Second semester 2021
Andrés Marrugo, PhD
Universidad Tecnológica de Bolívar
Aims and Scope
This semester course is an introduction to optical metrology. It is aimed at graduate students in the Faculty of Engineering. We will focus on the practical and theoretical aspects of techniques in optical metrology.
At the end of the lectures, one would be able to:
- Have clear idea of challenges in metrology due to increasing trend towards miniaturisation.
- Understand many different metrological devices and principles and applicability of those devices.
- Understand the process and provide metrological solution for the improvement of a process.
We will be using Jupyter Python notebooks as a numerical computing and graphical platform for solving many problems in the course. To avoid installing Jupyter Python locally, I encourage you to use Google Colab.
Tutorials, review materials
- Linear algebra review
- Random variables review
- Linear algebra with Numpy
- Manipulating images in Python/OpenCV
- Data analysis with Pandas
- Visualisation with matplotlib
This website will be updated as we go along.
Lecture 1: Introduction
We will be discussing the main aspects about metrology and why it is so important in manufacturing.
Lecture 1 slides
Lecture 2: Random Data and Characterization of Measurement Systems
In this lecture we will be discussing about random data, their properties and measurement systems. We will also discuss static and dynamic characterization of measurement systems.
Lecture 2 slides
- J Bendat and A Piersol - Random Data - Chapter 1
- Bajorski- Fundamentals of Statistics
- A student’s guide to Data and Error Analysis - Chapter 5 and 7
- A Beginner’s Guide to Uncertainty of Measurement
- Linear regression
Lecture 2: Cont’d
In this lecture we focus on practical aspects and calculations of characterization of measurement systems, calibration and uncertainty via confidence interval estimation.
For the calculations we will be using Python via Jupyter notebooks. You can download the Anaconda distribution that contains Python and many more scientific packages or use Google Colab.
The notebooks for this lecture:
- A Statistical Overview on Univariate Calibration, Inverse Regression, and Detection Limits
- Notes on device calibration
- J Bendat and A Piersol - Statistical Principles - Chapter 4
Data characterization and fitting. Solve the exercises in the Illustration of confidence intervals notebook and the fitting data notebook. Submit a ZIP file (firstname-lastname.zip) with the two notebooks in .ipynb format through the upload link. Due date: 2021-08-27.
Lecture 3: Basic Optical Principles and Imaging Systems
In this lecture we will discuss the Basic Optical Principles and Imaging Systems.
Lecture 4: Methods in Surface Metrology
In this lecture we introduce several basic concepts about surface measurements and characterization. The quantification of roughness and the different roughness scores. How roughness is related to the manufacturing process.
Please read the Robust 3D surface recovery paper below.
Lecture 4 slides
- Robust 3D surface recovery paper
- David Whitehose - Surfaces and their Measurement - Chapters 2 and 4
Surface roughness characterization. Due date: 2021-09-30.
- Assignment 2
- Upload link
- White-Light Interference 3D Microscopes
- Standard 3D matlab .mat file
- Standard 3D in csv format
Suplementary material for assignment 2
Lecture 5: Interferometry
In this lecture we will be discussing about the fundamentals of interferometry and its applications.
Lecture 5 slides