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.

Live imaging of laser machining via plasma deep learning.

Real-time imaging of laser materials processing can be challenging as the laser generated plasma can prevent direct observation of the sample. However, the spatial structure of the generated plasma is strongly dependent on the surface profile of the sample, and therefore can be interrogated to indirectly provide an image of the sample. In this study, we demonstrate that deep learning can be used to predict the appearance of the surface of silicon before and after the laser pulse, in real-time, when being machined by single femtosecond pulses, directly from camera images of the generated plasma. This demonstration has immediate impact for real-time feedback and monitoring of laser materials processing where direct observation of the sample is not possible.

Acoustic and plasma sensing of laser ablation via deep learning.

Monitoring laser ablation when using high power lasers can be challenging due to plasma obscuring the view of the machined sample. Whilst the appearance of the generated plasma is correlated with the laser ablation conditions, extracting useful information is extremely difficult due to the highly nonlinear processes involved. Here, we show that deep learning can enable the identification of laser pulse energy and a prediction for the appearance of the ablated sample, directly from camera images of the plasma generated during single-pulse femtosecond ablation of silica. We show that this information can also be identified directly from the acoustic signal recorded during this process. This approach has the potential to enhance real-time feedback and monitoring of laser materials processing in situations where the sample is obscured from direct viewing, and hence could be an invaluable diagnostic for laser-based manufacturing.

Bi-frequency operation in a membrane external-cavity surface-emitting laser.

We report on the achievement of continuous wave bi-frequency operation in a membrane external-cavity surface-emitting laser (MECSEL), which is optically pumped with up to 4 W of 808 nm pump light. The presence of spatially specific loss of the intra-cavity high reflectivity mirror allows loss to be controlled on certain transverse cavity modes. The regions of spatially specific loss are defined through the removal of Bragg layers from the surface of the cavity high reflectivity mirror in the form of crosshair patterns with undamaged central regions, which are created using a laser ablation system incorporating a digital micromirror device (DMD). By aligning the laser cavity mode with the geometric centre of the loss patterns, the laser simultaneously operated on two Hermite-Gaussian spatial modes: the fundamental HG00 and the higher order HG11 mode. We demonstrate bi-frequency operation over a range of pump powers and sizes of spatial loss features, with a wavelength separation of approximately 5 nm centred at 1005 nm.

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.

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

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.

Modelling of fibre laser cutting via deep learning.

Laser cutting is a materials processing technique used throughout academia and industry. However, defects such as striations can be formed while cutting, which can negatively affect the final quality of the cut. As the light-matter interactions that occur during laser machining are highly non-linear and difficult to model mathematically, there is interest in developing novel simulation methods for studying these interactions. Deep learning enables a data-driven approach to the modelling of complex systems. Here, we show that deep learning can be used to determine the scanning speed used for laser cutting, directly from microscope images of the cut surface. Furthermore, we demonstrate that a trained neural network can generate realistic predictions of the visual appearance of the laser cut surface, and hence can be used as a predictive visualisation tool.

The future of bone regeneration: integrating AI into tissue engineering.

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

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.

What is the functional/organic distinction actually doing in psychiatry and neurology?

The functional-organic distinction aims to distinguish symptoms, signs, and syndromes that can be explained by diagnosable biological changes, from those that cannot. The distinction is central to clinical practice and is a key organising principle in diagnostic systems. Following a pragmatist approach that examines meaning through use, we examine how the functional-organic distinction is deployed and conceptualised in psychiatry and neurology. We note that the conceptual scope of the terms 'functional' and 'organic' varies considerably by context. Techniques for differentially diagnosing 'functional' and 'organic' diverge in the strength of evidence they produce as a necessary function of the syndrome in question. Clinicians do not agree on the meaning of the terms and report using them strategically. The distinction often relies on an implied model of 'zero sum' causality and encourages classification of syndromes into discrete 'functional' and 'organic' versions. Although this clearly applies in some instances, this is often in contrast to our best scientific understanding of neuropsychiatric disorders as arising from a dynamic interaction between personal, social and neuropathological factors. We also note 'functional' and 'organic' have loaded social meanings, creating the potential for social disempowerment. Given this, we argue for a better understanding of how strategic simplification and complex scientific reality limit each other in neuropsychiatric thinking. We also note that the contribution of people who experience the interaction between 'functional' and 'organic' factors has rarely informed the validity of this distinction and the dilemmas arising from it, and we highlight this as a research priority.

Semiconductor disk laser in bi-frequency operation by laser ablation micromachining of a laser mirror.

We present bi-frequency continuous wave oscillation in a semiconductor disk laser through direct writing of loss-inducing patterns onto an intra-cavity high reflector mirror. The laser is a Vertical External Cavity Surface Emitting Laser which is optically pumped by up to 1.1 W of 808 nm light from a fibre coupled multi-mode diode laser, and oscillates on two Hermite-Gaussian spatial modes simultaneously, achieving wavelength separations between 0.2 nm and 5 nm around 995 nm. We use a Digital Micromirror Device (DMD) enabled laser ablation system to define spatially specific loss regions on a laser mirror by machining away the Bragg layers from the mirror surface. The ablated pattern is comprised of two orthogonal lines with the centermost region undamaged, and is positioned in the laser cavity so as to interact with the lasing mode, thereby promoting the simultaneous oscillation of the fundamental and a higher order spatial mode. We demonstrate bi-frequency oscillation over a range of mask gap sizes and pump powers.

Real-time particle pollution sensing using machine learning.

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.

Predictive capabilities for laser machining via a neural network.

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.

Ultrafast multi-layer subtractive patterning.

Subtractive femtosecond laser machining using multiple pulses with different spatial intensity profiles centred on the same position on a sample has been used to fabricate surface relief structuring. A digital micromirror device was used as an intensity spatial light modulator, with a fixed position relative to the sample, to ensure optimal alignment between successive masks. Up to 50 distinct layers, 335 nm lateral spatial resolution and 2.6 µm maximum depth structures were produced. The lateral dimensions of the structures are approximately 40 µm. Surface relief structuring is shown to match intended depth profiles in a nickel substrate, and highly repeatable stitching of identical features in close proximity is also demonstrated.

Single-pulse ablation of multi-depth structures via spatially filtered binary intensity masks.

Digital micromirror devices (DMDs) show great promise for use as intensity spatial light modulators. When used in conjunction with pulsed lasers of a timescale below the DMD pixel switching time, DMDs are generally only used as binary intensity masks (i.e., "on" or "off" intensity for each mask pixel). In this work, we show that by exploiting the numerical aperture of an optical system during the design of binary masks, near-continuous intensity control can be accessed, whilst still maintaining high-precision laser-machining resolution. Complex features with ablation depths up to ∼60 nm, corresponding to grayscale values in bitmap images, are produced in single pulses via ablation with 150 fs laser pulses on nickel substrates, with lateral resolutions of ∼2.5 µm.

Sub-diffraction limit laser ablation via multiple exposures using a digital micromirror device.

We present the use of digital micromirror devices as variable illumination masks for pitch-splitting multiple exposures to laser machine the surfaces of materials. Ultrafast laser pulses of length 150 fs and 800 nm central wavelength were used for the sequential machining of contiguous patterns on the surface of samples in order to build up complex structures with sub-diffraction limit features. Machined patterns of tens to hundreds of micrometers in lateral dimensions with feature separations as low as 270 nm were produced in electroless nickel on an optical setup diffraction limited to 727 nm, showing a reduction factor below the Abbe diffraction limit of ∼2.7×. This was compared to similar patterns in a photoresist optimized for two-photon absorption, which showed a reduction factor of only 2×, demonstrating that multiple exposures via ablation can produce a greater resolution enhancement than via two-photon polymerization.

Rapid bespoke laser ablation of variable period grating structures using a digital micromirror device for multi-colored surface images.

A digital micromirror device has been used to project variable-period grating patterns at high values of demagnification for direct laser ablation on planar surfaces. Femtosecond laser pulses of ∼1 mJ pulse energy at 800 nm wavelength from a Ti:sapphire laser were used to machine complex patterns with areas of up to ∼1 cm2 on thin films of bismuth telluride by dynamically modifying the grating period as the sample was translated beneath the imaged laser pulses. Individual ∼30 by 30 µm gratings were stitched together to form contiguous structures, which had diffractive effects clearly visible to the naked eye. This technique may have applications in marking, coding, and security features.

Picosecond optically reconfigurable filters exploiting full free spectral range tuning of single ring and Vernier effect resonators.

We demonstrate that phase shifts larger than 2π can be induced by all-optical tuning in silicon waveguides of a few micrometers in length. By generating high concentrations of free carriers in the silicon employing absorption of ultrashort, ultraviolet laser pulses, the refractive index of silicon can be drastically reduced. As a result, the resonance wavelength of optical resonators can be freely tuned over the full free spectral range. This allows for active integrated optic devices that can be switched with GHz frequencies into any desired state by all-optical means.