arXiv subjects

Nathan Youngblood

Publications and source records attributed to Nathan Youngblood.

At least 19 recordsLinked to original sources

arXiv2026

Cryogenic thermo-optic response of low-loss phase change material for non-volatile photonic phase shifter

Phase change materials (PCMs) can enable non-volatile optical memory through the large refractive index and extinction coefficient contrast between crystalline and amorphous states. Among them, $\mathrm{Sb}_{2}\mathrm{Se}_{3}$ combines low optical attenuation at telecom wavelengths, making it promising for low-loss programmable phase shifters. Its non-volatility is particularly useful for cryogenic systems, where power dissipation and thermal load constrain scalability. However, its optical properties at cryogenic temperatures remains poorly understood. Here, we report the first cryogenic optical characterization of $\mathrm{Sb}_{2}\mathrm{Se}_{3}$ integrated on a foundry silicon photonic platform from 4K to 300K for both crystalline and amorphous states. We observe that the magnitude of the thermo-optic coefficient decreases upon cooling, whereas the optical attenuation changes only weakly for both the amorphous and crystalline phases. We report the stability of the material upon repeated thermal cycling. These measurements provide the material parameters and stability required to design low-loss, non-volatile photonic memory elements for scalable cryogenic information processing.

physics.optics↗

arXiv2026

Counterdirectional Exciton and Trion Motion in Applied Electric Field

Charged excitonic complexes are central to the optoelectronic and many-body properties of semiconductors, yet their real-space transport dynamics remain largely unexplored. Here, we report the direct optical observation of trion motion under an applied electric field. The trions exhibit electrically driven drift with velocities approaching {$10^5~\mathrm{m/s}$}. Unexpectedly, the trion flow induces a pronounced back-action on coexisting neutral excitons, driving them in the opposite direction and giving rise to counterpropagating exciton-trion transport. Our results reveal an interaction-driven nonequilibrium transport regime of mixed excitonic fluids and establish a direct route for imaging the dynamics of more complex charged quasiparticles, including doubly charged excitons.

physics.optics↗

arXiv2026

Distributed Coherent Optical Computing via Injection-Locked Photonic Networks

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such strategies when optical data is generated at a remote source due to environmental phase variations in fiber. Conventional approaches to distributed computing rely on optical-to-electrical conversion and buffering, limiting truly real-time and distributed computation. Here, we propose a new strategy via optical injection locking to enable distributed, real-time coherent optical processing without unnecessary conversions in the optical-to-electrical or analog-to-digital domains. Using a semiconductor laser rate-equation model, we explore the conditions required for stable operation by sweeping the power injection ratio, frequency detuning, and modulation conditions of the remote and injected lasers. Our results indicate that higher injection powers broaden the locking margin but more readily exhibit frequency-selective features associated with relaxation oscillations and increased amplitude-phase mixing, whereas lower injection powers yield a narrower, but more predictable operating window which remains stable under large modulation depth. End-to-end symbol-sequence simulations with balanced detection and temporal integration further confirm that reducing the injection ratio suppresses residual remote-modulation components in the injected laser output and improves computational accuracy. Overall, our study provides guidance and design trade-offs for remote coherent detection and distributed coherent photonic computing enabled by injection locking.

physics.optics↗

arXiv2026

Ultra-Thin Aluminum-Doped Silver for Transmissive Thermally Reconfigurable Visible Photonics

Functional materials with high electrical conductivity and optical transmittance are vital for thermally tunable free-space photonic systems. Conventional transparent conductors such as graphene and indium tin oxide are limited by high contact resistance, poor mechanical stability, or complex fabrication. Ultra-thin metals, such as pure silver, have also been explored with limited success due to thermal instability and dewetting. Here, we propose an ultra-thin Al-doped Ag film to tackle these challenges. Aluminum promotes heterogeneous nucleation of silver, enabling the formation of continuous, smooth films that are thermally stable at reduced thicknesses while maintaining excellent electrical conductivity and transparency. We find that a 12 nm Al-doped Ag film exhibits an average transmittance of 80% across the visible range with a sheet resistance of 8.3$\pm$1.16 $Ω$cm$^2$. Moreover, on-chip Al-doped Ag microheaters exhibit uniform, rapid thermal response, and stable electrical performance, maintaining functionality for over $10^7$ ON and OFF cycles at temperatures below 400$°$C. Furthermore, as a benchmark, we demonstrate reversible phase-change switching in Ge$_2$Sb$_2$Se$_4$Te (GSST) and VO$_2$. 30$\times$30 $μ$m$^2$ GSST cells exhibited complete crystallization and amorphization under 2.2 V - 200 ms and 4.1V - 50$μ$s pulses, respectively, resulting in a 40% transmission contrast at 780 nm and a tenfold improvement in power consumption compared to similar devices. Additionally, VO$_2$ films displayed reversible insulator-to-metal transitions near 65°C with reflectance and transmittance modulation in the visible and the near-infrared at frequencies up to 25 Hz with room for improvement. These results establish Al-doped Ag as a robust transparent metallic heater for integration in dynamic metasurfaces, optical coatings, and more.

physics.optics↗

arXiv2025

Incorporating Si into Sb2Se3: Tailoring Optical Phase Change Materials via Nanocomposites

Chalcogenide-based optical phase change materials (OPCMs) exhibit a large contrast in refractive index when reversibly switched between their stable amorphous and crystalline states. OPCMs have rapidly gained attention due to their versatility as nonvolatile amplitude or phase modulators in various photonic devices. However, open challenges remain, such as achieving reliable response and transparency spanning into the visible spectrum, a combination of properties in which current broadband OPCMs (e.g., Ge2Sb2Se4Te1, Sb2Se3, or Sb2S3) fall short. Discovering novel materials or engineering existing ones is, therefore, crucial in extending the application scope of OPCMs. Here, we use magnetron co-sputtering to study the effects of Si doping into Sb2Se3. We employ ellipsometry, X-ray diffraction, Raman spectroscopy, and scanning and transmission electron microscopy to investigate the effects of Si doping on the optical properties and crystal structure and compare these results with those from first principles calculations. Moreover, we study the crystallization and melt-quenching of thin films via nano-differential scanning calorimetry (NanoDSC). Our experiments demonstrate that 20% Si doping increases the transparency window in both states, specifically to 800 nm (1.55 eV) in the amorphous phase, while reducing power consumption by lowering the melting temperature. However, this reduction comes at the cost of reducing the refractive index contrast between states and slowing the kinetics of the phase transition.

cond-mat.mtrl-sci↗

arXiv2025

Fully analog end-to-end online training with real-time adaptibility on integrated photonic platform

Analog neuromorphic photonic processors are uniquely positioned to harness the ultrafast bandwidth and inherent parallelism of light, enabling scalability, on-chip integration and significant improvement in computational performance. However, major challenges remain unresolved especially in achieving real-time online training, efficient end-to-end anolog systems, and adaptive learning for dynamical environmental changes. Here, we demonstrate an on-chip photonic analog end-to-end adaptive learning system realized on a foundry-manufactured silicon photonic integrated circuit. Our platform leverages a multiplexed gradient descent algorithm to perform in-situ, on-the-fly training, while maintaining robustness in online tracking and real-time adaptation. At its core, the processor features a monolithic integration of a microring resonator weight bank array and on-chip photodetectors, enabling direct optical measurement of gradient signals. This eliminates the need for high-precision digital matrix multiplications, significantly reducing computational overhead and latency, an essential requirement for effective online training. We experimentally demonstrate real-time, end-to-end analog training for both linear and nonlinear classification tasks at gigabaud rates, achieving accuracies of over 90\% and 80\%, respectively. Our analog neuromorphic processor introduces self-learning capabilities that dynamically adjust training parameters, setting the stage for truly autonomous neuromorphic architectures capable of efficient, real-time processing in unpredictable real-world environments. As a result, we showcase adaptive online tracking of dynamically changing input datasets and achieve over 90\% accuracy, alongside robustness to external temperature fluctuations and internal thermal crosstalk.

physics.optics↗

arXiv2025

Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments

Real-world analog systems intrinsically suffer from noise that can impede model convergence and accuracy on a variety of deep learning models. We demonstrate that differentiable activations like GELU and SiLU enable robust propagation of gradients which help to mitigate analog quantization error that is ubiquitous to all analog systems. We perform analysis and training of convolutional, linear, and transformer networks in the presence of quantized noise. Here, we are able to demonstrate that continuously differentiable activation functions are significantly more noise resilient over conventional rectified activations. As in the case of ReLU, the error in gradients are 100x higher than those in GELU near zero. Our findings provide guidance for selecting appropriate activations to realize performant and reliable hardware implementations across several machine learning domains such as computer vision, signal processing, and beyond. Code available at: \href{https://github.com/Vivswan/GeLUReLUInterpolation}{https://github.com/Vivswan/GeLUReLUInterpolation}.}

cs.LG↗

arXiv2025

Enabling High-Bandwidth Coherent Modulation Through Scalable Lithium Niobate Resonant Devices

We present a compact, resonant-based coherent modulator on a thin-film lithium niobate (TFLN) platform, addressing the growing demand for high-speed, energy-efficient modulators in modern telecommunications. The design incorporates Mach-Zehnder Interferometers (MZIs) with a Gires-Tournois etalon in each arm with a modulation region of only ~80 micrometers, eliminating the need for traveling-wave electrodes and enabling compatibility with wavelength-division multiplexing (WDM). Experimental results demonstrate a modulation bandwidth of 29 GHz, while ensuring low optical loss and high scalability. Our architecture supports in-phase and out-of-phase modulation, enabling differential control of amplitude and phase for advanced modulation formats such as quadrature amplitude modulation (QAM). Compared to previous designs, our approach enhances throughput, modulation density, and scalability, making it ideal for applications in coherent communications and optical computing. By combining the advantages of the TFLN platform with innovative resonator engineering, this work advances the development of compact, high-performance modulators for high-density on-chip communication networks.

physics.optics↗

arXiv2024

Light-Induced Electron Pairing in a Bilayer Structure

Previous experimental and theoretical work has given evidence of the existence of doubly charged exciton states in strongly screened bilayers of transition metal dichalcogenide (TMD) layers. These complexes are important because they are performed electron pairs that can, in principle, undergo Bose-Einstein condensation (BEC), in which case they would also form a new type of superconductor, consisting of stable bosons with net charges. In this paper, we present key electrostatic and magnetic measurements that definitively confirm the existence of these charged bosons. These measurements include 1) continuous control of the doping density with both positive and negative carriers, showing the expected population dependencies on the free carrier density, and 2) measurement of the dependence on the magnetic field, showing that this new bound state is a spin triplet. These results imply that it is promising to look for BEC and superconductivity in this system.

cond-mat.supr-con↗

arXiv2024

High-Speed Multifunctional Photonic Memory on a Foundry-Processed Photonic Platform

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result showcases a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments.

physics.optics↗

arXiv2024

Microheater hotspot engineering for repeatable multi-level switching in foundry-processed phase change silicon photonics

Nonvolatile photonic integrated circuits employing phase change materials have relied either on optical switching mechanisms with precise multi-level control but poor scalability or electrical switching with seamless integration and scalability but mostly limited to a binary response. Recent works have demonstrated electrical multi-level switching; however, they relied on the stochastic nucleation process to achieve partial crystallization with low demonstrated repeatability and cyclability. Here, we re-engineer waveguide-integrated microheaters to achieve precise spatial control of the temperature profile (i.e., hotspot) and, thus, switch deterministic areas of an embedded phase change material cell. We experimentally demonstrate this concept using a variety of foundry-processed doped-silicon microheaters on a silicon-on-insulator platform to trigger multi-step amorphization and reversible switching of Sb$_{2}$Se$_{3}$ and Ge$_{2}$Sb$_{2}$Se$_{4}$Te alloys. We further characterize the response of our microheaters using Transient Thermoreflectance Imaging. Our approach combines the deterministic control resulting from a spatially resolved glassy-crystalline distribution with the scalability of electro-thermal switching devices, thus paving the way to reliable multi-level switching towards robust reprogrammable phase-change photonic devices for analog processing and computing.

physics.app-ph↗

arXiv2024

OFHE: An Electro-Optical Accelerator for Discretized TFHE

This paper presents \textit{OFHE}, an electro-optical accelerator designed to process Discretized TFHE (DTFHE) operations, which encrypt multi-bit messages and support homomorphic multiplications, lookup table operations and full-domain functional bootstrappings. While DTFHE is more efficient and versatile than other fully homomorphic encryption schemes, it requires 32-, 64-, and 128-bit polynomial multiplications, which can be time-consuming. Existing TFHE accelerators are not easily upgradable to support DTFHE operations due to limited datapaths, a lack of datapath bit-width reconfigurability, and power inefficiencies when processing FFT and inverse FFT (IFFT) kernels. Compared to prior TFHE accelerators, OFHE addresses these challenges by improving the DTFHE operation latency by 8.7\%, the DTFHE operation throughput by $57\%$, and the DTFHE operation throughput per Watt by $94\%$.

cs.CR↗

arXiv2023

Nonvolatile Tuning of Bragg Structures Using Transparent Phase-Change Materials

Bragg gratings offer high-performance filtering and routing of light on-chip through a periodic modulation of a waveguide's effective refractive index. Here, we model and experimentally demonstrate the use of Sb2Se3, a nonvolatile and transparent phase-change material, to tune the resonance conditions in two devices which leverage periodic Bragg gratings: a stopband filter and Fabry-Perot cavity. Through simulations, we show that similar refractive indices between silicon and amorphous Sb2Se3 can be used to induce broadband transparency, while the crystalline state can enhance the index contrast in these Bragg devices. Our experimental results show the promise and limitations of this design approach and highlight specific fabrication challenges which need to be addressed in future implementations.

physics.optics↗

arXiv2023

AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks

AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framework to train and optimize linear and convolutional neural networks with up to 9 layers and ~1.7 million parameters, while gaining insights into how normalization, activation function, reduced precision, and noise influence accuracy in analog photonic neural networks. By following the same layer structure design present in PyTorch, the AnalogVNN framework allows users to convert most digital neural network models to their analog counterparts with just a few lines of code, taking full advantage of the open-source optimization, deep learning, and GPU acceleration libraries available through PyTorch. Code is available at https://analogvnn.github.io

cs.LG↗

arXiv2023

In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ per dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (greater than 87.36) that leads to an enhanced computing accuracy (standard deviation less than 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

physics.app-ph↗

arXiv2022

Time-resolved temperature mapping leveraging the strong thermo-optic effect in phase-change devices

Optical phase-change materials are highly promising for emerging applications such as tunable metasurfaces, reconfigurable photonic circuits, and non-von Neumann computing. However, these materials typically require both high melting temperatures and fast quenching rates to reversibly switch between their crystalline and amorphous phases, a significant challenge for large-scale integration. Here, we present an experimental technique which leverages the thermo-optic effect in GST to enable both spatial and temporal thermal measurements of two common electro-thermal microheater designs currently used by the phase-change community. Our approach shows excellent agreement between experimental results and numerical simulations and provides a non-invasive method for rapid characterization of electrically programmable phase-change devices.

physics.optics↗

arXiv2022

Monadic Pavlovian associative learning in a backpropagation-free photonic network

Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications even though other learning concepts, in particular backpropagation on artificial neural networks (ANNs) have flourished. However, training using the backpropagation method on 'conventional' ANNs, especially in the form of modern deep neural networks (DNNs), is computationally and energy intensive. Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element. We realize this on an integrated photonic platform using phase-change materials combined with on-chip cascaded directional couplers. We then develop a scaled-up circuit network using our monadic Pavlovian photonic hardware that delivers a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free architectures to address general learning tasks. Our approach reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed, whilst also offering higher bandwidth inherent to our photonic implementation.

physics.optics↗

arXiv2020

Parallel convolution processing using an integrated photonic tensor core

With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially increasing amounts of data - data that needs to be processed in a fast, efficient and smart way. These developments are pushing the limits of existing computing paradigms, and highly parallelized, fast and scalable hardware concepts are becoming progressively more important. Here, we demonstrate a computational specific integrated photonic tensor core - the optical analog of an ASIC-capable of operating at Tera-Multiply-Accumulate per second (TMAC/s) speeds. The photonic core achieves parallelized photonic in-memory computing using phase-change memory arrays and photonic chip-based optical frequency combs (soliton microcombs). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 GHz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates, ultra-low loss silicon nitride waveguides, and high speed on-chip detectors and modulators, our approach provides a path towards full CMOS wafer-scale integration of the photonic tensor core. While we focus on convolution processing, more generally our results indicate the major potential of integrated photonics for parallel, fast, and efficient computational hardware in demanding AI applications such as autonomous driving, live video processing, and next generation cloud computing services.

physics.optics↗