Finding Quantum Critical Points with Neural-Network Quantum States

Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.Comment: 19 pages, 12 figures, extended version of an accepted paper at the 24th European Conference on Artificial Intelligence (ECAI 2020

Oxide Growth Effects in Micron and Sub-Micron Field Regions: A Comparison Between Wet and Dry Oxidation

The silicon oxide growth in narrow spacing is affected by the compressive stress present in the oxide and a reduction of the growth rate (field oxide thinning) can occur. In this work it is shown that the stress present in the growing oxide under the oxidation mask induces a field oxide thickening phenomenon which is able to reduce the field oxide thinning one. Furthermore, the comparison between wet and dry oxidation points out the influence of the ammonia in developing the field oxide thinning