Keywords: Equivariant Neural Networks, Spatial Light Modulator, Thermocapillary Dewetting, Rotational Equivariance, Deep Learning
TL;DR: We propose to use Equivariant Neural Networks to learn and predict the input temperature signal required to induce a given height pattern in the reflected light of a thermocapillary dewetting-based spatial light modulator.
Abstract: Spatial Light Modulators (SLMs) are devices that can modulate the amplitude or the phase of a beam of light. These devices are used in applications such as beam front aberration and microscopic manipulation with optical tweezers. Here, we study the problem of learning to modulate light in a new type of temperature-controlled SLM. These SLMs are panels that use a thin viscous film in which shallow wave patterns can be induced by varying the temperature of the panel. This method can be used for modulating light such as high-power lasers. The problem here is to learn which input temperature signal is necessary in order to induce a given pattern in the reflected light. We propose a deep equivariant model to learn this relationship. We generate a synthetic dataset consisting of temperature signals and corresponding light patterns by simulating the thin film lubrication equation that governs the phenomenon of thermocapillary dewetting. We use this dataset to train our networks. We demonstrate the advantage of using equivariant neural networks over convolutional neural networks in order to learn the mapping.