Intelligent Holographic Techniques like digital holographic microscopy (DHM) capture the entire wavefront of light scattered from an object. This generates vast amounts of complex data (amplitude and phase).
Intelligent Photonics is a paradigm where photonic systems (systems that use light) are integrated with artificial intelligence (AI) and machine learning (ML). Instead of just being a passive medium for transmitting data, light itself is used to perform computational tasks, often with high speed and low energy consumption. The ‘intelligence’ comes from the co-design of optics and algorithms.
Core Tenets of Intelligent Photonics:
AI/ML for Photonics: Using algorithms to design, control, and optimise photonic devices.
Photonics for AI/ML: Using optical hardware (optical neural networks) to accelerate AI computations.
Adaptive & Reconfigurable Systems: Systems that can change their function in real time based on feedback or environmental changes.
The Convergence: Holography in Intelligent Photonics. Holography, the science of creating and reconstructing wavefronts, is a perfect tool for intelligent photonics. When made ‘intelligent’ holography moves beyond static 3D images to become a dynamic, programmable, and computational interface for light.
AI Generated Computer Generated Holography (CGH)
Traditional CGH vs. AI CGH: Traditional methods (e.g., Gerchberg Saxton algorithm) are iterative and can be slow, often resulting in noisy or low quality reconstructions.
How AI Helps: Deep learning models, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), can be trained to calculate hologram patterns almost instantly. They learn the complex relationship between a target 3D scene and its corresponding diffraction pattern, producing holograms with higher quality, better focal control, and reduced speckle noise.
Application: Real time 3D displays for virtual reality, heads up displays, and scientific visualisation.
Holographic Optical Elements (HOEs) for Smart Systems
HOEs are thin, lightweight optical components that use holographic principles to perform functions like focusing, beam splitting, and spectral filtering. In intelligent photonics, they become dynamic.
Intelligent HOEs: By using a Spatial Light Modulator (SLM) as a reconfigurable HOE, a single system can perform multiple tasks. An AI controller can determine the optimal holographic pattern needed on the SLM to achieve a specific goal (e.g., route an optical signal to a different port, shape a laser beam for a specific task, or create a complex optical trap).
Application: Reconfigurable optical interconnects in data centres, adaptive laser material processing, and advanced optical tweezers in biophotonics.
Holographic Imaging and Sensing with AI. Techniques like digital holographic microscopy (DHM) capture the entire wavefront of light scattered from an object. This generates vast amounts of complex data (amplitude and phase).
The Role of AI: AI acts as the 'intelligent' interpreter of this holographic data.
Automatic Classification: ML models can instantly identify and classify cells in a fluid (e.g. detecting malaria infected red blood cells) without labels.
Phase Unwrapping and De-noising: AI can quickly solve the challenging problem of phase unwrapping and remove noise, drastically improving image quality and reconstruction speed.
Synthetic Label Free staining: AI can predict what a label stained biological sample would look like, based only on its holographic phase image, saving time and cost.
Application: High throughput medical diagnostics, live cell imaging, and quality control in micro-fabrication.
Holography in Optical Neural Networks (ONNs). This is where holography contributes directly to the hardware of AI. ONNs use light to perform neural network calculations, promising massive speed and energy efficiency gains over electronic GPUs.
The Holographic Connection: The fundamental operation in a neural network is a matrix multiplication (weights × inputs). A hologram can be seen as a physical representation of a complex matrix.
Dynamic Weight Banks: An SLM can display a holographic pattern that acts as a reconfigurable weight matrix. The input data (modulated on a laser beam) passes through the SLM, and the resulting diffraction pattern is the result of the multiplication.
Free Space Interconnects: Holography is ideal for creating the dense, three dimensional interconnections between ‘neurons’ (light sources and detectors) in an ONN, mimicking the connectivity of the brain.
Application: Ultra fast AI inference for specific tasks like image recognition and radio frequency signal processing