Spatial analysis of femtosecond laser generated plasma using principal component analysis.

The appearance of plasma generated during femtosecond laser machining depends strongly on the features present on the sample before machining occurs. However, the complexity of femtosecond light-matter interaction means that development of a theoretical understanding of plasma generation is challenging. In this work, principal component analysis is applied to experimental images of plasma generated during femtosecond laser machining of silicon to calculate the orthogonal spatial patterns of the plasma variance (plasma modes), and to identify which sample variance (sample modes) are associated with these plasma modes.

Single-step beam intensity and profile optimization using a 256 × 256 micromirror array and reinforcement learning.

Many optical applications require accurate control over a beam's spatial intensity profile, in particular, achieving uniform irradiance across a target area can be critically important for nonlinear optical processes such as laser machining. This paper introduces a novel control algorithm for Digital Micromirror Devices (DMDs) that simultaneously and adaptively modulates both the intensity and the spatial intensity profile of an incident beam with random and intricate intensity variations in a single step. The algorithm treats each micromirror within the DMD as an independent Bernoulli distribution characterized by a learnable parameter.

Imaging pollen using a Raspberry Pi and LED with deep learning.

The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigation of hay fever symptoms. We demonstrate the use of a white light LED (light emitting diode) to illuminate pollen grains and capture their scattering pattern using a Raspberry Pi camera. The scattering patterns are transformed into 20× microscope magnification equivalent images using deep learning.

Evolution of laughter from play.

In this hypothesis, I discuss how laughter from physical play could have evolved to being induced via visual or even verbal stimuli, and serves as a signal to highlight incongruity that could potentially pose a threat to survival. I suggest how laughter's induction could have negated the need for physical contact in play, evolving from its use in tickling, to tickle-misses, and to taunting, and I discuss how the application of deep learning neural networks trained on images of spectra of a variety of laughter types from a variety of individuals or even species, could be used to determine such evolutionary pathways via the use of latent space exploration.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A nanoporous gold membrane for sensing applications.

Design and fabrication of three-dimensionally structured, gold membranes containing hexagonally close-packed microcavities with nanopores in the base, are described. Our aim is to create a nanoporous structure with localized enhancement of the fluorescence or Raman scattering at, and in the nanopore when excited with light of approximately 600 nm, with a view to provide sensitive detection of biomolecules. A range of geometries of the nanopore integrated into hexagonally close-packed assemblies of gold micro-cavities was first evaluated theoretically.

Pulsed laser deposited diode-pumped 7.4 W Yb:Lu₂O₃ planar waveguide laser.

Fabrication, characterization, and laser performance of an Yb:Lu₂O₃ planar waveguide laser are reported. Pulsed laser deposition was employed to grow an 8 µm-thick Yb-doped lutetia waveguide on a YAG substrate. X-ray diffraction was used to determine the crystallinity, and spectroscopic characterization showed the absorption and emission cross-sections were indistinguishable from those reported for bulk material.

Design and fabrication of a 3D-structured gold film with nanopores for local electric field enhancement in the pore.

Three-dimensionally structured gold membrane films with nanopores of defined, periodic geometries are designed and fabricated to provide the spatially localised enhancement of electric fields by manipulation of the plasmons inside nanopores. Square nanopores of different size and orientation relative to the pyramid are considered for films in aqueous and air environments, which allow for control of the position of electric fields within the structure. Designs suitable for use with 780 nm light were created.

456-mW graphene Q-switched Yb:yttria waveguide laser by evanescent-field interaction.

In this Letter, we present a passively Q-switched Yb:Y2O3 waveguide laser using evanescent-field interaction with an atmospheric-pressure-chemical-vapor-deposited graphene saturable absorber. The waveguide, pumped by a broad area diode laser, produced an average output power of 456 mW at an absorbed power of 4.1 W.

Diode-end-pumped 1.2 W Yb:Y₂O₃ planar waveguide laser.

Fabrication, characterization and laser performance of a Watt-level ytterbium-doped yttria waveguide laser is presented. The waveguide was grown onto a YAG substrate by pulsed laser deposition and features a 6 µm thick ytterbium-doped yttria layer sandwiched between two 3 µm undoped yttria layers. The laser deposited film was characterized by X-ray diffraction, showing a high degree of crystallinity and analyzed spectroscopically, showing performance indistinguishable from previously reported bulk material.