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About Us:
Micron Technology\'s vision is to transform how the world uses information to enrich life. It is our mission to be a global leader in memory and storage solution! We have five meaningful values: People, Innovation, Tenacity, Collaboration and Customer Focus. We conduct business with Integrity, accountability and professionalism while supporting our global community. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We recruit, hire, train, promote, discipline and provide other conditions of employment without regard to a person\'s race, color, religion, sex, age, national origin, disability, sexual orientation, gender identity and expression, pregnancy, veteran\'s status, or other classifications protected under law. This includes providing reasonable accommodation for team members\' disabilities or religious beliefs and practices.
Location
1 North Coast Drive, Singapore
Department:
F10N Manufacturing Diffusion
Project Title:
Development of Robotic Process Automation (RPA) and machine learning models to identify post processed wafer defects signatures and trigger automated reaction mechanism.
Description:
The production of Semiconductor chips is a multi-step process, between several processes, dedicated machines were used scan the wafers to ensure the wafers are clean before moving on to the next process. Although the production of semiconductor wafers takes place in a cleanroom, it is common to find residual particles littered across the wafer surface after being processed. This is likely caused by chemical reactions between different chemicals during processing. These particles could cause potential yield lost or even scaping entire wafer if left unchecked. Through several trails and tests, different cleaning recipes were created and implemented to purge out residual particles in the tool before processing the next batch of wafers. These cleaning recipes are created with different combination of gas flows and temperatures. Different patterns of particle issue may be a result of underlying issues within the tool. For example, streak patterns may signify issues with the specific injectors or gas regulators, randomly scattered particles may signify that the tool is saturated with residual particles. Currently, such defect patterns could only be accurately classified by a pair of trained human eyes and reaction to such defects are also manually triggered.
Through this internship, the project strives to use machine learning model to identify the types of defect pattern expeditiously and also to develop a RPA workflow to automatically trigger appropriate reaction mechanism to rectify the particle issues. This greatly reduces the time and manpower needed thus reducing cost.
Aim of the project:
Utilize machine learning models to identify, classify and recognize wafer defect patterns and automate appropriate reaction mechanism process with the aid of Robotic Process Automation (RPA) software
Scope:
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