Skip to content

Temperature Dependence of Elemental Depth Profiles at the Au/Si Interface Using Confocal Micro X‐Ray Fluorescence Analysis

Sep 2026 · X-Ray Spectrometry · 12 references
X-ray Spectroscopy and Fluorescence Analysis

Abstract

ABSTRACT This study aims to establish a method for nondestructively evaluating the elemental diffusion behavior at metal/semiconductor interfaces in situ, which is extremely important for reliability assessment of next‐generation semiconductor devices. Confocal micro X‐ray fluorescence spectrometry (CMXRF) was employed to capture the dynamic changes during the heating process, which are difficult to observe using conventional destructive analysis methods such as transmission electron microscopy and Auger electron spectroscopy. Using a gold‐deposited silicon wafer as a model sample, quantitative evaluation of changes in depth distribution under different temperature conditions (60°C, 100°C, and 110°C) was performed using atmospheric‐type and vacuum‐type CMXRF systems. As a result of the experiments, no significant diffusion was observed after heating at 60°C for 9 h. However, under conditions of 100°C and 110°C, a decrease in the peak intensity of the Au Lα line and an increase in the full width at half maximum of the profile were confirmed. Particularly in the in situ measurements at 100°C, we successfully captured the dynamic behavior of Au intensity decreasing within approximately 120 min from the start of heating. These results suggest that CMXRF is an effective method for nondestructively tracking metal diffusion phenomena in semiconductor devices in situ.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.