Predictive visualisation for laser-processing of materials can be challenging, as the nonlinear interaction of light and matter is complicated to model, particularly when scaling up from atom-level to bulk material. Here, we demonstrate a predictive visualisation approach that uses a pair of neural networks (NNs) that are trained using data obtained from laser machining using a digital micromirror device (DMD) acting as an intensity spatial light modulator. The DMD enables laser machining using many beam shapes, and hence can be used to produce significant amounts of training data for NNs. Here, the training data corresponds to hundreds of DMD patterns (i.e. beam shapes) and their associated images and 3D depth profiles. The trained NNs are able to generate a surface image and 3D depth profile, showing what the ablated surface would look like, for a wide range of ablating beam shapes. The predicted visualisations are remarkably effective and almost indistinguishable from real experimental data in appearance.
Such a NN approach has considerable advantages over modelling techniques that start from first-principles (i.e. light-atom interaction), since zero understanding of the underlying physical processes is needed, as instead the NN learns directly via observation of labelled experimental data. We will show that the NN learns key optical properties such as diffraction, the nonlinear interaction of light and matter, and the statistical distribution of debris and burring of material, all with zero human assistance. This offers a new paradigm in predictive capabilities, which could be applied to almost any manufacturing process.
Dual frequency comb generation is a field which has seen considerable interest in recent years, with notable implementations such as dual wavelength operation of a Mode-locked Integrated External-cavity Surface Emit- ting Laser (MIXSEL), CW pumping of orthogonal polarisation states in a microring resonator, and optical phase-locking of discrete frequency combs. Dual frequency operation of CW Vertical External Cavity Surface Emitting Lasers (VECSEL) has been demonstrated in a particularly well controlled way using sub-wavelength metallic masks fabricated onto the surface of the laser gain structure. We present a variation of this technique in which patterned loss masks are machined onto a VECSEL cavity mirror using a Digital Micromirror Device (DMD)-enabled femtosecond-laser ablation system, where the DMD is used as an intensity spatial light mod- ulator. Interaction of the loss mask with the laser mode area results in the VECSEL oscillating preferentially on the spatial modes that observe the least loss within the aperture, and modulation of pump power enables control of the oscillating mode frequency separation. We describe the characteristics of the masks and the properties of the laser operation as progress towards eventual pulsed emission. Our technique has the advan- tages of discrete gain and Semiconductor Saturable Absorber Mirror (SESAM) structures, very fast fabrication times and the ability to fabricate multiple apertures on a single mirror.
Materials processing using femtosecond laser pulses offers the potential for high-precision manufacturing. However, due to the associated nonlinear processes, even small levels of experimental noise (e.g. instability in laser power, or unexpected debris) can result in substantial deviations from the desired machined structures. There is therefore much interest in the development of closed-loop feedback processes. Recent advances in the algorithms behind neural networks, and in particular convolutional neural networks (CNNs) have led to rapid advancements in the field. Here, we will present the first demonstration of the application of a CNN for observing and identifying the experimental parameters exclusively from a camera that observes the sample during laser machining. We will show that the CNN was able to accurately determine the laser fluence, number of pulses and the material used.
Although there are many other computational approaches for image-based feedback, this CNN approach has the significant advantage that it works purely as a pattern recognition device, and hence requires minimal human input with regards to the physical processes that underlie the laser machining process. Therefore, this avoids the need for a comprehensive programmatical description of the nonlinear interaction of laser light and material. Training time was one hour, and the time to process and identify the experimental parameters from a single image was approximately 30 milliseconds, hence showing the potential for a CNN to act as the central component of a real-time feedback system for laser machining, and enabling undesired or incorrect machining to be immediately compensated.
Digital micromirror devices (DMDs) have found many scientific research applications. We present adaptive optics techniques exploiting the point spread function (PSF) of a DMD pixel to enhance the fidelity of image-projection-based laser machining. Femtosecond laser pulses with intensity profiles spatially shaped by a DMD were demagnified to a sample via a microscope objective, with ~10 DMD mirrors, each of width ~10µm, approximately projecting to the optical setup diffraction limit of ~1µm. A single DMD mirror then scales geometrically to dimensions well below the diffraction limit, permitting various techniques to enhance machining. By digitally shifting an intensity mask on the DMD between pulses while the sample remains static, machined features with resolutions below the single-exposure diffraction limit are produced (similar to pitch splitting multiple exposure techniques), with a reduction of <2.5x achieved in nickel. By combining digital image shifts with real-time sample image recognition algorithms, point-to-point positional accuracy is camera-resolution-limited (~500nm) rather than translation stage-limited. Furthermore, the PSF allows near-continuous intensity distributions rather than binary on/off intensity patterns, and have been used to produce variable-depth surface texturing (up to 40nm depth changes with 2µm period demonstrated in metals) features via single shots. Algorithms have been used to automate optical proximity corrections for arbitrary intensity masks in order to reduce machining errors due to optical filtering. These techniques are being combined to produce <1cm2 size, highly complex substrates for the production of biologically-friendly cell growth assays, with the viability of human bone stem cells on flexible substrates demonstrated.