The application of denoising machine learning to STM data has several advantages, such as improving data quality, aiding visual interpretation of data, and speeding up measurement time. With...Show moreThe application of denoising machine learning to STM data has several advantages, such as improving data quality, aiding visual interpretation of data, and speeding up measurement time. With experimental data, the absence of a ground truth poses a problem for traditional supervised learning techniques. In this work, state-of-the art self-supervised machine learning techniques are applied to reduce noise in quasiparticle interference data of overdoped cuprates, using only the noisy measurements. The machine learning methods are shown to outperform traditional denoising methods. Further ideas to improve and generalize the denoising of quasiparticle interference data are proposed.Show less
To study the coupling mechanism in high-Tc superconductors we would like to observe them using STM while suppressing the superconductivity with high currents. As the superconductor under study we...Show moreTo study the coupling mechanism in high-Tc superconductors we would like to observe them using STM while suppressing the superconductivity with high currents. As the superconductor under study we choose Bi2S2C1C2O8+x because of easy exfoliatability and doping. Wanting to achieve the required high current densities we decide on lithographically contacting a flake and performing a cleaving in the STM. Several methods are attempted and successful cleaving outside of the STM is achieved. The procedure, however, cannot be reproduced reliably inside the chamber. The journey leading to the result does yield some promising insights to complete the final step.Show less
