PHM Data Challenge

Call for Participation

Click image above to download pdf

The PHM Data Challenge is a competition open to all potential conference attendees. This year the challenge is focused on predicting faults in an ion mill etching tool. Participants will be scored based on their ability to successfully predict in advance when faults occur.

This is a fully open competition in which collaboration is encouraged. The teams may be composed of any combination of students, researchers, and industry professionals. The results will be evaluated by the Data Challenge Committee and all teams will be ranked. The top three scoring teams will be invited to present at a special session of the PHM conference and will be recognized at the Conference banquet event (scheduled for the evening of Wednesday, September 26th).

Data Challenge Chairs (data@phmconference.org)
Jack Bonatakis, Seagate Technology, jack.bonatakis@seagate.com
Abbas Chokor, Seagate Technology, abbas.chokor@seagate.com
Nicholas Propes, Seagate Technology, nicholas.c.propes@seagate.com

Teams

Collaboration is encouraged and teams may be comprised of one or more students and professionals. The teams judged to have the first, second, and third best scores will be awarded prizes of $600, $400, and $200, respectively, contingent upon:

  • Having at least one member of the team register and attend the PHM 2018 Conference.
  • Submitting a peer-reviewed conference paper. Submission of the data challenge special session papers is outside the regular paper submission process and follows its own modified schedule.
  • Presenting the analysis results and technique employed at a special session within the Conference program.

The organizers of the competition reserve the right to both modify these rules and disqualify any team for any efforts it deems inconsistent with fair and open practices. In addition, the top entries will also be encouraged to submit a journal-quality paper to the International Journal of Prognostics and Health Management (ijPHM).

Data Challenge Registration

Teams may register by contacting the Competition organizers (emails above) with their name(s), affiliation, and a team alias under which the scores would be posted. Please note: In the spirit of fair competition, we allow only one account per team. Please do not register multiple times under different user names, under fictitious names, or using anonymous accounts. Competition organizers reserve the right to delete multiple entries from the same person (or team) and/or to disqualify those who are trying to “game” the system or using fictitious identities.

Key PHM Data Challenge Dates

Key Dates
Competition Open April 27, 2018
Training Data Posted and Scoring Website Open April 27, 2018
Final Validation Set Posted August 5, 2018
Competition Closed August 12, 2018 (12:00 pm PST)
Preliminary Winners Announced August 19, 2018
Winners Announced September 2, 2018
Final Papers Due, Winners Announced September 16, 2018
PHM Conference Dates September 24-27, 2018

System Description

This year’s data challenge examines the fault behavior of an ion mill etch tool used in a wafer manufacturing process (see references at the end of this document). An ion mill etching tool is shown in Figure 1. The process of ion mill etching typically consists of the following steps:

  1. Inserting a wafer into the mill
  2. Configure wafer settings (rotation speed, angles, beam current / voltages, etc.)
  3. Processing the wafer for a set amount of time
  4. Repeat 2 or 3 for different steps of recipe
  5. Remove wafer from mill


Figure 1. An Ion Mill Etching System.

An ion source generates ions that are accelerated through an electric field using a series of grids set at specific voltages. This creates an ion beam that travels and eventually strikes the wafer surface. Material is removed from the wafer when ions hit the wafer surface. The wafer is placed on a rotating fixture that can be tilted at different angles facing the incoming ion beam. The wafer can be shielded from the ion beam until ready for milling operation to commence using a shutter mechanism as shown in Figure 2. A Particle Beam Neutralizer (PBN) control system influences the ion beam shape / ion distribution as it travels to the wafer surface.

The wafer is cooled by a helium / water system called flowcool. The cooling system passes helium gas behind the wafer at a specified flow rate. The helium gas is indirectly cooled by a water system. The wafer and fixture o-ring separates the flowcool gas from the ion mill vacuum chamber.

Many different failure mechanisms can be present in this system including leaks between flowcool and ion mill chambers, electric grid wear, ion chamber wear, etc. It would be beneficial to predict where and when these failures occur and schedule downtime of these ion mills for maintenance operations.


Figure 2. Wafer and ion mill etching process.

Objectives
The objective of this data challenge is to build a model from time series sensor data collected from various ion mill etching tools operating under various conditions and settings.

  1. Diagnose failures (i.e. detect and identify)
  2. Determine time remaining until next failure (i.e. predict remaining useful life)

Predictions of time-to-failure at a specific time should only use time-series data from current and past times. In other words, do not try to predict the point of failure first and then backtrack through time to determine time-to-failure predictions.

Data Description
The data for this challenge will be available at https://drive.google.com/open?id=15Jx9Scq9FqpIGn8jbAQB_lcHSXvIoPzb. The description of the settings / sensor data can be found in the Table below. The data has been anonymized so the units are not provided.

ID# Parameter Name Type Description
S1 time Numeric time
S2 Tool Categorical tool id
S3 stage Categorical processing stage of wafer
S4 Lot Categorical wafer id
S5 runnum Numeric number of times tool has been run
S6 recipe Categorical describes tool settings used to process wafer
S7 recipe_step Categorical process step of a recipe
S8 IONGAUGEPRESSURE Numeric (Sensor) pressure reading for the main process chamber when under vacuum
S9 ETCHBEAMVOLTAGE Numeric voltage potential applied to the beam plate of the grid assembly
S10 ETCHBEAMCURRENT Numeric ion current impacting the beam grid determining the amount of ions accelerated through the grid assembly to the wafer
S11 ETCHSUPPRESSORVOLTAGE Numeric voltage potential applied to the suppressor plate of the grid assembly
S12 ETCHSUPPRESSORCURRENT Numeric (Sensor) ion current impacting the suppressor grid plate
S13 FLOWCOOLFLOWRATE Numeric rate of flow of helium through the flowcool circuit, controlled by mass flow controller
S14 FLOWCOOLPRESSURE Numeric (Sensor) resulting helium pressure in the flowcool circuit
S15 ETCHGASCHANNEL1READBACK Numeric rate of flow of argon into the source assembly in the vacuum chamber
S16 ETCHPBNGASREADBACK Numeric rate of flow of argon into the PBN assembly in the chamber
S17 FIXTURETILTANGLE Numeric wafer tilt angle setting
S18 ROTATIONSPEED Numeric wafer rotation speed setting
S19 ACTUALROTATIONANGLE Numeric (Sensor) measure wafer rotation angle
S20 FIXTURESHUTTERPOSITION Numeric open / close shutter setting for wafer shielding
S21 ETCHSOURCEUSAGE Numeric counter of use for the grid assembly consumable
S22 ETCHAUXSOURCETIMER Numeric counter of the use for the chamber shields consumable
S23 ETCHAUX2SOURCETIMER Numeric counter of the use for the chamber shields consumable
S24 ACTUALSTEPDURATION Numeric (Sensor) measured time duration for a particular step

The faults are marked in another file with corresponding time.

ID# Parameter Name Type Description
F1 time Numeric time (e.g. seconds)
F2 fault_name Categorical name of the particular class of fault that occurred at the specified time
F3 stage Categorical  

The time when the failure occurs is provided and is when the operator shuts down the machine for maintenance. This is what should be predicted. The actual start of the failure may occur much earlier than the provide failure time—this time is not provided.

The data is contained in a zip file. In this zip file, there are two folders, train and test. The train folder contains the training data used for modeling purposes. The test folder contains the test data that is to be used with your model to generate submissions of time-to-failure for the three different failure modes of interest: FlowCool Pressure Dropped Below Limit, Flowcool Pressure Too High Check Flowcool Pump, and Flowcool leak. The time where faults occur is found in the train/train_faults folder. Some time-to-failure examples are provided in the train/train_tff folder. There are ‘null’ values where faults do not occur in within a specified time horizon. The .csv files under the train folder represent the ‘sensor’ data that are used as predictors. Each of these files represent a separate ion milling tool.

Submissions
The scoring website (http://70.32.24.178:8080) is where submissions are to be sent for automated scoring on the test data set. Please contact nicholas.c.propes@seagate.com to create an account with the following information:

  • Team Name:
  • Team Members Real Names:
  • Team Contact Email:
  • Team Affiliation:

A submission consists of a single .zip file. This file is constructed by creating a folder called ‘test’ and placing the separate prediction files within. These prediction files should have the same filename and same number of rows as the corresponding ‘sensor’ data file. However, the prediction file should have the following columns: time, TTF_FlowCool Pressure Dropped Below Limit, TTF_Flowcool Pressure Too High Check Flowcool Pump, and TTF_Flowcool leak. There should be one prediction file per ‘sensor’ data file. Use the data in the test folder from the supplied data to create the 5 prediction files to place into your own test folder for submission. Only one submission per day per team is allowed.

Scoring
Scoring is computed by comparing the TTF submission with a ground truth TTF. Each TTF prediction has a subscore that is computed with the following rules:

Ground Truth TTF (GT) Submission TTF (SUB) Score
Number Number exp(-0.001*GT)*abs(GT-SUB)
NaN Number exp(-0.001*SUB)*SUB
Number NaN exp(-0.001*GT)*GT
NaN NaN 0

The subscores for each prediction are summed and then divided by the total number of cells for each file. The file scores then then summed. A better score is one that is lower.

References

  1. http://www.ionbeammilling.com/about_the_ion_milling_process
  2. https://www.azom.com/article.aspx?ArticleID=7533
AttachmentSizeTimestamp
PHM Data Challenge 2018 vFinal v2.pdf554.55 KBMay 15, 2018 - 6:59pm
  
 
 
 

follow us

PHM Society on Facebook Follow PHM Society on Twitter PHM Society on LinkedIn PHM Society RSS News Feed