  Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a
promising candidate for the next-generation metrology solutions in the
semiconductor industry, as it can image wafer samples in reflection geometry at
the nanoscale. This technique has surged attention recently, owing to the
significant progress in high-harmonic generation (HHG) EUV sources and
advancements in both hardware and software for computation.
  In this study, a novel algorithm is introduced and tested, which enables
wavelength-multiplexed reconstruction that enhances the measurement throughput
and introduces data diversity, allowing the accurate characterisation of sample
structures. To tackle the inherent instabilities of the HHG source, a modal
approach was adopted, which represents the cross-density function of the
illumination by a series of mutually incoherent and independent spatial modes.
  The proposed algorithm was implemented on a mainstream machine learning
platform, which leverages automatic differentiation to manage the drastic
growth in model complexity and expedites the computation using GPU
acceleration. By optimising over 200 million parameters, we demonstrate the
algorithm's capacity to accommodate experimental uncertainties and achieve a
resolution approaching the diffraction limit in reflection geometry. The
reconstruction of wafer samples with 20-nm heigh patterned gold structures on a
silicon substrate highlights our ability to handle complex physical
interrelations involving a multitude of parameters. These results establish
ptychography as an efficient and accurate metrology tool.
