In 2015, we launched a program with the aim of preserving the scientific heritage of our laboratory, which entails conserving and showcasing historical documents and objects. Within this program, we offer internships that integrate history and science. One such internship focuses on utilizing modern optical design software to comprehend historical optical devices such as Charles Féry’s spectrophotometer from the 1910s. This approach illustrates how contemporary tools can illuminate the origins of scientific knowledge.
We describe a project underway since 2015 at the Université de Franche-Comté in France where we have been preserving the history of optics and photonics, with the particular aim of ensuring our students are made aware of this rich scientific heritage. We have successfully located and preserved a wide range of instrumentation and archival material dating from the mid-19th century to the 1960s, including some of the first European studies of lasers, holograms, and their applications. We are currently placing an emphasis on recording oral histories of current and former researchers and educators to ensure that our history during the latter part of the 20th century is fully recorded whilst memories are still fresh, and whilst supporting equipment and laboratory material can be found and archived.
An area of particular importance in developing advanced imaging techniques concerns 3D motion measurement in small-scale mechatronics and automated microscopy. One major drawback is related to complex motion measurement with 6 degrees of freedom. In the proposed work, the extraction of unknown metrics such as focusing distance, in plane and out-of-plane positioning from digital holograms is performed including real‐time constraints. This work explores extended computer micro-vision capabilities offered by combining digital holographic microscopy (DHM) and last generation of deep learning algorithms such as Vision Transformer (ViT) networks. Our experiments show that the reconstruction in-focus distance can be predicted in DHM with a high accuracy using tiny modified architectures of deep ViT networks and convolutional neural networks (CNN). We compare ViT and Tiny ViT models with deep CNN usually used in digital holography such as VGG16, LeNet and AlexNet.
The real-time positioning of an object on a microscopic scale is a significant challenge and remains difficult to apply. Many traditional imaging techniques exist but their axial resolution and/or their measurement range is often limited. We develop a novel high‐profile technology based on three pillars to meet these challenges. Using digital holography, we determine the correct focus distance on a large scale. Secondly, a new generation transformer neural networks processes the hologram giving in real-time (~30 frames per seconds) a submicrometric axial resolution, exceeding therefore the diffraction limit of the depth of field. Finally, the spatial structuring of the object allows us a nanometric lateral positioning by classical techniques, which will be sped up by a machine learning technique. Such high frame rates enable real-time processing in many different application scenarios.
We develop a novel high‐profile application of machine learning techniques by elevating digital holography and sensing in robotics to a new level. The extraction of unknown metrics such as focusing distance and in plane positioning without full image restoration from digital holograms is performed by pre‐processing approach in space‐domain and/or in Fourier‐domain, including real‐time constraints. Measuring a single hologram, we successfully determine the axial distance of a complex object to the 10x microscope objective over a range of 100 µm with an accuracy of 1.25 µm. We apply a machine learning technique to the hologram to speed up tracking in the plane of the pseudo-periodic target position up to several tens of frames per second (fps). Such high frame rates enable real-time processing in many different application scenarios.
We analyze the fundamental impact of noise propagation in deep neural network (DNN) comprising nonlinear neurons and with connections optimized by training. Our motivation is to understand the impact of noise in analogue neural network realizations. We consider the influence of additive and multiplicative, correlated and uncorrelated types of internal noise in DNNs. We find general properties of the noise impact depending on the noise type, activation function, depth and the statistics of connection matrices and show that noise accumulation can be efficiently avoided. Our work is based on analytical methods predicting the noise levels in all layers of the network.
Maximal computing performance can only be achieved if neural networks are fully hardware implemented. Besides the potentially large benefits, such parallel and analogue hardware platforms face new, fundamental challenges. An important concern is that such systems might ultimately succumb to the detrimental impact of noise. We study of noise propagation through deep neural networks with various neuron nonlinearities and trained via back-propagation for image recognition and time-series prediction. We consider correlated and uncorrelated, multiplicative and additive noise and use noise amplitudes extracted from a physical experiment. The developed analytical framework is of great relevance for future hardware neural networks. It allows predicting the noise level at the system’s output based on the properties of its constituents. As such it is an essential tool for future hardware neural network engineering and performance estimation.
We propose a novel implementation of autonomous photonic neural networks based on optically-addressed spatial light modulators (OASLMs). In our approach, the OASLM operates as a spatially non-uniform birefringent waveplate, the retardation of which nonlinearly depends on the incident light intensity. We develop a complete electrical and optical model of the device and investigate the optimal operational characteristics. We study both, feed-forward and recurrent neural networks and demonstrate that OASLMs are promising candidates for the implement of autonomous photonic neural networks with large numbers of neurons and ultra low energy consumption.
We experimentally create a neural network via a spatial light modulator, implementing connections between 2025 in parallel based on diffractive coupling. We numerically validate the scheme for at least 34.000 photonic neurons. Based on a digital micro-mirror array we demonstrate photonic reinforcement learning and predict a chaotic time-series via our optical neural network. The prediction error efficiently converges. Finally, we give insight based on the first investigation of effects to be encountered in neural networks physically implemented in analogue substrates.
An experimental study of the variation of quality factor (Q-factor) of mm-size whispering-gallery mode (WGM) resonators manufactured with fluoride crystals as a function of surface roughness is proposed. Q-factors of the order of 1 billion are measured at 1550 nm. The experimental procedure needs repeated polishing steps, after which the surface roughness is measured by quantitative phase imaging, based on a white-light phase-shifting interferometry approach, while the Q-factors are determined using the cavity-ring-down method. This process allows us to reach an explicit curve linking the Q-factor of the disk-resonator to the surface roughness of the rim.
The variations of Q-factor as a function of surface roughness is universal, in the sense that it is globally independent of the bulk material under consideration. We used a white-light interferometer to investigate the dependency of Q-factors considering three different difluoride crystals as bulk materials; in all cases, we have found that a billion Q-factors at 1550 nm are achieved when the rms surface roughness has a nanometer order of magnitude.
We have also compared our experimental data with theoretical estimations. This comparison enabled us to highlight a mismatch, which can be explained by the many physical constraints imposed by the mechanical grinding and polishing protocol. We expect that our work will contribute to a better understanding of the Q-factor limitations for mm-size WGM resonators, which are finding applications in a broad range of areas.
We propose a vision-based position sensor based on Digital Holography (DH) for in-plane and out-of-plane displacements measurement of a patterned plate with sub-pixel resolutions. DH is a lensless imaging principle using solid-state camera and/or spatial light modulators (SLM). Object scenes are generated or reconstructed numerically through wave propagation computations applied to a diffracted optical field recorded as an interferogram. The application of visual positioning to manipulation tasks in micro-robotics requires high accuracy and wide ranges of displacements that, unfortunately, are limited by finite depth-of-focus and fixed working distance of refractive imaging systems. Recently, we demonstrated that DH allows in-plane positioning of mobile targets ensuring nanometer resolutions at diverse working distances within a continuous range of more than 15 centimeters. By recording a set of digital holograms of a pseudo-periodic pattern fixed onto a moving target, images in phase and in intensity are restored by numerical reconstruction using Angular Spectrum Propagation methods by adjusting the reconstruction distance. A last step consists in performing a direct phase measurements of periodic pattern to reach nanometer resolutions. Three 2DFFT are required at minimum to extract the pattern position, which is time consuming if several hundred of holograms are recorded. We explore a new approach that consists to restore in-plane / out-of-plane position directly from the 2DFFT of the digital hologram without any need for image restitution. The proposed vision-based position sensor combines a 10 Mp CMOS camera and a SLM in order to perform a fine control of the interferometer reference arm.
Shaping complex light fields such as nondiffracting beams, provide important novel routes to control laser materials processing. Nondiffracting beams are produced from an interference between waves with an angle kept constant along the propagation direction. These beams are of outmost importance for laser materials processing because they offer invariant light-matter interaction conditions. We have used and developed several families of beams generated with phase and amplitude shaping and we will review their impact for laser surface processing and high aspect ratio laser processing in the bulk of transparent materials. Bessel beams and higher order Bessel beams allow for high aspect ratio channel drilling, elongated void creation in the bulk of transparent media, or tubular damage creation. We will also discuss the impact of accelerating beam shaping, ie beams with a curved main intensity lobe, to dice materials with a curved edge.
This project has received funding from the European Research Council (ERC) under the European
Union's Horizon 2020 research and innovation programme (grant agreement No 682032-PULSAR).
We investigate theoretically and experimentally the computational properties of an optoelectronic neuromorphic processor based on a complex nonlinear dynamics. This neuromorphic approach is based on a new paradigm of or reservoir computing, which is intrinsically different from the concept of Turing machines. It essentially consists in expanding the input information to be processed into a higher dimensional phase space, through the nonlinear transient response of a complex dynamics excited by the input information. The computed output is then extracted via a linear separation of the transient trajectory in the complex phase space, performed through a learning phase consisting of the resolution of a regression problem. We here investigate an architecture for photonic neuromorphic computing via these complex nonlinear dynamical transients. A versatile photonic nonlinear transient computer based on a multiple-delay is reported. Its hybrid analogue and digital architecture allows for an easy reconfiguration, and for direct implementation of in-line processing. Its computational efficiency in parameter space is also analyzed, and the computational performance of this system is successfully evaluated on a standard spoken digit recognition task. We then discuss the pathways that can lead to its effective integration.
Ultrafast laser pulses are a powerful tool to process dielectrics. Here, we review our recent work concerning high aspect
ratio micro and nanochannel processing in glass. We show how femtosecond Bessel beams overcome many of the
difficulties associated with Gaussian beams. We report on single shot processing of nanochannels with aspect ratio up to
100. Underlying physical phenomena are discussed.
Although ultrafast lasers have demonstrated much success in structuring and ablating dielectrics on the micrometer scale
and below, high aspect ratio structuring remains a challenge. Specifically, microfluidics or lab-on-chip DNA sequencing
systems require high aspect ratio sub-10 μm wide channels with no taper. Micro-dicing also requires machining with
vertical walls. Backside water assisted ultrafast laser processing with Gaussian beams allows the production of high
aspect ratio microchannels but requires sub-micron sample positioning and precise control of translation velocity.
In this context, we propose a new approach based on Bessel beams that exhibit a focal range exceeding the Rayleigh
range by over one order of magnitude. An SLM-based setup allows us to produce a Bessel beam with central core
diameter of 1.5 μm FWHM extending over a longitudinal range of 150 μm. A working window in the parameter space
has been identified that allows the reliable production of high aspect ratio taper-free microchannels without sample
translation. We report a systematic investigation of the damage morphology dependence on focusing geometry and
energy per pulse.
The novel propagation characteristics of Bessel beams have been widely applied to optical manipulation and harmonic
generation, and have provided new perspectives on fundamentals of ultrashort laser pulse propagation in nonlinear
media. Fully exploiting their many unique properties, however, requires the development of techniques for the
generation of high quality Bessel beams with flexible adjustment of the beam parameters. Moreover, long working
distances are needed to produce Bessel beams inside bulk samples. In this paper, we report on the development of a
novel spatial light modulator based setup that combines the properties of parameter flexibility, long working distance,
high throughput and operation on micron-scale. We report both on the general characterization of the beam properties as
well as a specific application in surface nanoprocessing.
Chaotic wavelength transmitters based on a DBR laser submitted to optoelectronic feedback with periodic time delay are
considered. We investigate the retrieval of the periodic time delay function from experimental time series. Square-wave
and sinusoidal modulations are considered for the frequency clock of a delay module based on a First-In First-Out
memory. It is shown that the period of the time delay can be extracted from experimental data by using the mutual
information function. Different values of the nonlinearity are considered. Applying a modified filling factor method the
periodic time delay function is retrieved in the sinusoidal modulation case for different periods and modulation depths.
In this paper we describe an interferometric process using a polychromatic light source and a spectroscopic detection system. This method is used for surface metrology or for bulk optical components characterisation (dispersion for example). As classical monochromatic interferometry, it consists in comparing a reference wave front with one issued from the component to be tested. However this measurement is assumed by determination of the spectral dispersion induced upon the various frequencies of the light source spectrum. The aim of this work is both dispersion measurements and characterisation of aspherical surfaces
Optical Coherent Tomography (OCT) technique is based on an interferometric device bringing to the inter-correlation between a short reference pulse and the signal issued from the medium. This correlation is obtained by mechanical length modulation of the interferometer reference arm. We propose here an original technique using the SISAM (“Spectroscope Interferentiel a Selection par l’Amplitude de la Modulation”: Interferential Spectrometer by Selection of Amplitude Modulation) correlator, which allows to obtain directly without length modulation, the inter-correlation signal between the reference and the tests waves. With a large spectral bandwidth light source, the temporal depth of the original pulse is short compared to the signal diffused in the complex medium, and the inter-correlation function may be reduced to the impulse response of the structure to be studied. This temporal analysis could be very interesting to obtain both amplitude and phase parameters on the waves propagated in the medium, and could induce significant data on the medium and its structure. We will present the experimental SISAM device and results obtained in imaging through turbid media with this technique. We will also discuss about efficiency of this method in terms of accuracy and of ability to characterize complex structures and media.
KEYWORDS: Digital holography, Image sensors, Sensors, Modulation transfer functions, Holograms, Linear filtering, Optical filters, Image filtering, Digital recording, Spatial frequencies
A common procedure of profilometry by means of white light interferometry is to scan one interferometer arm step by step. In this way, the intensity detected for each surface point reproduces the autocorrelogram of the light source, which is used for the determination of the absolute phase between a reference position and the zero optical path difference position. Phase changes due to reflection on the inspected surface produces a shift of the interference fringes with respect to the coherence envelope. If those phase changes vary from points to points, artifacts can be introduced in the profile reconstruction. We propose to measure simultaneously the interferometric phase and the shift of the interference fringes with respect to the coherence envelope. That processing is based on a wavelet transformation of the sampled light source correlograms and leads to complementary information that describes more completely the optical behavior of surfaces.
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