Particle acceleration, and the thermalisation of energetic particles, are fundamental processes across the universe. Whilst the Sun is an excellent object to study this phenomenon, since it is the most energetic particle accelerator in the Solar System, this phenomenon arises in many other astrophysical objects, such as active galactic nuclei, black holes, neutron stars, gamma ray bursts, solar and stellar coronae, accretion disks and planetary magnetospheres. Observations in the Extreme Ultraviolet (EUV) are essential for these studies but can only be made from space. Current spectrographs operating in the EUV use an entrance slit and cover the required field of view using a scanning mechanism. This results in a relatively slow image cadence on the order of minutes to capture inherently rapid and transient processes, and/or in the spectrograph slit `missing the action'. The application of image slicers for EUV integral field spectrographs is therefore revolutionary. The development of this technology will enable observations of EUV spectra from an entire 2D field of view in seconds, over two orders of magnitude faster than what is currently possible. The Spectral Imaging of the Solar Atmosphere (SISA) instrument is the first integral field spectrograph proposed for observations at 180A combining the image slicer technology and curved diffraction gratings in a highly efficient and compact layout, while providing important spectroscopic diagnostics for characterization of solar coronal and flare plasmas. SISA's characteristics, main challenges and the on-going activities to enable the image slicer technology for EUV applications are presented in this paper.

Extreme Ultraviolet (EUV) photolithography process, is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary Least Squares (OLS) is the most widely used method for parameters tuning of Overlay models but in most cases it fails to compensate for such phenomena. In this paper we propose the usage of Ridge Regression, a widely known Machine Learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method has been applied in perturbed data from a 300 mm wafer fab and the results show reduced residuals when ridge regression is applied instead of OLS.