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.

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Abstract: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/cm 2 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.

In this paper, we develop the idea of Thompson which treats the relationship between bridge position, incompressible meridianal planar surfaces, and thin position. We show that for a link in thin position there exits a canonical depth 1 nested tangle decomposition with incompressible 2-spheres arising from the thin position (Proposition 3.7), and we show that there is a maximal essential tangle decomposition of the link that is closely related to the thin position (Theorem 4.3).

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Femtosecond laser-induced backward transfer of transparent photopolymers is demonstrated in the solid state, assisted by a digital micromirror spatial light modulator for producing shaped deposits. Through use of an absorbing silicon carrier substrate, we have been able to successfully transfer solid-phase material, with lateral dimensions as small as *6 lm. In addition, a carrier of silicon incorporating a photonic waveguide relief structure enables the transfer of imprinted deposits that have been accomplished with surface features exactly complementing those present on the substrate, with an observed minimum feature size of 140 nm.

Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, highresolution microscopy with automatic image processing.