This document discusses virtual metrology in semiconductor manufacturing. Virtual metrology aims to estimate metrology values using process data and previous metrology information, as it is difficult to measure every wafer. The methodology involves three stages: data pre-processing to ensure quality input data; virtual metrology module development to build and validate prediction models; and virtual metrology module implementation to integrate the module into production. Virtual metrology provides benefits like increased production time and predictive maintenance by enabling continuous metrology information without needing to sample and measure every wafer.

1. VIRTUAL METROLOGY IN
SEMICONDUCTOR MANUFACTURING
Guided by Prof. ANAND RAJAGOPAL
ROSHAN SUNIL KURIAN
MUT15ME051

2. CONTENTS
■ INTRODUCTION
■ LIMITATION OF CURRENT METROLOGY
■ VIRTUAL METROLOGY
■ METHODOLOGY
■ ADVANTAGES
■ DISADVANTAGES
■ REFERNCES

3. INTRODUCTION
■ A process is controlled based on the information obtained.
■ Metrology values are available only for a small fraction of samples.
■ To overcome this, Virtual Metrology is used.

4. LIMITATIONS OF CURRENT METROLOGY
■ Metrology value depends on few sampled wafers.
■ Usage of metrology results is restricted.
■ Increased cycle time.

5. VIRTUAL METROLOGY
■ Prediction of metrology variables using information about the state of process or
product.
■ Class of methods aiming to estimate the metrology values, given the process data and
previous metrology information.
■ In practise it is difficult to measure each wafer.
■ So Virtual Metrology is used.

8. METHODOLOGY
■ Methodology is composed of three successive stages:
– Data Pre-processing
– VM Module Development
– VM Module Implementation

9. 1. DATA PRE-PROCESSING
■ Assuring the input quality of the data.
■ Three steps are involved:
– 1. Data Sources
■ To define family of products.
■ Selecting recipes and steps for VM module development.
■ Preliminary study of processes and metrology equipment's is necessary.

10. 1. DATA PRE-PROCESSING
– 2. Data Acquisition
■ To define raw data set including FDC data from production equipment and
measurement data from metrology equipment.
– 3. Data Consolidation
■ Includes data cleaning and statistical data analysis.
■ Identifying and removing outliers, missing values etc.. .
■ Statistical analysis include data normalization , data correlation studies.

11. 2. VM MODULE DEVELOPMENT
■ Aims to build different prediction models , compare them and validate the best model.
■ Three steps are involved:
– 4. VM Modelling
■ To choose nonlinear prediction methods.
■ Build prediction model in two levels.

12. 2. VM MODULE DEVELOPMENT
– 5. Model Comparison
■ Models compared based on robustness and prediction accuracy criteria.
■ To select the best model based to the performance.
– 6. VM Module
■ To perform the VM module with adjustments of the best model chosen in the previous
step.

13. 3. VM MODULE IMPLEMENTATION
■ To define the steps to integrate the VM module into the industrial environment.
■ This stage includes three steps :
– 7. VM Module Test
■ To perform off line test with the offline data from production.
■ To identify the problems of the model and to evaluate the results.
■ To define a prototype for offline VM Module Implementation.

14. 3. VM MODULE IMPLEMENTATION
– 8. VM Module in Production
■ Provide guidelines for full integration of VM module into manufacturing system.
– 9. VM Module Consolidation
■ To define the maintenance policies for the update of real time VM module.

15. ADVANTAGES
■ Increased production time.
■ Low scrap rate.
■ Predictive maintenance.
■ Real time tool performance.
■ Continuous information.

16. DISADVANTAGES
■ Large number of input variables.
■ Process is not stationary.

17. REFERENCES
■ Virtual metrology for run-to-run control in semiconductor manufacturing, Pilsung Kang
a, Dongil Kim a, Hyoung-joo Lee b, Seungyong Doh c, Sungzoon Cho a, Expert
Systems with Applications 38 (2011) 2508–2522
■ University of Padua, Department of Information Engineering, Virtual Metrology for
Semiconductor Manufacturing Applications, Prof. Beghi Alessandro, Gian Antonio
Susto, Padua, 28 June 2010

18. THANK YOU