Ultra-wide broadband and low-loss optical transformers for structured light

Optical transformations are widely used for image processing, optical communications, quantum information and sensing. However, the design processing for these approaches usually results in transformations that are only suitable for a few select wavelengths and are implemented using inefficient diffractive optical approaches. The aim of the project is to

pioneer the development of a novel AI assisted approach to design cascaded optical transforms made of multiple conformal mappings. This design approach will enable the creation of wideband and low loss mode dependent optical couplers for integrated photonics and will serve as the interface between free-space or fibre for a range of applications demonstrated in

both Quantum or Classical communication and sensing. Multiple Plane Light Conversion (MPLC) technology has been demonstrated for a range of applications including space division multiplexing, environmental simulations, and digital holography [Fontaine]. MPLCs are computationally generated using wavefront matching

techniques, which are powerful approaches for the iterative generation of a unique set of masks to map a set of modes at the input plane to a set of modes at the output plane. Fontaine et al. have demonstrated the demultiplexing of 210 spatial modes using 7 phase planes with optima performance at 1550nm with approximately 4dB higher transmission losses at the extremes of

the c-band [Fontaine]. The number of required planes has been hotly debated, where for general transformations it is commonly considered that one needs at least N mask for the transformation of N modes. However, in particular cases, the number of masks can be greatly reduced if the input and output sets are chosen correctly. A key question in the research area is

what the optimal number of screens are. A further core issue with the wavefront matching techniques, is they are inherently wavelength selective as the phase profile of each optical element is iteratively varied and a loss function is minimised to produce the optimised set of phase masks. However, this approach commonly leads to non-physical solutions that are not

valid for experimental realisation as these methods are based on Fourier propagation modelling [Fontaine]. The project will develop a new approach in the design of MPLC optical systems, which will utilise AI to determine the minimum number of conformal mappings to transform from the input to the output. Hossack et al., outlined a proof that any single plane can perform a conformal

mapping in the far field of that plane [Hossack]. Cascaded conformal mapping could subsequently transform any arbitrary input to any other output and in fact would determine the theoretical minimum number of transformations required. When such mappings are well defined, as previously demonstrated by [Berkhout, Lavery], two planes can be used to sort over

50 spatial modes. However, the determination of the correct conformal mapping is nontrivial for arbitrary transformations. Utilising a physical propagation trained neural networks, which could be designed to recognise possible combinations of transformations to achieve the desired reshaping from a known input to a required output.

Once designed, these surfaces will be fabricated using diamond turning from an external provider and utilised for hybrid communication and sensing applications in both fibre and in free space.