Wavelength-multiplexed Multi-mode EUV Reflection Ptychography based on Automatic-Differentiation

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.

Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics

Optical measurements often exhibit mixed Poisson-Gaussian noise statistics, which hampers image quality, particularly under low signal-to-noise ratio (SNR) conditions. Computational imaging falls short in such situations when solely Poissonian noise statistics are assumed. In response to this challenge, we define a loss function that explicitly incorporates this mixed noise nature. By using maximum-likelihood estimation, we devise a practical method to account for camera readout noise in gradient-based ptychography optimization. Our results, based on both experimental and numerical data, demonstrate that this approach outperforms the conventional one, enabling enhanced image reconstruction quality under challenging noise conditions through a straightforward methodological adjustment.

Sensor fusion in ptychography

Ptychography is a lensless, computational imaging method that utilises diffraction patterns to determine the amplitude and phase of an object. In transmission ptychography, the diffraction patterns are recorded by a detector positioned along the optical axis downstream of the object. The light scattered at the highest diffraction angle carries information about the finest structures of the object. We present a setup to simultaneously capture a signal near the optical axis and a signal scattered at high diffraction angles. Moreover, we present an algorithm based on a shifted angular spectrum method and automatic differentiation that utilises this recorded signal. By jointly reconstructing the object from the resulting low and high diffraction angle images, the resolution of the reconstructed image is improved remarkably. The effective numerical aperture of the compound sensor is determined by the maximum diffraction angle captured by the off axis sensor.