Understanding the electronic structure of materials is crucial for the rational design and operation of modern electronic devices, particularly semiconductors. The performance of these devices is fundamentally governed by the arrangement and behavior of electrons. This tutorial will provide a comprehensive overview of photoelectron spectroscopy (PES) as a powerful technique for probing and analyzing these electronic properties. We will first explain the basic principles of PES, including the photoelectric effect, and how it can be used to directly measure electron binding energies. The presentation will then explore how PES can be used to understand the electronic structures of various materials, focusing on key concepts such as energy levels, bandgaps, and quasiparticle behaviors of electrons. We will discuss practical applications of PES, including the measurement and interpretation of energy band diagrams and the determination of energy level alignments at interfaces, which are critical for device performance. The goal of this session is to provide attendees with the fundamental knowledge necessary to use PES for advanced material characterization and device physics rationalization.

The rapid growth of artificial intelligence and data-intensive applications has exposed fundamental limitations in conventional von Neumann computing systems, particularly the energy and latency overheads caused by frequent data movement between memory and processors. In-memory computing has emerged as a promising paradigm to address this challenge by performing computation directly within memory arrays. Among various candidates, emerging memory devices such as resistive switching memories and capacitive devices have attracted significant attention due to their scalability, non-volatility, and compatibility with existing semiconductor processes. This tutorial will introduce the fundamental device physics of emerging memories and their integration into array architectures for in-memory computing. Particular emphasis will be placed on how device characteristics affect system-level performance in machine learning. Furthermore, recent advances in crossbar arrays with memristive devices and memcapacitive devices will be discussed to highlight the trade-offs between accuracy, energy efficiency, and scalability.