ISSM2022 AI Algorithm Contest Smart Metrology Challenge Using Semiconductor Actual Tool Data

This contest aims to broaden the scope of practical research and development through the trial using actual data generated at the semiconductor manufacturing. This contest is to compete AI algorithms related to ”smart" inspection process, which is a key driver of competitiveness for semiconductor manufacturing fabs. It is required to create a "learning model to predict manufacturing results using sensor data" built into actual semiconductor manufacturing equipment. The contestants compete accuracy of predicting manufacturing results based on sensor data alone.

Entry has been closed.

Requirement

· Qualification: Students (Both individuals and teams can participate)

· Kaggle in Class: The participants are required to use Kaggle in Class, a tool used by hundreds of universities around the world to practice data analysis techniques. It is encouraged for those who want to try Kaggle-in-class for the first time.

· Language: Python or R

· Criteria: "Learning model to predict manufacturing results using sensor data" and evaluation.

　　　　　 Compete for prediction accuracy.

· Submission: The applicants are required to submit the following report and abstract for preliminary experiments.

1. Report on algorithm code to be submitted to Kaggle-in-Class

2. Abstract (1page of MS Word, template) to be submitted to ISSM secretariat

· Data sets: data actually used for semiconductor manufacturing are provided.

Training data - 958 rows, 312 columns including header.

TP is objective and explanatory variables are from 4th column onward.

Test data - 396 rows, 311 columns including header.

Note: The provided data sets are ONLY allowed to be used for ”ISSM2022 AI Algorithm Contest Smart Metrology Challenge Using Semiconductor Actual Tool Data”

Submission

1. Report on algorithm code to be submitted to Kaggle-in-Class

2. Abstract (1page of MS Word, template) to be submitted to ISSM secretariat

Upload your abstract (word file in ISSM template) to the following Drop Box.

The file Name must have your team name.　eg. Team-ISSM. docx

Drop Box for abstract submission

https://www.dropbox.com/request/0DYiyaFMeS3PTqE2WRii

Schedule

Entry due date: November 13, 2022

*It is highly recommended to complete entry sheet so that you can get the data with Kaggle-in-class ID.

Kaggle User application date: From September 20, 2022 (Schedule)

Report on Algorithm code due date: December 4, 2022

Abstract due date: December 4, 2022

Award and Award Examination Guidelines

Award

The Award Ceremony will be held during ISSM 2022 period (December 12-13, 2022).

Assignment supervisor

Prof. Manabu Kano

Human Systems Laboratory

Dept of Systems Science, Kyoto University

﻿ISSM2022 AI Algorithm Contest Smart Metrology Challenge Using Semiconductor Actual Tool Data

ISSM2022 AI Algorithm Contest Smart Metrology Challenge Using Semiconductor Actual Tool Data