Materials Science. Non-Equilibrium Phase Transformations.
Vol. 12 (2026), Issue 1, pg(s) 3-21

Artificial intelligence-assisted WDM multiplexing in multicore plastic optical fibers

  • 1 Faculty of Engineering, Holon Institute of Technology (HIT), Israel

Abstract

This work presents an artificial-intelligence-assisted design methodology for wavelength-division multiplexing (WDM) in multicore plastic optical fibers (MC-POFs) operating in the green visible spectrum. The device is based on polycarbonate (PC) guiding cores embedded in a CYTOP background and uses controlled inter-core coupling to combine four wavelength channels at 500, 520, 540, and 560 nm into one output core. The electromagnetic response is evaluated with the beam-propagation method (BPM) in RSoft Photonics CAD, while an external AI-assisted optimization layer is used to rank candidate geometries, refine the switching length, and search the coupled parameter space defined by the air-hole diameter, lattice pitch, inter-core gap, and channel separation. The physical simulator remains the source of the optical field solution; the AI layer is used as an optimization and decision-support mechanism rather than as a replacement for Maxwell-based propagation modeling. The optimized structure has a total active length of 20 mm, a pitch of 549.8 nm, an air-hole diameter of 241.6 nm, an inter-core gap of 1.099 µm, and a normalized diameter-to-pitch ratio of 0.4395. Simulated transmission reaches 88%, 91%, 95%, and 97% for the four channels, corresponding to insertion losses between 0.55 and 0.13 dB. The wavelength-dependent coupling lengths decrease from 395.33 µm at 500 nm to 153.52 µm at 560 nm, allowing a common switching-length condition to be approached with near-integer coupling ratios. Thermal drift analysis indicates stable operation for a worst-case ±3 nm wavelength displacement. A two-channel experimental demonstration at 500 and 540 nm provides additional support for the spatial multiplexing concept. The resulting platform combines compact WDM routing with AI-assisted photonic optimization and offers a pathway toward low-complexity visible-light links, sensing networks, and photonic neural interconnects.

Keywords

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