Tutorial: A practical guide to the alignment of defocused spatial light modulators for fast diffractive neural networks

TL;DR

This paper provides a practical semi-automatic method for precisely aligning multiple spatial light modulators in optical diffractive neural networks, enabling faster training and improved performance.

Contribution

It introduces a scalable, pixel-level conjugation alignment procedure for SLMs in optical DNNs, enhancing multi-channel processing capabilities.

Findings

Achieved pixel-level conjugation in SLM alignment

Enabled optical DNN processing of hundreds of inputs simultaneously

Reduced training times and experimental noise

Abstract

The conjugation of multiple spatial light modulators (SLMs) enables the construction of optical diffractive neural networks (DNNs). To accelerate training, which is limited by the low refresh rate of SLMs, spatial multiplexing of the input data across different spatial channels is possible, maximizing the number of available spatial degrees of freedom (DoFs). Precise alignment is required in order to ensure that the same physical operation is performed across each channel and thus the learning operation of the network. We present a semi-automatic procedure for this experimentally challenging alignment resulting in a pixel-level conjugation. It is scalable to any number of SLMs and may be useful in wavefront shaping setups where precise conjugation of SLMs is required, e.g. for the control of optical waves in phase and amplitude. The resulting setup functions as an optical DNN capable of processing hundreds of inputs simultaneously, thereby reducing training times and experimental noise through spatial averaging. We further present a characterization of the setup and an alignment method.