Single-step phase identification and phase locking for coherent beam combination using deep learning.

Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

Characterisation of engineered defects in extreme ultraviolet mirror substrates using lab-scale extreme ultraviolet reflection ptychography.

Ptychography is a lensless imaging technique that is aberration-free and capable of imaging both the amplitude and the phase of radiation reflected or transmitted from an object using iterative algorithms. Working with extreme ultraviolet (EUV) light, ptychography can provide better resolution than conventional optical microscopy and deeper penetration than scanning electron microscope. As a compact lab-scale EUV light sources, high harmonic generation meets the high coherence requirement of ptychography and gives more flexibilities in both budget and experimental time compared to synchrotrons. The ability to measure phase makes reflection-mode ptychography a good choice for characterising both the surface topography and the internal structural changes in EUV multilayer mirrors. This paper describes the use of reflection-mode ptychography with a lab-scale high harmonic generation based EUV light source to perform quantitative measurement of the amplitude and phase reflection from EUV multilayer mirrors with engineered substrate defects. Using EUV light at 29.6nm from a tabletop high harmonic generation light source, a lateral resolution down to ∼88nm and a phase resolution of 0.08rad (equivalent to topographic height variation of 0.27nm) are achieved. The effect of surface distortion and roughness on EUV reflectivity is compared to topographic properties of the mirror defects measured using both atomic force microscopy and scanning transmission electron microscopy. Modelling of reflection properties from multilayer mirrors is used to predict the potential of a combination of on-resonance, actinic ptychographic imaging at 13.5nm and atomic force microscopy for characterising the changes in multilayered structures.

Motion control for laser machining via reinforcement learning.

Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.

Single-frame 3D lensless microscopic imaging via deep learning.

Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to image pollen grains in 3-dimensions using such a technique could be truly disruptive in the palynology, bioaerosol sensing, and ecology sectors. Here, we show the ability to employ deep learning to generate 3-dimensional images of pollen grains using a series of 2-dimensional images created from 2-dimensional scattering patterns. Using a microscope to obtain 3D Z-stack images of a pollen grain and a 520 nm laser to obtain scattering patterns from the pollen, a single scattering pattern per 3D image was obtained for each position of the pollen grain within the laser beam. In order to create a neural network to transform a single scattering pattern into different 2D images from the Z-stack, additional Z-axis information is required to be added to the scattering pattern. Information was therefore encoded into the scattering pattern image channels, such that the scattering pattern occupied the red channel, and a value indicating the position in the Z-axis occupied the green and blue channels. Following neural network training, 3D images were formed from collated generated 2D images. The volumes of the pollen grains were generated with a mean accuracy of ∼84%. The development of airborne-pollen sensors based on this technique could enable the collection of rich data that would be invaluable to scientists for understanding mechanisms of pollen production climate change and effects on the wider public health.

Single step phase optimisation for coherent beam combination using deep learning.

Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

Uniform aligned bioconjugation of biomolecule motifs for integration within microfabricated microfluidic devices.

Full details and a step-by-step guide suitable for printing proteins aligned to micron-sized sensors and subsequent integration and alignment of microfluidic structures are presented. The precise alignment and grafting of micron-sized biomolecule patterns with an underlying substrate at predefined locations is achieved using a novel semi-automated microcontact printer. Through integration of optical alignment methods in the x, y, and z directions, uniform contact of micron-sized stamps is achieved. Feature compression of the stamp is avoided by fine control of the stamp during contact. This printing method has been developed in combination with robust, compatible bioconjugate chemistry for patterning of a dextran-functionalized silicon oxide substrate with a NeutrAvidin-"inked" stamp and subsequent incubation with a biotin-functionalized protein. The bioconjugate chemistry is such that uniform coverage of the protein (without denaturation) over the printed motif is obtained and reproduction of the initial mask shape and dimensions is achieved. Later integration with a microfluidic structure aligned with the printed motif on the substrate is also described.

Soft-x-ray wavelength shift induced by ionization effects in a capillary.

Coherent soft x rays are produced by high-harmonic generation in a capillary filled with Ar gas. We demonstrate that the tuning of the harmonic wavelengths with intensity and chirp arises from changes in the Ar ionization level. Control over the tuning can be achieved either by changing the average intensity of the laser pulse or by varying the quadratic spectral phase of the laser pulse. We observe an ionization-dependent blueshift of the fundamental wavelength that is directly imprinted on the harmonic wavelengths. The harmonic tuning is shown to depend on nonlinear spectral shifts of the fundamental laser pulse that are due to the plasma created by ionization, rather than directly on any chirp imposed on the fundamental wavelength.