arXiv subjects

Christophe Moser

Publications and source records attributed to Christophe Moser.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2023

Light focusing and additive manufacturing through highly scattering media using upconversion nanoparticles

Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form random speckle patterns, making it impossible to print features at the diffraction-limited size with conventional printing approaches. The poor tissue penetration depth of ultra-violet or blue light, which is commonly used to trigger photopolymerization, further limits the fabrication of high cell-density tissue constructs. Recently, several strategies based on wavefront shaping have been developed to manipulate the light and refocus it inside scattering media to a diffraction-limited spot. In this study, we present a high-resolution additive manufacturing technique using upconversion nanoparticles and a wavefront shaping method that does not require measurement from an invasive detector, i.e., it is a non-invasive technique. Upconversion nanoparticles convert near-infrared light to ultraviolet and visible light. The ultraviolet light serves as a light source for photopolymerization and the visible light as a guide star for digital light shaping. The incident light pattern is manipulated using the feedback information of the guide star to focus light through the tissue. In this way, we experimentally demonstrate that near-infrared light can be non-invasively focused through a strongly scattering medium. By exploiting the optical memory effect, we further demonstrate micro-meter resolution additive manufacturing through highly scattering media such as a 300-μm-thick chicken breast. This study provides a proof of concept of high-resolution additive manufacturing through turbid media with potential application in tissue engineering.

physics.optics↗

arXiv2023

Volume holograms with linear diffraction efficiency relation by (3+1)D printing

We demonstrate the fabrication of volume holograms using 2-photon polymerization with dynamic control of light exposure. We refer to our method as (3+1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional with the square of the number of superimposed holograms. By using (3+1)D printing for fabrication, the refractive index of each voxel is created independently and thus by, digitally filtering the undesired interference terms, the diffraction efficiency is now inversely proportional to the number of multiplexed gratings. We experimentally demonstrated this linear dependence by recording M=50 volume gratings. To the best of our knowledge, this is the first experimental demonstration of distributed volume holograms that overcome the 1/M^2 limit.

physics.optics↗

arXiv2023

Forward-Forward Training of an Optical Neural Network

Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple trainable layers in tandem with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced Forward-Forward Algorithm (FFA) eliminates the need for perfect characterization of the learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA, can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing NN performance.

cs.LG↗

arXiv2022

Cavitation induced by pulsed and continuous-wave fiber lasers in confinement

Bubbles generated with lasers under confinement have been investigated for their potential use as the driving mechanism for liquid micro-jets in various microfluidic devices, such as needle free jet injectors. Here, we report on the study of bubble formation by a continuous-wave (CW) and a pulsed laser inside an open-ended microfluidic capillary. This results in a direct comparison between bubbles generated by laser sources emitting light in different time scales (ms and ns). The bubble kinetics represents an important parameter because it determines the available kinetic energy for a subsequent liquid jet. We show that the bubble growth rate increases linearly with the delivered energy for both the CW and the pulsed laser. Experiments show that at equal absorption coefficient, the bubble growth for both lasers is similar, which indicates that they can be used interchangeably for a jet generation. However, bubbles generated by a CW laser require more optical energy, which is due to heat dissipation. Furthermore, the bubbles generated by the CW laser show a slightly larger variation in size and growth rate for identical initial conditions, which we attribute to the stochastic nature of thermocavitation.

physics.flu-dyn↗

arXiv2022

Multi-photon polymerization using upconversion nanoparticles for tunable feature-size printing

The recent development of light-based 3D printing technologies has marked a turning point in additive manufacturing. Through photopolymerization, liquid resins can be solidified into complex objects. Usually, the polymerization is triggered by exciting a photoinitiator with ultraviolet (UV) or blue light. In two-photon printing (TPP), the excitation is done through the non-linear absorption of two photons; it enables printing 100-nm voxels but requires expensive femtosecond lasers which strongly limits their broad dissemination. Upconversion nanoparticles (UCNPs) have recently been proposed as an alternative to TPP for photopolymerization but using continuous-wave lasers. UCNPs convert near-infrared (NIR) into visible/UV light to initiate the polymerization locally as in TPP. Here we provide a study of this multi-photon mechanism and demonstrate how the non-linearity impacts the printing process. In particular, we report on the possibility of fine-tuning the size of the printed voxel by adjusting the NIR excitation intensity. Using gelatin-based hydrogel, we are able to vary the transverse voxel size from 1.3 to 2.8 μm and the axial size from 7.7 to 59 μm by adjusting the NIR power without changing the degree of polymerization. This work opens up new opportunities for speeding up the fabrication while preserving the minimum feature size with cheap light sources.

physics.optics↗

arXiv2022

Volumetric Helical Additive Manufacturing

3D printing has revolutionized the manufacturing of volumetric components and structures for various fields. Thanks to the advent of photocurable resins, several fully volumetric light-based techniques have been recently developed to overcome the current limitations of 3D printing. Although fast, this new generation of printers cannot fabricate objects whose size typically exceeds the centimeter without severely affecting the final resolution. Based on tomographic volumetric additive manufacturing, we propose a method for volumetric helical additive manufacturing (VHAM) multi-cm scale structures without magnifying the projected patterns. It consists of illuminating the photoresist while the latter follows a helical motion. This movement allows for increasing the height of the printable object. Additionally, we off-center the modulator used for projecting the light patterns to double the object's lateral size. We demonstrate experimentally the interest in using these two tricks for printing larger objects (up to 3 cm x 3 cm x 5 cm) while maintaining high resolution (< 200 um) and short print time (< 10 min).

physics.app-ph↗

arXiv2022

Nonlinear Optical Data Transformer for Machine Learning

Modern machine learning models use an ever-increasing number of parameters to train (175 billion parameters for GPT-3) with large datasets to obtain better performance. Bigger is better has been the norm. Optical computing has been reawakened as a potential solution to large-scale computing through optical accelerators that carry out linear operations while reducing electrical power. However, to achieve efficient computing with light, creating and controlling nonlinearity optically rather than electronically remains a challenge. This study explores a reservoir computing (RC) approach whereby a 14 mm long few-mode waveguide in LiNbO3 on insulator is used as a complex nonlinear optical processor. A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide. The output spectrum depends nonlinearly on the input. We experimentally show that a simple digital linear classifier with 784 parameters using the output spectrum from the waveguide as input increased the classification accuracy of several databases compared to non-transformed data, approximately 10$\%$. In comparison, a deep digital neural network (NN) with 40000 parameters was necessary to achieve the same accuracy. Reducing the number of parameters by a factor of $\sim$50 illustrates that a compact optical RC approach can perform on par with a deep digital NN.

physics.optics↗

arXiv2022

Programming Nonlinear Propagation for Efficient Optical Learning Machines

The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation since light propagation through a non-absorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMF) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. In this work, we propose an optical neural network architecture, which performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.

cs.ET↗

arXiv2021

Roadmap on Wavefront Shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

physics.optics↗

arXiv2021

Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases. In this work, we propose a learning procedure to obtain a desired target output from a physical system. We use Variational Auto-Encoders (VAE) to provide a generative model of the system function and use this model to obtain the required input of the system that produces the target output. We showcase the applicability of our method for two datasets in optical physics and neuroscience.

cs.LG↗

arXiv2021

Tomographic Volumetric Additive Manufacturing of Silicon Oxycarbide Ceramics

Ceramics are highly technical materials with properties of interest for multiple industries. Precisely because of their high chemical, thermal, and mechanical resistance, ceramics are difficult to mold into complex shapes. A possibility to make convoluted ceramic parts is to use preceramic polymers (PCP) in liquid form. The PCP resin is first solidified in a desired geometry and then transformed into ceramic compounds through a pyrolysis step that preserves the shape. Light-based additive manufacturing (AM) is a promising route to achieve solidification of the PCP resin. Different approaches, such as stereolithography, have already been proposed but they all rely on a layer-by-layer printing process which sets limitations on the printing speed and object geometry. Here, we report on the fabrication of complex 3D centimeter-scale ceramic parts by using tomographic volumetric printing which is fast, high resolution and offers a lot of freedom in terms of geometrical design compared to state-of-the-art AM techniques. First, we formulated a photosensitive preceramic resin that was solidified by projecting light patterns from multiple angles. Then, the obtained 3D printed parts were converted into ceramics by pyrolyzing them in a furnace. We demonstrate the strength of this approach through the fabrication of dense microcomponents exhibiting overhangs and hollow geometries without the need of supporting structures, and characterize their resistance to high heat and harsh chemical treatments.

physics.app-ph↗

arXiv2021

Direct (3+1)D laser writing of graded-index optical elements

We propose single-step additive fabrication of graded-index optical elements by introducing the light exposure as the additional dimension to three-dimensional (3D) laser writing, hence (3+1)D writing. We use a commercial printer and photoresist to realize the proposed single step fabrication method that can be swiftly adopted for research and engineering. After presenting the characterization of the graded-index profiles via basic structures, we demonstrate two different optical devices: volume holograms that are superimposed using angular and peristrophic multiplexing, and optical waveguides with well-defined refractive index profiles. In the latter, we precisely control the propagating modes via tuning the (3+1)D-printed waveguide parameters and report step-index and graded-index core-cladding transitions.

physics.optics↗

arXiv2021

Light-based Volumetric Additive Manufacturing in Scattering Resins

3D printing has revolutionized the manufacturing of volumetric components and structures for use in various fields. Owing to the advent of photo-curable resins, several fully volumetric light-based techniques have been recently developed to push further the resolution and speed limitations of 3D printing. However, these new approaches only work with homogeneous and relatively transparent resins so that the incident light patterns used for photo-polymerization are not impacted along their propagation through the material. Herein, we describe a strategy to print in scattering materials. It consists of characterizing how light is distorted by the curable resin and then applying a digital correction to the light patterns to counteract the effect of scattering. Using a tomographic volumetric printer, we experimentally demonstrate the importance of taking light scattering into account when computing the projected patterns and show that our applied correction significantly improves printability, even when the object size exceeds the scattering mean free path of light.

physics.optics↗

arXiv2021

Scalable Optical Learning Operator

Today's heavy machine learning tasks are fueled by large datasets. Computing is performed with power hungry processors whose performance is ultimately limited by the data transfer to and from memory. Optics is one of the powerful means of communicating and processing information and there is intense current interest in optical information processing for realizing high-speed computations. Here we present and experimentally demonstrate an optical computing framework based on spatiotemporal effects in multimode fibers for a range of learning tasks from classifying COVID-19 X-ray lung images and speech recognition to predicting age from face images. The presented framework overcomes the energy scaling problem of existing systems without compromising speed. We leveraged simultaneous, linear, and nonlinear interaction of spatial modes as a computation engine. We numerically and experimentally showed the ability of the method to execute several different tasks with accuracy comparable to a digital implementation.

physics.optics↗

arXiv2020

All-fiber spatiotemporally mode-locked laser with multimode fiber-based filtering

We demonstrate the first multimode fiber interference-based filtering in an all-fiber spatiotemporally mode-locked laser. The oscillator generates dissipative soliton pulses at 1036 nm with 12 mW average power, 6.24 ps duration and 24.3 MHz repetition rate. The reported pulse energy (0.5 nJ) represents ~4 times improvement over the previously reported single-mode all-normal dispersion mode-locked lasers with multimode interference-based filtering. Numerical simulations are performed to reveal cavity and spatiotemporal mode-locking dynamics. The proposed oscillator presents an alternative approach to achieve spatiotemporal mode-locking with an all-fiber design that can be fabricated simply.

physics.optics↗

arXiv2020

Single mode output by controlling the spatiotemporal nonlinearities in mode-locked femtosecond multimode fiber lasers

The performance of fiber mode-locked lasers is limited due to the high nonlinearity induced by the spatial confinement of the single-mode fiber core. To massively increase the pulse energy of the femtosecond pulses, amplification is obtained outside the oscillator. Recently, spatiotemporal mode-locking has been proposed as a new path to fiber lasers. However, the beam quality was either highly multimode and/or with low pulse energy. Here we present an approach to reach high energy per pulse directly in the mode-locked multimode fiber oscillator with a near single-mode output beam. Our approach relies on spatial self-beam cleaning via the nonlinear Kerr effect and we demonstrate a multimode fiber oscillator with M2<1.13 beam profile, up to 24 nJ energy and sub-100 fs compressed duration. The reported approach is further power scalable with larger core sized fibers and could benefit applications that require high power ultrashort lasers with commercially available optical fibers.

physics.optics↗

arXiv2020

Competing Neural Networks for Robust Control of Nonlinear Systems

The output of physical systems is often accessible by measurements such as the 3D position of a robotic arm actuated by many actuators or the speckle patterns formed by shining the spot of a laser pointer on a wall. The selection of the input of such a system (actuators and the shape of the laser spot respectively) to obtain a desired output is difficult because it is an ill-posed problem i.e. there are multiple inputs yielding the same output. In this paper, we propose an approach that provides a robust solution to this dilemma for any physical system. We show that it is possible to find the appropriate input of a system that results in a desired output, despite the input-output relation being nonlinear and\or with incomplete measurements of the systems variables. We showcase our approach using an extremely ill-posed problem in imaging. We demonstrate the projection of arbitrary shapes through a multimode fiber (MMF) when a sample of intensity-only measurements are taken at the output. We show image projection fidelity as high as ~90 %, which is on par with the gold standard methods which characterize the system fully by phase and amplitude measurements. The generality as well as simplicity of the proposed approach provides a new way of target-oriented control in real-world applications.

physics.optics↗