arXiv subjects

Christophe Moser

Publications and source records attributed to Christophe Moser.

At least 19 recordsLinked to original sources

arXiv2026

Overcoming Scattering in High-Cell-Density Tomographic Volumetric Bioprinting Using Computational Light Optimization

Tomographic volumetric additive manufacturing has emerged as a transformative 3D printing technology for rapidly fabricating complex geometries. It offers significant advantages for bioprinting due to its contactless and short process time (a few tens of seconds). However, the presence of high cell densities ($>10^7$ cells mL-1) in bioresins introduces substantial light scattering, which degrades printing resolution and fidelity, hindering the fabrication of biologically relevant microstructures such as vascular channels and cavities. To address this challenge, we utilize a computational patterning framework leveraging physically based inverse rendering to optimize light delivery in scattering environments. This method iteratively refines tomographic projections by simulating light-matter interactions in cell-laden hydrogels, enabling precise compensation for scattering effects. Experimental results demonstrate that our approach achieves 500 ${\mu}m$ diameter vascular channels at $2*10^7$ cells mL-1. Furthermore, we integrate this computational method with refractive index matching strategies, reducing scattering artifacts by minimizing optical mismatch between cells and the hydrogel matrix, enabling printing at $4.1*10^7$ cells mL-1. The compatibility of these dual strategies enables unprecedented print fidelity in turbid bioresins. This advancement expands the scope of tomographic volumetric additive manufacturing for engineering functional tissues with intricate microarchitectures.

physics.optics↗

arXiv2026

Too Big, Too Small, Too $O_2$: The Pandoro Effect from Oxygen Gradients in Tomographic Volumetric Additive Manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) enables rapid, layerless biofabrication; however, its application to thermoreversible hydrogels is often compromised by complex chemical kinetics. In this study, we identify and characterize a recurrent printing artifact - termed the Pandoro effect - manifesting as a truncated-cone distortion caused by premature polymerization at the vial bottom and inhibition at the top. We demonstrate that this phenomenon originates from a vertical oxygen gradient driven by the thermal hysteresis of resin preparation: heating depletes dissolved oxygen, while subsequent cooling induces diffusion-limited re-oxygenation from the air-resin interface. To mitigate this, we present a multi-tiered strategy. First, we introduce a coupled ray-optical and photochemical optimization model that rigorously accounts for spatially heterogeneous inhibitor concentrations. Unlike conventional threshold-based approaches, this differentiable framework explicitly simulates the spatiotemporal reaction-diffusion dynamics of oxygen depletion, allowing the inverse solver to predictively compensate for local inhibition gradients. Complementing this algorithmic correction, we validate two process-based interventions: the elimination of the air-resin interface and the control of headspace atmosphere. We demonstrate that these strategies effectively suppress the Pandoro effect, and are compatible with cell-laden resins. This work establishes guidelines for reproducible volumetric bioprinting and expands our open-source Dr.TVAM platform with advanced polymerization modeling capabilities.

physics.optics↗

arXiv2026

Single-View Holographic Volumetric 3D Printing with Coupled Differentiable Wave-Optical and Photochemical Optimization

Volumetric additive manufacturing promises near-instantaneous fabrication of 3D objects, yet achieving high fidelity at the micro-scale remains challenging due to the complex interplay between optical diffraction and chemical effects. We present \emph{Single-View Holographic Volumetric Additive Manufacturing} (SHVAM), a mechanically static system that shapes volumetric dose distributions using time-multiplexed, phase-only holograms projected from a single optical axis. To achieve high resolution with SHVAM, we formulate hologram synthesis as a coupled inverse problem, integrating a differentiable wave-optical forward model with a simplified photochemical model that explicitly captures inhibitor diffusion and non-linear dose response. Optimizing hologram sequences under these coupled constraints allows us to pre-compensate for chemical blur, yielding higher print fidelity than optical-only optimization. We demonstrate the efficacy of SHVAM by fabricating simple 2D and 3D structures with lateral feature sizes of approximately \SI{10}{\micro\meter} within a $\SI{0.8}{\milli\meter} \times \SI{0.8}{\milli\meter} \times \SI{3}{\milli\meter}$ volume in seconds.

physics.optics↗

arXiv2026

A unified multiscale 3D printer combining single-photon Tomographic Volumetric Additive Manufacturing and Two-Photon Polymerization

Single-photon polymerization ensures rapid photopolymerization of centimeter-scale structures with features on the order of tens of micrometers, whereas 2PP provides sub-micrometer features at sub-millimeter scales. Existing hybrid approaches combining these techniques typically rely on stitched or layer-by-layer fabrication and often require separate printing platforms, making mesoscale manufacturing time-consuming. Here, we introduce a hybrid unified 3D printer that leverages the complementary strengths of both printing mechanisms to bridge this scale resolution-fabrication time gap. We propose integrating 2PP for high-resolution, localized spatial control with single-photon TVAM for enabling rapid, high-throughput 3D fabrication. In this approach, TVAM first forms millimeter-scale volumetric structures attached on a glass rod, via overprinting, which is then accessible, on the same platform, for subsequent high-resolution 2PP. Without needing to change the photoresin or introducing intermediate post-processing steps, we proceed to demonstrate finely printed structures via 2PP, fabricated both inside (embedded within) and on the surface of the millimeter-scale 3D objects printed with TVAM. Here, TVAM contributes in two distinct ways: by generating a pre-polymerized volume that facilitates subsequent 2PP, and by directly driving layer-less volumetric polymerization in designated regions within seconds. We experimentally demonstrate that this dual-mode strategy provides a mesoscale approach spanning three orders of magnitude in scale for rapid fabrication of millimeter-scale structures featuring 830 nm details. For applications such as micro-optics, biomedical scaffolds and tissue engineering, tens-of-micrometer features are sufficient across the majority of the volume, with higher resolution confined to localized functional regions.

physics.optics↗

arXiv2025

High resolution Fluorescence lifetime IMaging Micro-Endoscopy (FLIMME) using a single multimode fiber

Endoscopic optical imaging using a single multimode fiber (MMF) has emerged as a promising approach for highly compact, minimally invasive, and high-resolution imaging. Unlike conventional fiber bundles, MMF-based endomicroscopes exploit the controlled excitation of multiple spatially overlapping modes in a single MMF. of core diameters of tens of micrometers. to deliver and collect light to form images with sub-micrometer resolution. Here, we introduce a fluorescence lifetime imaging microscopy (FLIM) modality to the MMF endomicroscope. We use amplitude modulation of a 405 nm single-mode light source at radio frequency (RF) and lock-in detection of autofluorescence to obtain intensity and lifetime images at sub-micrometer resolution. We experimentally demonstrate the capability of the ultrathin endomicroscope to perform label-free imaging in thick ex vivo murine submandibular gland tissue. With a temporal resolution of 0.03 ns, the FLIM images show distinguished structures of lifetime differences down to 0.5 ns. The combination of sub-micrometer fluorescence intensity and lifetime images in a minimally invasive endomicroscope opens new avenues for label-free cancer detection.

physics.optics↗

arXiv2025

Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies

Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S - unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accuracy with only public sensor data, SolarCAST models three classes of confounding factors behind X-S correlations using scalable neural components: (i) observable synchronous variables (e.g., time of day, station identity), handled via an embedding module; (ii) latent synchronous factors (e.g., regional weather patterns), captured by a spatio-temporal graph neural network; and (iii) time-lagged influences (e.g., cloud movement across stations), modeled with a gated transformer that learns temporal shifts. It outperforms leading time-series and multimodal baselines across diverse geographical conditions, and achieves a 25.9% error reduction over the top commercial forecaster, Solcast. SolarCAST offers a lightweight, practical, and generalizable solution for localized solar forecasting.

cs.LG↗

arXiv2025

Overprinting with Tomographic Volumetric Additive Manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) is a light-based 3D printing technique capable of producing centimeter-scale objects within seconds. A key challenge lies in the calculation of tomographic projection patterns under non-standard conditions, such as the presence of occlusions and materials with diverse optical properties, including varying refractive indices or scattering surfaces. This work demonstrates a broad range of overprinting scenarios, where new structures are directly printed onto or around pre-existing components made from different materials. Our simulations and experimental verifications perform overprinting of absorbing, refracting, reflecting and scattering elements in both round and square vials. All scenarios are optimized with our differentiable, physically based ray-optics approach using the open-source Dr.TVAM framework, delivering high-quality projections for both laser- and LED-based illuminations within minutes and lower-quality projections within seconds, exceeding existing open-source solutions in speed, flexibility, and quality.

physics.optics↗

arXiv2025

High Light-Efficiency Holographic Tomographic Volumetric Additive Manufacturing using a MEMS-based Phase-only Light Modulator

Light-based 3D printing, which relies on photocurable resins, has shown the capability to produce complex geometries with high resolution and fidelity. Tomographic Volumetric Additive Manufacturing (TVAM) employs a digital micromirror device (DMD) to project high-speed sequences of amplitude light patterns into a rotating resin volume, enabling rapid fabrication of 3D structures through photopolymerization. Typically, the light projection efficiency in such binary amplitude modulator-based systems is below a few percent. Recent advancements introduced phase encoding in TVAM using binary amplitude modulators, improving depth control and boosting light projection efficiency to approximately 10%. This was achieved by implementing the Lee hologram technique to encode phase into binary amplitude patterns. In this work, we present the first 3D printing platform utilizing a phase-only light modulator (PLM), based on an array of micro-electro-mechanical pistons. Compared to amplitude encoding, phase encoding with the PLM yields a 70-fold increase in laser power efficiency. By coupling this efficient light engine with a speckle reduction method in holographic volumetric additive manufacturing (HoloVAM), we experimentally demonstrate printing across different scales from hundreds of micrometers to centimeters using only digital control. The PLM opens up new avenues in volumetric AM for holographic techniques using low-cost single-mode UV laser diodes.

physics.optics↗

arXiv2025

Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series

Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performance; and 3) a lightweight multimodal model, called Solar Multimodal Transformer (SMT), that delivers accurate short-term solar irradiance forecasting by combining images and scaled time series. Benchmarking against Solcast, a leading solar forecasting service provider, our model improved prediction accuracy by 25.95%. Our approach allows for easy adaptation to various camera specifications, offering broad applicability for real-world solar forecasting challenges.

cs.CV↗

arXiv2025

Roadmap on Neuromorphic Photonics

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

cs.ET↗

arXiv2025

Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the complexity of trainable layers. Here, we utilize ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations, for this purpose. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity. Moreover, the framework demonstrates exceptional resilience to experimental drifts. By integrating low-energy physical systems into neural networks, this approach enables scalable, energy-efficient AI models with significantly reduced computational demands.

physics.optics↗

arXiv2024

Optical Diffusion Models for Image Generation

Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam through a semi-transparent medium can be programmed to implement a denoising diffusion model on image samples. This framework projects noisy image patterns through passive diffractive optical layers, which collectively only transmit the predicted noise term in the image. The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising. Hence this method enables high-speed image generation with minimal power consumption, benefiting from the bandwidth and energy efficiency of optical information processing.

physics.optics↗

arXiv2024

Training of Physical Neural Networks

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models.

physics.app-ph↗

arXiv2024

Programming the scalable optical learning operator with spatial-spectral optimization

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9% classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99% in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations.

physics.optics↗

arXiv2024

Wave optical model for tomographic volumetric additive manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. Tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimization is based on a ray optical assumption which ultimately leads to limited resolution around $20\mu\textrm{m}$ and varying throughout the volume of the 3D object. In this work, we introduce a rigorous wave-based optical amplitude optimization scheme for TVAM which shows that high-resolution printing is theoretically possible over the full volume. The wave optical optimization approach is based on an efficient angular spectrum method of plane waves with custom written memory efficient gradients and allows for optimization of realistic volumes for TVAM such as $(100\mu\textrm{m})^3$ or $(10\textrm{mm})^3$ with $550^3$ voxels and 600 angles. Our simulations show that ray-optics start to produce artifacts when the desired features are $20\mu\textrm{m}$ and below and more importantly, the amplitude modulated TVAM can reach micrometer features when optimizing the patterns using a full wave model.

physics.optics↗

arXiv2024

Multicasting Optical Reconfigurable Switch

Artificial Intelligence (AI) demands large data flows within datacenters, heavily relying on multicasting data transfers. As AI models scale, the requirement for high-bandwidth and low-latency networking compounds. The common use of electrical packet switching faces limitations due to optical-electrical-optical conversion bottlenecks. Optical switches, while bandwidth-agnostic and low-latency, suffer from having only unicast or non-scalable multicasting capability. This paper introduces an optical switching technique addressing this challenge. Our approach enables arbitrarily programmable simultaneous unicast and multicast connectivity, eliminating the need for optical splitters that hinder scalability due to optical power loss. We use phase modulation in multiple layers, tailored to implement any multicast connectivity map. Phase modulation also enables wavelength selectivity on top of spatial selectivity, resulting in an optical switch that implements space-wavelength routing. We conducted simulations and experiments to validate our approach. Our results affirm the concept's feasibility, effectiveness, and scalability, as a multicasting switch by experimentally demonstrating 16 spatial ports using 2 wavelength channels. Numerically, 64 spatial ports with 4 wavelength channels each were simulated, with approximately constant efficiency (< 3 dB) as ports and wavelength channels scale.

physics.optics↗

arXiv2024

Holographic Volumetric Additive Manufacturing

3D printing has revolutionized the manufacturing of volumetric components and structures in many areas. Different technologies have been developed including light-induced techniques based on the photopolymerization of liquid resins. In particular, a recently introduced method, so-called Tomographic Volumetric AM (VAM), allows the fabrication of mesoscale objects within tens of seconds without the need for support structures. This method works by projecting thousands of amplitude patterns, computed via a reverse tomography algorithm, into a resin from different angles to produce the desired three-dimensional shape when the resin reaches the polymerization threshold. To date, only amplitude modulation of the patterns has been reported. Here, we show that holographic phase modulation unlocks new capabilities for VAM printing. Specifically, the effective light projection efficiency is improved by at least a factor of 10 over amplitude coding; the resolution can reach the light diffraction limit; and phase encoding allows to control ballistic photons in scattering media, which potentially increases the volume of 3D objects that can be printed in opaque and non-absorbing resins. The approach uses CGH to convert phase, encoded on a 2D modulator to the desired intensity projections by light propagation in a photosensitive resin container. We demonstrate the potential of holographic phase coding using simulations and experiments, the latter by implementing a volumetric printer using a DMD, as the 2D phase modulator in a Fourier configuration. Specifically, we use Lee holograms to encode phase onto a binary DMD. Combining tiled holograms with PSF shaping mitigates the speckle noise typically associated with computer-generated holograms and speed-up their computation. We use these holographic projections to fabricate millimetric 3D objects in less than a minute with a resolution down to 164 um.

physics.optics↗

arXiv2024

Single-Photon-Assisted Two-Photon Polymerization

Light-based additive manufacturing (AM) has revolutionized the fabrication of complex three-dimensional (3D) objects offering a cost-effective and high-speed alternative to traditional machining. One-photon polymerization is a key process in this advancement, standing out for rapid printing time, albeit with limited resolution. Two-photon polymerization (2PP) empowers AM with unprecedented resolution but is accompanied by a tradeoff of prolonged printing times. We propose combining the single-photon absorption (1PA) and 2PP to benefit from the dual capabilities, allowing for faster printing while maintaining high resolution and improved depth sectioning, respectively. In this study, we employ a blue light source to pre-excite a photocurable resin by 1PA followed by a precisely focused femtosecond (fs) beam to provide the missing energy necessary to reach the polymerization threshold to solidify the resin through two-photon absorption. First, we investigate the impact of pre-sensitization by blue light illumination on 2PP and demonstrate one order of magnitude faster printing time for a voxel size of 150 nm as compared to the same voxel size printed by 2PP only. Then, we build a custom 2PP printer utilizing blue light sensitization in a light-sheet mode and demonstrate successful 3D prints.

physics.optics↗