Resistive Random-Access Memory (ReRAM) is a leading next-generation non-volatile memory based on resistance switching in a simple Metal–Insulator–Metal cell. This seminar first reviews ReRAM theory. It then presents an experimental application: LaXO₃ perovskite thin films (X = Fe, Mn, Cr, Ni) synthesized by low-cost sol–gel spin coating using a self-made spin coater. Films were characterized by XRD, FESEM, UV-Vis, and VSM, and Au/LaXO₃/FTO devices were fabricated and electrically tested. All devices showed bipolar resistive switching; LaFeO₃ performed best, with an ON/OFF ratio of ~7 × 10⁵, low operating voltages (0.7 V / −1 V), and stable endurance. The work demonstrates that simple spin coating can produce high-quality perovskite RRAM devices.

Machine learning offers new tools for addressing complex problems across many fields of science. In theoretical physics, the exponential growth of the many-body Hilbert space makes exact calculations challenging and motivates approximation methods. This seminar introduces how neural networks can represent many-body wavefunctions and approximate ground states through variational energy minimization. After outlining the basic neural-network machinery, we illustrate the approach using the quantum Ising model and discuss its advantages and limitations.

All faculty, researchers and students are invited to attend.