AI-Driven Self-assembled Nanophotonic Crystals for High-performance Optical Computing - Kushwaha, Ragini; Raja, Adil; Pant, Seema (Malikussaleh University, Aceh, Indonesia, 2025)
A nanophotonic crystal (NPEC), however, can represent a transformative approach for AI-based optical computing and ultimately computing at the nanoscale, replacing individual components with the self-assembly of an infinite array of nanostructures on a chip with one vision foreseen as ultra-fast, energy-efficient artificial photonic neural network. Finally, this research proposes an innovative framework in an AI-driven self-assembly technique to create defect-free nanophotonic structures based on the tunable optical properties. Through the integration of AI-guided dynamic reconfiguration mechanisms, these crystals can dynamically reconfigure light propagation paths in real time and therefore boost very high computational speed and minimise power consumption. Based on the idea of AI-assisted refractive index tuning and programmable optical waveguides, the proposed system can be used to implement logic gates and deep learning operations, using which we can avoid traditional electronic computing. We experimentally validate the feasibility of this approach using matrix multiplications and a convolutional neural network (CNN) acceleration running up to 87-fold faster than comparable conventional silicon-based architectures. Furthermore, the self-assembled nanophotonic processors are integrated with quantum photonic systems for neuro-morphic computing of the near future. They address key challenges specific to photonic computing, and this advances the frontier of photonic computing in the areas of both fabricability, scalability and defect control, and energy efficiency. This suggests that optical AI hardware using AI-driven self-assembled nanophotonic crystals can significantly improve the operation of optical AI hardware, from very efficient and fast computing solutions for machine learning, data analytics, and AI-enhanced applications.