Arnon Max Hurwitz

Run-to-run control in semiconductor manufacturing

Introduction What is Run-top-Run Control? Target Audience Purpose of this Book Outline Background Current State-of-the-Art in Semiconductor Manufacturing Process Control History of the Development of Run-to-Run Control Current State o-of-the-Art in Run-to-Run Control Development and Deployment Simple Example: Run-to-Run Control and Comparison to Statistical Process Control Identifying Target Applications for Run-to-Run Control Class of Applications that can Utilize Run-t0-Run Control General Run-to-Run Control Development and Deployment Process Issues in Deploying Run-to-Run Control Developing a Run-to-Run Solution: Run-to-Run Algorithms Introduction Linear Approximation Algorithms Higher Order Approximation Algorithms Neural Network Algorithms Other Approaches Developing a Run-to-Run Solution: Practical Extensions to Algorithms Developing and Deploying Run-to-Run Solutions: Integrating Control Introduction The Generic Cell Controller Other Approaches Integrating into Factory-Wide Manufacturing System Run-to-Run Solution Development, Deployment, and Customization Methodology Introduction Process Identification Choosing a Run-to-Run Control Solution Customizing the Run-to-Run Control Solution to the Process Issues Run-to-Run Control System Deployment Case Studies Chemical-Mechanical Planarization Vapor Phase Epitaxy Advanced Topics Feasibility Analysis of Run-to-Run Control Solutions Stability Analysis of Run-to-Run Control Solutions Combining Process Run-to-Run Control with Inter-Process Control Conclusions Summary of Run-to-Run Development and Deployment Process Deploying Run-to-Run Control in a Timely and Cost Effective Manner Overcoming Barriers to Deployment Future Research and Development Issues References

A multilevel approach to the control of a chemical–mechanical planarization process

A multilevel hierarchical control system has been designed and is being applied to chemical–mechanical planarization (CMP) process control. The current implementation of the control system incorporates closed‐loop run‐to‐run (R2R) control and open‐loop real‐time monitoring, and can accommodate inter‐cell control. The R2R control element is enabled via a generic cell controller (GCC) implementation that provides flexible automated control of the process and equipment, multiple control algorithm branches and fuzzy logic decision capability among the branches, simulation capabilities, hardware and software independence, and extensive GUI support for control and data analysis. The R2R element utilizes a linear approximation multivariate control algorithm (branch) that supports individual exponential weighted moving average (EWMA) modeling of advices (outputs), weighting of inputs, granularity, and input bounding. The real‐time element of the control system utilizes a partial least squares (PLS) algorithm to identify real‐time equipment input trace patterns and relate these patterns to alarming conditions. The entire control system is designed to provide multivariate control of CMP process removal rate and uniformity. As a result of extensive design of experiments and testing, the R2R control level has been demonstrated to achieve good control of removal rate and fair control of uniformity. The addition of the real‐time element is expected to improve process control and reduce R2R process noise, thus leading to a more effective R2R control element.

Real-time process monitoring for changing inputs

A method is presented for statistical process control using real-time data in applications with finite processing times. This extends existing methods that monitor process behavior, assuming constant inputs and process conditions. Summary scores constructed from linear combinations of the realtime data are monitored both in real time and at the end of processing. The summary scores have a minimal loss of information due to the high dimensionality and correlated nature of real-time data. A multivariate regression model relates the inputs and initial conditions to the summary variables. This permits prediction of summary scores for different inputs and initial conditions. Large deviations from the predicted summary variables, measured by process monitor statistics, signal abnormal behavior. Diagnostic tools identify the patterns in the real-time variable(s) that cause the signals.

List of Acronyms

ACP Africa, Caribbean, Pacific countries AGC anti-globalization coalition ANZCER Australia–New Zealand Closer Economic Relations (trade agreement) APEC Asia Pacific Economic Cooperation ASEAN Association of South-East Asian Nations ATC Agreement on Textiles and Clothing (Uruguay Round) AU Africa Union BIAC Business and Industry Advisory Committee (OECD) CAFTA Central American Free Trade Agreement CAP Common Agricultural Policy (EU) CER Agreement on Closer Economic Relations between Australia and New Zealand CITES Convention on International Trade in Engendered Species (of Wild Flora and Fauna) CSR corporate social responsibility (NGOs) CU customs union DSU Dispute Settlement Understanding (WTO) EBA ‘Everything But Arms’ (EU agreement with ACP) ECJ European Court of Justice ECOWAS Economic Community of West African States EEC European Economic Community (1956–89) EFTA European Free Trade Association EMU European Monetary Union EU European Union FCCC Framework Convention on Climate Change FIELD Foundation for International Environmental Law and Development FTAA Free Trade Area of the Americas GATS General Agreement on Trade in Services (WTO) GATT General Agreement on Tariffs and Trade GMO (GM) genetically modified organism GSP Generalized System of Preferences (UNCTAD 1973) HIPC Highly Indebted Poor Countries IBRD International Bank for Reconstruction and Development