520 nm bandwidth together with a resolution of less than 8 pm, which translates into a record-breaking bandwidth-to-resolution ratio of over 65,000. The classification of different types of solid substances and the concentration measurement of aqueous and organic solutions are performed, all achieving approximately 100% accuracy. Notably, the detection limit of our sensor matches that of the commercial benchtop counterparts, which is as low as 0.1% (i.e. 100 mg/dL) for identifying the concentration of glucose solution.

physics.optics↗

arXiv2024

Asymmetrical estimator for training encapsulated deep photonic neural networks

Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs). The truncated photonic signal propagation and the computation overhead bottleneck DPNN's operation efficiency and increase system construction cost. Here, we introduce the asymmetrical training (AsyT) method, tailored for encapsulated DPNNs, where the signal is preserved in the analogue photonic domain for the entire structure. AsyT offers a lightweight solution for DPNNs with minimum readouts, fast and energy-efficient operation, and minimum system footprint. AsyT's ease of operation, error tolerance, and generality aim to promote PNN acceleration in a widened operational scenario despite the fabrication variations and imperfect controls. We demonstrated AsyT for encapsulated DPNN with integrated photonic chips, repeatably enhancing the performance from in-silico BP for different network structures and datasets.

cs.LG↗

arXiv2024

Unlocking Electro-optic Resonant Phase Shifting for Multi-dimensional, Ultra-dynamic Photonic Switches

Optical circuit switching is connection-oriented, being deterministic through the reservation of a complete wavelength channel or spatial path for a certain period. However, this comes at a trade-off against link dynamics, and overall capacity can thus be constrained by the time slot reservations, especially for switches with microsecond- to millisecond-scale reconfiguration times. For data-intensive applications, the communication patterns associated with random data sets typically yield short-lived flows. This situation calls for a new multi-dimensional switching paradigm that fully exploits not only the space and wavelength domains but also with nanosecond-scale reconfigurable capability in the time domain to enable ultra-dynamic links. In this work, we focus on the exploitation of micro-ring resonant phase shifters (RPSs) that are wavelength selective for optical switching in a single plane. By proposing an innovative analytical method with transmission circle chart, we fully unlock the power of RPS with nanosecond-scale reconfigurability and the capability to arbitrarily manipulate its phase and amplitude. Such a compact model offers fresh insights into designs with under and critically coupled RPSs beyond the commonly explored over-coupling condition. This creates not only versatile switch elements but also perfect absorbers for robust multi-wavelength operations. The proposed device can bring about a breakthrough in the optical switching capacity that potentially addresses the challenges faced by modern data center networks, as well as other photonic circuits for high-throughput signal processing.

physics.optics↗

arXiv2024

Benchmarking reconstructive spectrometer with multi-resonant cavities

Recent years have seen the rapid development of miniaturized reconstructive spectrometers (RSs), yet they still confront a range of technical challenges, such as bandwidth/resolution ratio, sensing speed, and/or power efficiency. Reported RS designs often suffer from insufficient decorrelation between sampling channels, which results in limited compressive sampling efficiency, in essence, due to inadequate engineering of sampling responses. This in turn leads to poor spectral-pixel-to-channel ratios (SPCRs), typically restricted at single digits. So far, there lacks a general guideline for manipulating RS sampling responses for the effectiveness of spectral information acquisition. In this study, we shed light on a fundamental parameter from the compressive sensing theory - the average mutual correlation coefficient v - and provide insight into how it serves as a critical benchmark in RS design with regards to the SPCR and reconstruction accuracy. To this end, we propose a novel RS design with multi-resonant cavities, consisting of a series of partial reflective interfaces. Such multi-cavity configuration offers an expansive parameter space, facilitating the superlative optimization of sampling matrices with minimized v. As a proof-of-concept demonstration, a single-shot, dual-band RS is implemented on a SiN platform, tailored for capturing signature spectral shapes across different wavelength regions, with customized photonic crystal nanobeam mirrors. Experimentally, the device demonstrates an overall operation bandwidth of 270 nm and a <0.5 nm resolution with only 15 sampling channels per band, leading to a record high SPCR of 18.0. Moreover, the proposed multi-cavity design can be readily adapted to various photonic platforms. For instance, we showcase that by employing multi-layer coatings, an ultra-broadband RS can be optimized to exhibit a 700 nm bandwidth with an SPCR of over 100.

physics.optics↗

arXiv2024

Photonic Chiplet Interconnection via 3D-Nanoprinted Interposer

Photonic integrated circuits utilize various waveguide materials, each excelling in specific metrics like efficient light emission, low propagation loss, high electro-optic efficiency, and potential for mass production. Inherent shortcomings in each platform push exploration of hybrid and heterogeneous integration, which demands specialized designs and extra fabrication processes for each material combination. Our work introduces a novel hybrid integration scheme employing a 3D-nanoprinted interposer for a photonic chiplet interconnection system. This method represents a generic solution that can readily couple between chips of any material system, with each fabricated on its own technology platform with no change in the established process flow for the individual chips. Mode-size engineering is enhanced by the off-chip parabolic micro-reflectors. The 3D-nanoprinted chip-coupling frame and fiber-guiding funnel enable low-loss, fully passive assembly with a fast-printing process achieving sub-micron accuracy. Mode-field-dimension conversion ratio of 5:2 from fiber to chip is demonstrated with <0.5dB excess loss on top of the 1.7dB inherent coupling loss, marking the largest mode size conversion using non-waveguided components. Additionally, our system demonstrates a 2.5dB die-to-die coupling loss between silicon and InP chips over a 140nm wavelength range (1480nm to 1620nm), showcasing the potential for extensive cross-platform integration by bridging different waveguide materials.

physics.optics↗

arXiv2023

Redesigning spectroscopic sensors with programmable photonic circuits

Optical spectroscopic sensors are a powerful tool to reveal light-matter interactions in many fields, such as physics, biology, chemistry, and astronomy. Miniaturizing the currently bulky spectrometers has become imperative for the wide range of applications that demand in situ or even in vitro characterization systems, a field that is growing rapidly. Benchtop spectrometers are capable of offering superior resolution and spectral range, but at the expense of requiring a large size. In this paper, we propose a novel method that redesigns spectroscopic sensors via the use of programmable photonic circuits. Drawing from compressive sensing theory, we start by investigating the most ideal sampling matrix for a reconstructive spectrometer and reveal that a sufficiently large number of sampling channels is a prerequisite for both fine resolution and low reconstruction error. This number is, however, still considerably smaller than that of the reconstructed spectral pixels, benefitting from the nature of reconstruction algorithms. We then show that the cascading of a few engineered MZI elements can be readily programmed to create an exponentially scalable number of such sampling spectral responses over an ultra-broad bandwidth, allowing for ultra-high resolution down to single-digit picometers without incurring additional hardware costs. Experimentally, we implement an on-chip spectrometer with a fully-programmable 6-stage cascaded MZI structure and demonstrate a < 10 pm resolution with a > 200 nm bandwidth using only 729 sampling channels. This achieves a bandwidth-to-resolution ratio of over 20,000, which is, to our best knowledge, about one order of magnitude greater than any reported miniaturized spectrometers to date. We further illustrate that by employing dispersion-engineered waveguide components, the device bandwidth can be extended to over 400 nm.

physics.optics↗

arXiv2023

I/O-efficient iterative matrix inversion with photonic integrated circuits

Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed bottleneck of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a novel photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted data in the optical domain unlocks the potential to break the IO bottleneck. We demonstrate notable IO advantages with a lossless PIP for real-valued matrix inversion and integral-differential equation solving, as well as a coherent PIP with optical loops integrated on-chip, enabling complex-valued computation and a net inversion time of 1.2 ns. Furthermore, we estimate at least an order of magnitude enhancement in IO efficiency of a PIP over photonic single-pass processors and the state-of-the-art electronic processors for reservoir training tasks and MIMO precoding tasks, indicating the huge potential of PIP technology in practical applications.

cs.ET↗

arXiv2021

Integrated Kerr frequency comb-driven silicon photonic transmitter

The exponential growth of computing needs for artificial intelligence and machine learning has had a dramatic impact on data centre energy consumption, which has risen to environmentally significant levels. Using light to send information between compute nodes can dramatically decrease this energy consumption while simultaneously increasing bandwidth. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. While previous demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data centre interconnects require a compact on-chip form factor for these operations. Here, we demonstrate the first integrated silicon photonic transmitter using a Kerr comb source. The demonstrated architecture is scalable to hundreds of wavelength channels, enabling a fundamentally new class of massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

physics.app-ph↗

arXiv2017

Faster-than-Nyquist Non-Orthogonal Frequency-Division Multiplexing for Visible Light Communications

In this paper, we propose a faster-than-Nyquist (FTN) non-orthogonal frequency-division multiplexing (NOFDM) scheme for visible light communications (VLC) where the multiplexing/demultiplexing employs the inverse fractional cosine transform (IFrCT)/FrCT. Different to the common fractional Fourier transform-based NOFDM (FrFT-NOFDM) signal, FrCT-based NOFDM (FrCT-NOFDM) signal is real-valued which can be directly applied to the VLC systems without the expensive upconversion. Thus, FrCT-NOFDM is more suitable for the cost-sensitive VLC systems. Meanwhile, under the same transmission rate, FrCT-NOFDM signal occupies smaller bandwidth compared to OFDM signal. When the bandwidth compression factor $α$ is set to $0.8$, $20\%$ bandwidth saving can be obtained. Therefore, FrCT-NOFDM has higher spectral efficiency and suffers less high-frequency distortion compared to OFDM, which benefits the bandwidth-limited VLC systems. As the simulation results show, bit error rate (BER) performance of FrCT-NOFDM with $α$ of $0.9$ or $0.8$ is better than that of OFDM. Moreover, FrCT-NOFDM has a superior security performance. In conclusion, FrCT-NOFDM shows great potential for application in the future VLC systems.

cs.IT↗