Optimization of 125-$\mu$m Heterogeneous Multi-Core Fibre Design Using Artificial Intelligence

We propose an automated heterogeneous trench-assisted multi-core fibre (MCF) design method. This method uses neural networks to speed up coating loss estimation by ~ 10 ⁶ times and using particle swarm optimization (PSO) algorithm to explore the optimal MCF design under various objectives and properties constraints. The latter reduces the permutation evaluations by ten orders of magnitude compared with the brute force method. The artificial intelligence (AI)-based method is used to design MCFs on two objectives: minimizing crosstalk (XT) and maximizing effective mode area (A _eff ). By optimizing XT with different A _eff and cutoff wavelength constraints combinations for 6-core fibres, we achieved -92.1 dB/km ultra-low XT for C+L band fibre and -64 dB/km for E+S+C+L-band fibre. Meanwhile, we explored the upper limit of A _eff given different bandwidth constraints resulting in a 6.82 relative core multiplicity factor. We performed capacity analysis of fibres for two transmission lengths. It is shown that bandwidth is the dominant factor while the increase brought by A _eff and the penalty caused by XT are relevantly small. Our fibres exceed the cutoff-limited capacity of the 7-core fibre in literature by 35.1% and 84.8% for 1200 km and 6000 km transmission respectively.