Denoising strategies for three-dimensional visualization of multilayer thin-film interfaces via angle-resolved hard X-ray photoelectron spectroscopy

Non-destructive three-dimensional visualization of buried interfaces in multilayer thin films is central to semiconductor device characterization. Angle-resolved hard X-ray photoelectron spectroscopy (AR-HAXPES) enables such depth-resolved analysis, but short-exposure measurements suffer from photon shot noise that degrades the reconstructed depth profiles - a problem that becomes acute as the technique is pushed toward spatiotemporal "4D-XPS" at focused-beam instruments. Here we benchmark two denoising strategies-statistical bin-pool resampling (BP) and self-supervised deep neural network (DNN) denoising-applied to AR-HAXPES data from a C/Al2O3/TiO2/Si multilayer film acquired with a laboratory Ga Kα source (9.25 keV) under two per-frame noise levels (24 s/frame × 2500 frames; 120 s/frame × 400 frames). Depth profiles are reconstructed per pixel via L1-regularized inversion and scored against a pseudo ground truth using Depth RMSE, film-thickness fidelity Δthk, and the first-order Wasserstein distance W1. The two strategies turn out to be complementary rather than competing: under high-noise conditions the self-supervised DNN dominates with up to 27-fold equivalent-exposure gain, whereas under low-noise conditions BP matches or slightly surpasses the DNN at roughly 6- to 8-fold gain and retains a clear advantage in reconstructed layer-thickness fidelity. BP - training-free and computationally trivial - is therefore the preferred choice whenever the per-frame statistics are sufficient for the inversion to resolve interfaces, while the self-supervised DNN remains justified specifically when photon starvation is severe.