ISMI Predictive Preventive Maintenance Implementation Guideline

Transcription

1 Predictive Preventive Maintenance Implementation Guideline International SEMATECH Manufacturing Initiative

2 Advanced Materials Research Center, AMRC, International SEMATECH Manufacturing Initiative, and are servicemarks of SEMATECH, Inc. SEMATECH and the SEMATECH logo are registered servicemarks of SEMATECH, Inc. All other servicemarks and trademarks are the property of their respective owners International SEMATECH Manufacturing Initiative, Inc.

3 Predictive Preventive Maintenance Implementation Guideline International SEMATECH Manufacturing Initiative October 25, 2010 Abstract: This document from the MFGM032M project provides guidance for semiconductor equipment suppliers, device makers, and other implementers of predictive/preventive maintenance (PPM) interfaces. It introduces common language to describe the data requirements and functionalities of the equipment and factory systems and uses examples to justify extending existing preventive maintenance (PM) factory systems to create an optimized maintenance scheduler. Keywords: Authors: Toysha Walker (Micron) and David Stark () Approvals: David Stark, Author Sue Gnat, Technology Transfer Team Leader

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5 iii Table of Contents 1 EXECUTIVE SUMMARY BACKGROUND Preventive Maintenance (PM) Condition-based Maintenance (CBM) Predictive Maintenance (PdM) PPM INTERFACE CBM AND PDM DATA INPUTS AND ANALYSIS Equipment Hardware Data Equipment Performance Index Data Factory Metrology Data FDC and APC Data CBM Analysis PdM Analysis DECISION APPLICATION Maintenance Management Data Factory Information and Control System (FICS) Data Scheduled PMs Data Decision Rules FINGERPRINTING DATA SELECTING CBM AND PDM CASES Scheduled Maintenance, CBM, and PdM Case Selection Methodology SUMMARY REFERENCES...9 List of Figures Figure 1 PPM Interface for PdM...3 Figure 2 PPM Data Inputs and Analysis...5 Figure 3 PPM Data Flow and Interoperability with Other Factory Systems...6 Figure 4 Quadrant Analysis...8

6 iv Acronyms and Abbreviations ALID Alarm Event Identifier APC Advanced Process Control CBM Condition-based Maintenance CEID Collection Event Identifier EDA Equipment Data Acquisition EPI Equipment Performance Index FDC Fault Detection & Classification FICS Fab Information & Control System IDM Integrated Device Manufacturer MES Manufacturing Execution System MTTF Mean Time to Failure OCAP Out of Control Action Plan OEE Overall Equipment Efficiency (SEMI E79) PdM Predictive Maintenance PHM Prognostics & Health Management PM Preventive Maintenance RUL Remaining Useful Life; product s life expectancy SEMI Semiconductor Equipment and Materials International SECS-II Semiconductor Equipment Communications Standards (SEMI E5) SPC Statistical Process Control SVID Status Variable Identifier TBM Time-based Maintenance UBM Usage-based Maintenance

7 v Definitions Advanced Process Control (APC) Techniques covering both feed-forward and feedback control and automated fault detection, applied by both the equipment (in situ) and by the factory (ex situ). Degradation Rate Rate at which the operational quality of an equipment system, subsystem, and component decreases over time. Equipment Performance Index (EPI) Indicators composed of equipment hardware and/or factory data. They can be used to calculate system/sub-system/component health metrics. Fault Detection & Classification (FDC) A methodology of monitoring statistical variations in processing tool data and detecting anomalies. Forecast An estimation in unknown situations. Just-in-Time (JIT) Inventory A strategy to improve the return on investment by reducing inprocess inventory and its associated carrying costs. Maintenance Event Any activity (e.g., tests, measurements, replacements, adjustments and repairs) intended to retain or restore a functional unit in or to a specified state in which the unit can perform its required functions. Mean Time to Failure (MTTF) The average time between failures with the modeling assumption that the failed system is run until it fails without preventive maintainence. Out of Control Action Plan (OCAP) A controlled document detailing the procedure to disposition product/processes or equipment repairs or maintenance activities in response to an equipment fault indication. Prediction A statement or claim that a particular event will occur in the future. Prognostics & Health Management (PHM) Information The discipline that links studies of failure mechanisms to system lifecycle management. PHM uses information to allow early detection of impending or incipient faults, remaining useful life calculations, and logistical decision-making based on predictions. Raw Data Unprocessed data. Remaining Useful Life (RUL) A forecast of time or operating cycles remaining until a failure occurs. Run-To-Failure Methodology A method whereby an equipment system, subsystem, and component is fixed or replaced only after it fails. Sensor A component that responds to changes in the physical environment and provides an analog or digital output value. Subsystem An intelligent aggregate that behaves as a unit. A subsystem is made up of sensors and/or actuators and may contain mechanical assemblies. Multiple modules may share subsystems.

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9 1 EXECUTIVE SUMMARY This document provides guidance for semiconductor equipment suppliers, device makers, and other implementers of preventive and predictive maintenance (PPM). It introduces a common language to describe PPM data requirements and functionalities to enable seamless interactions between equipment and factory systems. Functionalities include gathering health indicators, assessing conditional states, and forecasting performance degradation to establish a condition-based and predictive maintenance program. The document explains how PPM information flows and interoperates with PM systems to create a maintenance event and suggests a methodology for selecting PPM cases. This document complements the 2008 Predictive and Preventive Maintenance (PPM) Equipment Implementation Guidelines [4] that details requirements for the equipment supplier. 1 2 BACKGROUND The objective of PPM is to optimize capital equipment return on investment. A PPM system will optimize scheduled maintenance, condition-based maintenance, and predictive maintenance. Scheduled maintenance is performed on a fixed, calendar-based schedule. Condition-based maintenance is accomplished by instantaneous monitoring of equipment and by performing maintenance when an equipment indicator reaches a predetermined threshold. Predictive maintenance is accomplished through acquiring relevant equipment and factory data and applying an equipment degradation model to predict the equipment s remaining useful life (RUL). A PPM system will combine scheduled maintenance, condition-based maintenance, and predictive maintenance to enable effective cost vs. performance decisions. 2.1 Preventive Maintenance (PM) In the early history of the semiconductor industry, chip manufacturers practiced run-to-failure maintenance, where equipment maintenance was performed once equipment had failed. Relying on run to failure maintenance was costly and infeasible in a mass manufacturing setting. With accumulated field experience, equipment manufacturers and users developed preventive maintenance programs. Preventive maintenance is the servicing of equipment and facilities before incipient failures occur or before they develop into major defects. The first implementations of preventive maintenance were scheduled maintenance programs. A scheduled preventive maintenance program is a fixed, calendar-based schedule of maintenance activities, where the schedule is derived from a priori knowledge of the historical, statistical frequency of specific failures. Scheduled Preventive Maintenance Example: Over 10 years ago, robot bearings belonging to a leading OEM supplier were being run to failure. These failures resulted in an increase in particle contamination due to bearing breakdown, minor wafer scratching, eventual wafer breakage, and extended downtime. As defect inspection methods and tool qualification procedures improved, particles and minor scratches were detected on product wafers before broken wafer events. Accumulated experience with this failure allowed the determination of a statistically valid robot bearing lifetime estimate. This estimate allowed the establishment of a scheduled preventive maintenance event to replace the robot bearing.

10 2 2.2 Condition-based Maintenance (CBM) While PM was superior to run-to-failure methodologies, condition-based maintenance (CBM) presented new opportunities to contain costs and increase overall equipment effectiveness (OEE). CBM involves direct monitoring of equipment, onboard sensors, processes, and external systems data to determine current equipment operating condition. Advanced analysis techniques such as real-time fault detection and classification (FDC) are applied to the data to identify whether performance indicators have deteriorated to a predetermined threshold or control limit. The CBM applications warn IDM factory systems of anomalies to trigger a maintenance event when they first occur. Condition Based Maintenance Example: The next evolution of the robot bearing PM incorporated robot speed, motor current, and blade position measurements into an adaptive control model. In conjunction with factory metrology measurements (particles and in-line inspections), a CBM indicator can be developed to show when the bearings are beginning to display degradation. 2.3 Predictive Maintenance (PdM) Predictive maintenance is accomplished through acquiring relevant equipment and factory data and applying an equipment degradation model to predict the equipment s RUL. CBM, where applied, has helped the OEM and IDM engineering organizations gain insight into equipment degradation mechanisms. The next logical step is to develop a predictive model of maintenance events where the degradation mechanisms are known, relevant data is available, and the business case is favorable (e.g., high value and low frequency maintenance events). Predictive maintenance acquires the relevant data, applies a mathematical model, and predicts the RUL until a specific maintenance event must be scheduled to avoid equipment failure. Predictive Maintenance Example: The last evolution in the robot bearing example is Predictive Maintenance. To predict the RUL of the robot bearing, the PdM application continuously acquires the relevant data from the equipment and factory and inputs this data into a predictive model. The predictive model provides continuous updates to the estimated RUL of the robot bearing. The predicted RUL allows the factory to optimally schedule for the preventive maintenance to repair the robot, taking into account work schedules, yield, parts inventory, and more. 3 PPM INTERFACE The PPM interface is defined as the interface designed to communicate prognostics and health management (PHM) information between the equipment and IDM factory systems. 1 The equipment provides data that can be used to derive equipment health. Collected health information is usually derived sensor data, not raw sensor values; for PPM, however, the Working Group asks the equipment to provide raw data wherever possible. The equipment communicates feature values with the following attributes: Ability to detect impending or incipient fault conditions Ability to detect a fault early enough to support prognosis 1 According to the PPM Working Group.

11 Ability to distinguish one fault type from another Ability to evaluate fault progression A low false-positive rate The PdM model may require equipment data only or a combination of equipment data and factory data to predict an RUL. Where factory data is required, the factory system adds metrology data to facilitate factory and equipment data analysis to make RUL predictions. As depicted in Figure 1, the IDM factory system accepts this PHM information from the equipment to make asset management decisions, which involve tool control, PM and work-in-progress (WIP) scheduling, and just-in-time (JIT) inventory parts management. Decisions including any tool interdictions (stop, idle, pause events) or maintenance schedules are the responsibility of the factory only, not the equipment. The PHM information should be communicated from the equipment to factory system by standard communication protocol SECS-II and/or equipment data acquisition (EDA) ports. Figure 1 shows the information flow through these protocols as dotted lines. SECS-II, a well established standard mechanism, not only allows the required PHM information to flow from equipment to factory, but also allows the factory system to set control limits and enable/disable PHM mechanisms. 2 The 2008 PPM Equipment Implementation Guidelines require that equipment suppliers extend SVIDs, CEIDs, and ALIDs to allow this capability. EDA allows PHM information and raw data to flow at a faster throughput. Additionally, EDA s multi-client functionality allows factory systems to create data collection plans based on specific needs. Both protocols are beneficial to PPM implementations. 3 Asset Management Decision Results of Equipment Predictive Analysis Factory Factory & Equipment Data Analysis Factory Metrology Data Equipment Results of Equipment Predictive Analysis PHM Information via SECS/GEM or EDA Equipment Data Analysis Hardware Data Input Figure 1 PPM Interface for PdM 2 Factory communication to equipment is not shown in Figure 1.

12 4 4 CBM AND PDM DATA INPUTS AND ANALYSIS CBM and PdM require equipment hardware data and factory metrology data to provide the parameters to discover and correct root problems before misprocessing or unscheduled downtime can occur. By monitoring equipment usage and operating conditions, OEMs and IDMs can avoid unnecessary maintenance on under-utilized equipment while properly maintaining equipment used under harsher conditions. 4.1 Equipment Hardware Data Equipment hardware data inputs consist of raw data, sensor data, and/or parts condition data. Equipment raw data is command or state data, either as classification, numerical value, or trace data from the equipments functional components or command and control software. Sensor data are measurements of the physical result of equipment component performance or environmental conditions. Parts condition data is data derived from raw data and sensor data to indicate part condition. Since the equipment supplier should have superior knowledge of the design and function of the equipment, requests that the supplier derive and make accessible all the required raw data, sensor data, and condition data to make PPM successful. 4.2 Equipment Performance Index Data The Equipment Performance Index (EPI) 3 is an indicator of the current health of a device. EPIs are data derived from raw data, sensor data, condition data, and factory data to indicate the instantaneous condition or health of a device. EPIs may be useful as PHM information to allow equipment health monitoring, early detection of impending or incipient faults, or remaining useful life calculations. EPIs may also be constructed by the equipment supplier as a means to provide customers required value-added data while maintaining intellectual property protection. EPIs can be constructed at any level in the equipment hierarchy: tool, module, subsystem, and component. EPIs may be consumed by CBM, FDC, PdM, and other equipment health monitoring applications. The PPM working group desires that equipment suppliers constructing EPIs explain the construction and use of each EPI the supplier provides. As shown in Figure 2, the equipment and factory data may be combined to provide EPIs. EPIs are derived from all available data sources, including raw data, sensor data, feature values, and metrology data. 4.3 Factory Metrology Data Because PPM considers both the reliability of the device and the quality of the product being manufactured, factory metrology data is pertinent to PPM. MES process data, defects, parametric data, and yield parameters must also figure into the PHM information. 4.4 FDC and APC Data CBM and PdM applications will consume FDC and APC data as value-added data from the factory, similar to the use of EPIs from the equipment. FDC and APC outputs have their own intended purpose, which overlaps with the purpose of CBM and PdM. The use of data constructed within the FDC and APC applications by CBM and PdM is a natural extension. This connection is shown in Figure 2. 3 EPIs are still under development. Upcoming pilots will produce best known methods for deriving EPIs. Therefore, this document does not cover how to construct an EPI.

13 5 Data Collection Data Analysis Event Trigger Equipment Hardware Data Parts Condition Data Equipment Raw Data PdM Input: Equipment and factory data Output: RUL for maintenance event CBM Input: equipment and factory data, EPI, FDC, SPC Output: CBM health status, alert maintenance needed due to indicator at threshold Decision Application Inputs: scheduled maintenance calendar, CBM alerts, PdM RULs, parts inventory, staffing, WIP schedules, factory schedule, decision rules Output: recommended maintenance schedule FDC System Factory/Metrology Data MES Process Data Defects Parametric Yield Parameter Statistical Process Control (SPC) Optional APC Systems Figure 2 PPM Data Inputs and Analysis 4.5 CBM Analysis The CBM application monitors the equipment health and indicates that a maintenance event is required when an instantaneous equipment health metric surpasses a predefined threshold. The health metric may be an SPC output, an FDC output, an EPI, raw or combined equipment and/or factory data. CBM may include usage-based counters, as in wafer count or process time, between signaling that a particular maintenance is due. What discerns CBM from PdM is that CBM monitors current health and invokes a maintenance action when a threshold is reached; PdM forecasts, or predicts, future healthy performance in the form of an RUL. CBM data analysis is less complicated than PdM analysis and thus is likely to be applied more broadly. 4.6 PdM Analysis The PdM application monitors the equipment health and indicates when in the future a maintenance event will be required. PdM uses advanced mathematical models applied to each failure that requires a specific maintenance event. The prediction is in the form of an RUL estimate. The RUL is continuously updated with each new batch of input data modeled and can be as often as with every wafer processed or less frequent where the input data to make the prediction includes factory metrology data. RUL estimates must be output in useful terms. Where the failure is independent of equipment use, the RUL will be denominated in calendar hours. Where the failure is dependent on equipment use, the RUL will be in denominated in process time or wafer count. Where there is recipe dependency, RUL estimates should be given for each recipe. PdM is more complicated than CBM; therefore, PdM must be done judiciously.

14 6 5 DECISION APPLICATION The decision application uses a rules engine to determine the optimal maintenance schedule. The inputs to the decision application are the existing schedule for scheduled PMs, CBM output, PdM output, parts inventory, maintenance event information (duration, parts required, staff required), staffing, WIP schedules, factory schedule, and other economic value drivers the IDM may elect to include (e.g., value by product, lot priorities, etc.). The variety of data input to the decision application will require a careful factory integration effort. The interconnectivity is expected to be different depending on the particular factory s system architecture. The IDM will set the decision rule logic to maximize profits. The decision application output is a recommended maintenance schedule. The IDM may elect to allow the decision application to set maintenance schedules or may elect to retain manual control. It is suggested that the recommended maintenance schedule highlights any changes with the source of the change noted. Thus, if the CBM application output is that a specific maintenance event existing on the schedule in the future is required immediately, and the decision application schedules that maintenance immediately, then the recommended maintenance schedule should highlight that the change was invoked by the CBM output. Data Collection Data Analysis Event Trigger & Scheduling Event Execution Equipment Hardware Data Parts Condition Data Equipment Raw Data Factory Metrology Data MES Process Data Defects Parametric Yield Parameter PdM Input: Equipment & factory data Output: RUL for maintenance event CBM Input: equipment & factory data, EPI, FDC, SPC Output: CBM health status, alert maintenance needed due to indicator at threshold FDC System SPC Decision Application Inputs: scheduled maintenance calendar, CBM alerts, PdM RULs, parts inventory, staffing, WIP schedules, factory schedule, decision rules Output: recommended maintenance schedule Maintenance Management Duration Scheduled PMs Parts Availability Necessary Resources Maintenance Cost TBM-Data (due date) Equipment QUAL Data UBM-Data (Wafer count) PPM Maintenance Event FAB Information & Control System Qualification Data Equipment Availability Production Constraints Qualification Data Equipment Availability Production Constraints Figure 3 PPM Data Flow and Interoperability with Other Factory Systems 5.1 Maintenance Management Data Maintenance management data, such as PM duration, regular PM schedule, parts availability, and required resources are important in creating an optimized maintenance event schedule. 5.2 Factory Information and Control System (FICS) Data FICS data, including equipment availability and production constraints, is required to create an optimal maintenance event schedule.

15 5.3 Scheduled PMs Data The scheduled PM calendar is required by the decision application to create an optimal maintenance event schedule. The decision application will determine whether to recommend rescheduling a maintenance event being invoked by CBM or PdM that is already an event on the scheduled maintenance calendar. 5.4 Decision Rules The decision application is a rules engine. The IDM sets the rule limits to maximize profits. A generic basic set of common rules make up the majority of those needed in the decision application for all IDMs. It is expected, however, that individual IDMs will customize the decision application by setting the limits on the rules and by adding additional rules. 7 6 FINGERPRINTING DATA Equipment fingerprinting is a method of taking a snapshot of the configuration of the equipment including all hardware/software settings, parameters, states, setpoints, calibrations, variables, etc. While fingerprinting is outside the scope of PPM, it is an application with significant overlap with PPM. Fingerprinting can be used to capture the health status of equipment in a golden state to be compared to any other time. This comparison can be useful in chamber matching, pre-to-post maintenance tool status comparison, and general historical tool performance monitoring. An effective fingerprinting function that captures status and dynamic tool performance may be used instead of costly tool qualification runs. 7 SELECTING CBM AND PDM CASES Intelligently implementing an integrated PHM system in a factory requires care. Not every piece of equipment in the factory requires PdM and CBM. Because developing CBM and/or PdM for tools whose performance will benefit requires significant resources, it should be undertaken judiciously. High cost, high downtime tools are candidates. Once the candidate tools are selected, a method can be followed to determine what maintenance should be calendar time-based scheduled maintenance, CBM, or PdM. Each maintenance event represents a case. 7.1 Scheduled Maintenance, CBM, and PdM Case Selection Methodology Case selection should follow a prescribed methodology, based on business value and technical criteria. 1. Select subject tool. To maximize the benefit, high cost and high downtime tools are prime candidates. The IDM should prioritize the toolset by these factors. 2. For the subject tool, analyze maintenance event frequency and cost from the tool history in use in the factory. One to two years of data from multiple copies of the subject tool is recommended. Estimate the cost for each downtime event. The cost may include parts, labor, scrap, rework, depreciation (monetized downtime), and opportunity costs (lost wafer processing due to wafer inventory build-up at subject tool). 3. Organize the maintenance history by maintenance event; scheduled maintenance and unscheduled maintenance. For each unscheduled maintenance event, determine the root cause failure. For each scheduled maintenance event, determine the root cause failure that the maintenance is intended to prevent. Group maintenance events with a common root cause. 4. Determine the technical probability of success. Is the degradation/failure understood? Can the degradation be sensed using indicator data from the equipment? Is this data accessible and transformable for use in the prediction algorithm or CBM? Is there data in the factory that can be used to sense the degradation? It this data accessible and transformable for use in the prediction algorithm or CBM? The answers to these questions will give an indication of the

16 8 probability of success for developing PdM or CBM for the maintenance event. algorithm or CBM? The answers to these questions will give an indication of the probability of success for developing PdM or CBM for the maintenance event. 5. Create a quadrant chart for all maintenance events for the subject tool. The quadrant chart displays the frequency of the event on the Y axis (occurrences per year) and the cost, or value, of the event on the X axis. Figure 4 shows an example quadrant chart with simulated data. The red quadrant-determining lines placement is not specified, but is up to the IDM to estimate and move them as a sensitivity analysis to determine their proper placement. 6. Simply stocking parts and making repairs as needed using a run to failure operation is recommended for maintenance events that have high frequency and low value (top left quadrant). 7. Calendar-based scheduled preventive maintenance is the recommended practice for maintenance events that are infrequent and low value (bottom left quadrant). 8. Tool redesign is required for events of high frequency and high value, as this mode of operation cannot be tolerated or simply responded to in a CBM or PdM practice (this is the top right quadrant). 9. CBM and PdM should be developed for events in the bottom right quadrant plus or minus some intrusion into the neighboring quadrants where the technical feasibility is high. These events have low frequency and high value. The CBM events are in triangles, and the PdM events are circled in the figure. Some events may be candidates for CBM or PdM; the determinant of which to pursue will be the technical assessment in FREQUENCY (X/YR) Stock Parts Run to Fail Sched PM CBM Redesign PdM VALUE Figure 4 Quadrant Analysis

17 8 SUMMARY PHM calls for a paradigm shift from scheduled preventive maintenance plus unscheduled maintenance towards continuous data-driven, condition-based monitoring using CBM and PdM. Proper application of CBM and PdM will increase the profitability of the IDM by reducing downtime and reducing maintenance costs. Modeled data complemented by decision support tools will allow for optimized maintenance programs that promote efficient asset management decisions. Thus, health assessment, degradation forecasting, and performance diagnosis functionalities added to existing PM systems will raise maintenance activities to next generation standards. 9 9 REFERENCES [1] Getting the most from Predictive Maintenance, Engineer s Digest, February [2] Consensus Preventive and Predictive Maintenance Vision Guideline: Version 1.1, Technology Transfer # C-ENG (ismi.sematech.org/docubase/abstracts/4819ceng.htm). [3] PPM Initiative: Research on the Current Status of Predictive Maintenance (PdM) Algorithms and Applications, Project Report # [4] Predictive and Preventive Maintenance Equipment Implementation Guidelines, Technology Transfer # A-ENG (ismi.sematech.org/docubase/abstracts/4934aeng.htm). [5] Liao, H., Zhao, W., & Guo, H., Predicting remaining useful life of an individual unit using proportional hards model and logistic recession model, Reliability and Maintainability Symposium, Jan. 2006, pp [6] Mosher, P., Enhancing Your Predictive Maintenance Program with Condition Monitoring, Flowserve Corp., October 11, 2006, (May 2009)

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