Surface dynamics and memory phenomena in oxide thin films grown by Plasma-Enhanced Atomic Layer Deposition

• Grant number: 24/17787-8
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: March 01, 2025
• End date: February 28, 2026
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Victor Lopez Richard
• Grantee: Ana Luiza Costa Silva
• Supervisor: Fabian Hartmann
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Institution abroad: Julius-Maximilians-Universität Würzburg (JMU), Germany
• Associated to the scholarship: 23/17490-2 - Influence of external effects on the properties of resistive memory of metal oxide semiconductor films, BP.PD

Abstract

As artificial intelligence and brain-inspired computing evolve, materials science is poised to drive transformative advancements in next-generation technologies. Traditional von Neumann computing architectures face significant energy inefficiencies due to the high data transfer demands between physically separated processing and memory units. Memristive devices offer a promising alternative, with potential applications in neuromorphic systems, non-volatile memory, and reservoir computing. However, challenges such as variability in device parameters and limited cycling endurance continue to hinder their full potential. This project seeks to overcome these limitations by conducting an in-depth analysis of memory transition phenomena based on fundamental solid-state transport mechanisms. Focusing on ZnO and HfO2 thin films, deposited via plasma-enhanced atomic layer deposition (PE-ALD) under various conditions, our study will examine memory effects using electron beam lithography to explore diverse sample geometries. Combining experimental investigation with theoretical simulations, this research aims to uncover the mechanisms driving memory behavior, correlating structural parameters with operating time scales. The results will inform strategies to optimize energy efficiency, improve operational stability, and assess miniaturization effects, contributing to the scalability, reliability, and performance of future memory and computing technologies.