Digital image correlation (DIC) is of vital importance in the field of experimental mechanics, yet producing suitable DIC patterns for demanding in-situ (micro) mechanical tests remains challenging, especially for ultrafine patterns, despite the large number of patterning techniques reported in the literature. Therefore, we propose a simple, flexible, one-step technique (only requiring a conventional physical vapour deposition machine) to obtain scalable, high-quality, robust DIC patterns, suitable for a range of microscopic techniques, by deposition of a low-melting temperature solder alloy in the so-called island growth mode, without elevating the substrate temperature. Proof of principle is shown by (near-)room temperature deposition of InSn patterns, yielding highly dense, homogeneous DIC patterns over large areas with a feature size that can be tuned from as small as~10 nm to~2 μm and with control over the feature shape and density by changing the deposition parameters. Pattern optimisation, in terms of feature size, density, and contrast, is demonstrated for imaging with atomic force microscopy, scanning electron microscopy, optical profilometry, and optical microscopy. Moreover, the performance of the InSn DIC patterns and their robustness to large deformations is validated in two challenging case studies of in-situ micromechanical testing: (a) self-adaptive isogeometric digital height correlation of optical surface height profiles of a coarse, bimodal InSn pattern providing microscopic 3D deformation fields (illustrated for delamination of Al stretchable interconnects on a PI substrate) and (b) DIC on scanning electron microscopy images of a much finer InSn pattern allowing quantification of high strains near fracture locations (illustrated for rupture of a polycrystalline Fe foil). As such, the high controllability, performance, and scalability of the DIC patterns, created by island growth of a solder alloy, offer a promising step towards more routine DIC-based in-situ micromechanical testing.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.