WSC 2008 Final Abstracts

Demand Forecast of Semiconductor Products based on Technology Diffusion
Chen-Fu Chien and Yun-Ju Chen (National Tsing Hua University) and Jin-Tang Peng (Yuanpei University)

Abstract:
Demand forecast plays a critical role to determine capital investment for capacity planning. Given the involved uncertainties and long lead-time for capacity expansion, semiconductor companies have to predict future demands as a basis for related manufacturing strategic decisions. As semiconductor products in a consumer era become more diversified with shortening life cycle, demand forecast also becomes more complex and difficult. This study aims to develop a demand forecast model based on product life cycle and technology diffusion. While little research has been done to employ diffusion models to forecast the demands of semiconductor products. The proposed model modifies a multi-generation diffusion model incorporating marketing variables into the model for semiconductor product and uses nonlinear least square method to estimate the parameters. An empirical study is conducted to validate the proposed model with real data of semiconductor products. This research concludes with discussion on future research directions.

A Bayesian Framework to Integrate Knowledge-Based and Data-Driven Inference Tools for Reliable Yield Diagnoses
Chih-Min Fan (Yuan Ze University)

Abstract:
This paper studies the issues of designing a Bayesian framework for the reliable diagnosis of various yield-loss factors induced in semiconductor manufacturing. The proposed framework integrates both the results from knowledge-based and data-driven inference tools as input data, where the former resembles expert's knowledge on diagnosing pre-known yield-loss factors while the latter serves for exploring new yield-loss factors. Three modules with specific designs for yield diagnosis applications are addressed: Pre-Process for generating candidate factors and corresponding prior distributions, Bayesian Inference for calculating posterior distributions, and Post-Process for deriving reliable rankings of candidate factors. The final output, a Bubble Diagram with Pareto Frontier, provides visual interpretations on the integral results from data-driven, knowledge-based and Bayesian inference tools. Specific issues addressed in the proposed Bayesian framework provide directions for implementing a real system.

Systematic Applications of Multivariate Analysis to Monitoring of Equipment Health in Semiconductor Manufacturing
A.G. Chao (National Tsing-Hua Unversity), S.P. Lee (Tsing-Yu University) and S.T. Tseng, David, S.H. Wong, and Shi-Shang Jang (National Tsing-Hua University)

Abstract:
In this work, a systematic procedure of building a model for monitoring batch processes in semiconductor manufacturing and visualization of monitoring results will be presented. Semiconductor manufacturing batch-processing stages usually consist of many steps. Aging trends likely to be detected only in the steady state period of each step. Large fluctuations can be found in on-off period of each step. Hence "step-trend" variables, i.e. mean shifts from reference profiles of each steps, are defined to track aging trends. Residuals from this shifted profile are then used to provide a combined health index of each batch through Hotelling T2 analysis.

Automated Generation and Parameterization of Throughput Models for Semiconductor Tools
Jan Lange (AMD Saxony LLC & Co. KG), Oliver Rose (Dresden University of Technology) and Kilian Schmidt and Roy Boerner (AMD Saxony LLC & Co. KG)

Abstract:
Cluster tools play an important role in modern semiconductor fabs. Due to their complexity in configuration and their varying material flow, the creation of accurate throughput models for cluster tools is a demanding task. Proposed analytical approaches are either quite intricate and require manual maintenance process, or are inexact due to reliance on lot-level events. This paper presents an approach for the automated generation and parameterization of detailed cluster tool models based on bottleneck analysis. Equipment configuration as well as all throughput model base data is extracted from recent equipment reporting data.

Toward On-Demand Wafer Fab Simulation Using Formal Structure & Behavior Models
Edward Huang, KySang Kwon, and Leon McGinnis (Georgia Institute of Technology)

Abstract:
Contemporary factories in capital intensive industries such as semiconductor manufacturing are very complex, with many sources of risk. The highly competitive and global business environment forces companies to analyze, design, and continuously re-design factories with distributed multi-disciplinary teams. Traditional factory design approaches using spreadsheets and stand-alone simulations cannot adequately cope with the resulting time, cost, and risk requirements. In this paper, we address the opportunity to achieve support fab design teams by providing on-demand simulation. The method for achieving this combines for-mal fab descriptive models with a process for generating fab analysis models from relatively standard sources of fab data.

Using OMG's SysML to Support Simulation
Christiaan J.J. Paredis and Thomas Johnson (Georgia Institute of Technology)

Abstract:
Currently, system engineering problems are solved using a wide range of domain-specific models and corresponding languages. It is unlikely that a single unified modeling language will be able to model in sufficient detail the large number of system aspects addressed by these domain-specific languages. Instead, a model integration framework is needed for managing the various modeling languages used to solve systems engineering problems. The Systems Modeling Language (OMG SysMLTM) can provide an answer to this need for model integration. Using SysML, a modeler can abstract a domain-specific language to a level that permits its interaction with other system models. In addition, graph transformation approach can be use to accomplishing automated, bidirectional transformation between SysML and the domain specific language. In this paper, a generic approach for defining such graph transformations is presented.