Objective: The learning objective of this module is for trainees to learn the fundamentals of scanning electron microscopy and gain hands on experiences through experimentation. Failure analysis tools are key role players in today’s physical inspection and assurance. Understanding how images are collected and the associated limitations and challenges when it comes to their application to electronics is critical to develop future tools and algorithms for assurance. Under this module we will provide lectures for understanding the principle of imaging and use other tools provided by our partner for virtual practice of SEM imaging. We will then provide a platform where users can remotely connect to the SEM machines at FICS and run imaging on different samples. Through this module, several algorithms will be developed to automate functions which will increase the productivity of remote imaging.
Target Audience: Government officers, Scientists
Prerequisite Knowledge and Skills:
- programming knowledge: Verilog HDL
- TeamViewer software
- A FPGA board experience
Resources Provided at the Training | Deliverables:
- Detailed description of set-ups used in training
- A video demo of the module
- Verilog scripts examples for analysis
Learning Outcome: This training has been performed on the TESCAN FERA3 and LYRA3 dual beam systems. Xilinx FPGA and AMD Opteron has been used to perform extensive SEM training. With this module, you will get the detailed understanding of performing high quality SEM Imaging. During the training, we will understand and discuss some of those applications as well. By end of this course trainees will understand how to operate Scanning Electron Microscope, and then how to use electron imaging in various other applications. In addition, automating scanning electron microscopy (SEM) imaging can save precious time for both researchers and operators. Therefore, during training we will also focus on how to do scripting to perform various operations automatically. Through this module, several algorithms will be developed to automate functions which will increase the productivity of remote imaging.