Daniel Heath

Dynamic spatial pulse shaping via a digital micromirror device for patterned laser-induced forward transfer of solid polymer films

We present laser-induced forward transfer of solid-phase polymer films, shaped using a Digital Micromirror Device (DMD) as a variable illumination mask. Femtosecond laser pulses with a fluence of 200-380 mJ/cm2 at a wavelength of 800 nm from a Ti:sapphire amplifier were used to reproducibly transfer thin films of poly(methyl methacrylate) as small as ~30 µm by ~30 µm with thickness ~1.3 µm. This first demonstration of DMD-based solid-phase LIFT shows minimum feature sizes of ~10µm.

Laser ablation via programmable image projection for submicron dimension machining in diamond

A digital micromirror device is used as an intensity spatial light modulator, in conjunction with a femtosecond laser, for programmable image-projection-based laser ablation of polycrystalline diamond. Results show the machining of complex structures on the diamond surface, where individual structures have submicron features, covering a total area of 10 × 10 μm and fabricated using ten laser pulses. This dynamic image-based machining technique may offer speed advantages over serial-writing procedures, whilst still producing wavelength-scale feature sizes.

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.

Machine learning for 3D simulated visualization of laser machining

Laser machining can depend on the combination of many complex and nonlinear physical processes. Simulations of laser machining that are built from first-principles, such as the photon-atom interaction, are therefore challenging to scale-up to experimentally useful dimensions. Here, we demonstrate a simulation approach using a neural network, which requires zero knowledge of the underlying physical processes and instead uses experimental data directly to create the model of the experiment. The neural network modelling approach was shown to accurately predict the 3D surface profile of the laser machined surface after exposure to various spatial intensity profiles, and was used to discover trends inherent within the experimental data that would have otherwise been difficult to discover.

Dataset for Real-time particle pollution sensing using machine learning

dataset supports grant jacob james a et al 2018 real time particle pollution sensing using machine learning optics expressassigned doi is https doi org 10 5258 soton d0436

Digital micromirror device based adaptive optics approach for enhanced micro-machining fidelity (Conference Presentation)

Outline - Past work and challenges in DMD projection-based laser manufacturing - DMD mask shifting - Taking advantage of limited bandwidth - Custom substrates, including cell growth assays - Conclusions Will go through some of our past work, along with challenges we’ve faced. Will show how a combination of mask repositioning on the DMD and exploiting limited bandwidth can overcome some of these problems. Will show some of the work we’re doing towards cell growth assays. Developing laser manufacturing techniques, with UK industrial partners, for high-precision, high-speed, flexible etc fabrication - applications in sensing, bio-medicine, metamaterials, etc etc etc. DMDs offer many advantages for laser machining, which have yet to be established in industry. Not just beam shaping, but other more advanced techniques, which the group at Southampton are pioneering, and I will talk about some of these today.