Post on 27-Mar-2015
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2003 ITRS Factory IntegrationFactory Information & Control Systems (FICS)
Backup Foils
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Factory Information and Control System (FICS) Backup Outline
1. Contributors
2. How Metrics were Selected
3. Production Equipment Performance and Factory Operations
4. Process Control Systems
5. Engineering Chain
6. AMHS Direct Transport
7. Suggested University and Industry Research for 2004+
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Contributors to this Section
Ray Bunkofske (IBM) Jonathon Chang (TSMC) Gino Crispieri (ISMT) Jean-Francois Delbes (STM) Barbara Goldstein (NIST) Ton Govaarts (Philips) Arieh Greenberg (Infineon) Franklin Kalk (DuPoint Mask) Giant Kao (TSMC) Ya-Shian Li (NIST)
Leon McGinnis (Georgia Tech) Shantha Mohan (Kaveri, Inc.) Eckhard Muller (M&W Zander) Richard Oechsner (Fraunhofer) Mark Pendleton (Asyst), Adrian Pyke (Middlesex) Lisa Pivin (Intel) Court Skinner (Consultant) KR Vadivazhagu (Infineon) Bob Wiggins (IBM)
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How Metrics were selected Almost every metric is a best in class or close to best in class
Sources are: Rob Leachman’s published 200mm benchmarking data, Individual IC maker feedback, and I300I Factory Guidelines for 300mm tool productivity
It is likely a factory will not achieve all the metrics outlined in the roadmap concurrently
Individual business models will dictate which metric is more important than others It is likely certain metrics may be sacrificed (periodically) for attaining other metrics
(Example: OEE/Utilization versus Cycle time)
The Factory Integration metrics are not as tightly tied to technology nodes as in other chapters such as Lithography
However, nodes offer convenient interception points to bring in new capability, tools, software and other operational potential solutions
Inclusion of each metric is dependent on consensus agreement
We think the metrics provide a good summary of stretch goals for most companies in today’s challenging environment.
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Production Equipment Performance & Factory Operations
6
b
Integrated FICS to Improve Equipment Performance
ProcessEquipment
UI
OHV
Stocker
UI
OHV
AMHS ControlSystem
Scheduling &Dispatching System
Equipment Controllers
Information Bus
Processing nearly complete SECS/GEM
1a. Load port event signals carrier leaving OR
1b. Equipment event indicates that processing is nearly finished
2. PM schedule checked to verify no PM is due
3. Dispatcher selects highest priority lot for processing
4. AMHS routes carrier to process equipment
5. Next lot delivered to equipment before it starves
aProcess Chamber
GOAL: No Equipment Idle Time (“starvation”) if Material is availableImproves output (w/ priority on “super hot lot”) through more
effective equipment utilizationRequires integrated equipment, scheduling/dispatching, AMHS,
factory operations, and PM
Equipment Tracking System
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Predictive PM to Improve Equipment Performance
ProcessEquipment
UI
OHV
Paging SystemScheduling &
Dispatching SystemEquipment Controllers
Information Bus
Equipment data
Process Chamber
1. Equipment data indicates need for future Preventative Maintenance (PM)
2. Scheduler determines when to PM the equipment3. PM is automatically scheduled in Equipment Tracking
system4. Prior to PM time, Scheduler validates need (based on lot
priority, tool impact, downstream impact)5. Technicians notified via page that specific PM is
required6. Equipment finishes processing and is taken offline for
PM
GOAL: Predict future PM time to have technician/consumables ready. Intelligently determine when to run PM based on lot priority & tool/downstream impact.
Improve equip perf by optimizing Preventative Maintenance (PM) timing and avoiding unscheduled or last minute scheduled down time
Requires integrated equipment, scheduling/dispatching, AMHS, and factory operations
Equipment Tracking System
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Process Control Systems
99/11/2001
Continued Opportunities for APC to Improve Factory Productivity
Goal Motivation SPC FDC Run to
RunIM
Optimize performance to Process Spec
Wafer cost
Die Performance
Prevent wafer/die loss & equipment damage
Wafer cost
Factory Output
Reduce Wafer Rework Wafer cost
Factory Output
Faster Factory TPT (Throughput Time)
New and normal product delivery
Better Equipment Reliability Capital Cost
1) Solutions can be applied in parallel2) Objective is a Quantified Improvement to the Key Factory Goals
1009/06/03
SECS/GEM Control Line
Equipment Data Acquisition (EDA) Standards Line
Today100 variables @ 3 Hz each= 300 values per sec
Future EDA Goal500 variables @10 Hz each= 10,000 values per sec
Future Equipment & Automation CapabilitiesDevelopment in 2001 [with standards]. Qualification/Production by 2005
Automation System Capabilities1. Data Sharable between APC applications2. High data transfer rates3. Single point configurations4. Integrated yield, process control, and
operational systems5. Rapid application development (run to
run algorithms, etc.)
Equipment &Process Data
SPCRunTo
Run
FDC
Yield PCS
Integrated APC/Yield Data & Systems
OperationsData
WIP
Dispatch
Tool Control
MCS
Manufacturing Execution System (MES)
Equipment Capabilities1. Standardized data and connectivity2. Fast sensor sampling & data transfer rates3. Host ability to stop processing as needed4. Graceful recovery when a fault occurs5. Ability to change parameters and values
between wafers6. Wafer tracking all points within the tool
119/11/2001
APC and Process Control Capabilities are Key Enablers for Agile Manufacturing
ModuleFlow
A
ProcessSteps
C
D
B
Target values
(Recipe and major parameters)
Physical Structure base control
Process Engineering
Eq. A
Eq. Process control info.
Eq.B
Interpretation into what equipment can execute
F/FAPC
Device structureOptimization Control
Information
Current center of interest
Detailed Eq. Status
info.
Machine-to-Machine Difference and
AdjustmentNPW Management and
Control
Chamber wet cleaning and Specification
Time dependent performance change and
compensation
Eq. Maint. and Rules
Eq. Process performance adj info.
Copyright 2000 by Masato Fujita, Selete/Panasonic
Manufacturing Experience
Resource ConsumptionManagement
More focus for agile manufacturing
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Fault Detection and Classification Prevent scrap or equipment damage
Category Purpose Capability Definition Equipment Requirements
System
Requirements
Roadmap Requirements
Fault Detection and Classifi-cation (FDC)
Prevent harm to product and/or equipment due to equipment operation while out of specifi-cation
Monitor equipment processing data to determine if the equipment is in spec
Shut down or pause equipment if out of spec
Accept changes from the Run to Run system to avoid inadvertent pauses to production
Real-time process sensors on process tools
Reporting of real-time data to host system
Along with buffers and filters to reduce data traffic
Report lot, slot, waferid, recipe step and chamber level recipe name as SVID’s.
Ability to stop processing at various intervals via host command
Immediately After this step After this lot After this wafer
Handle large network volumes 10-15 MB / sec, no single fail pointsRedundant hardware, auto fail-over for both hardware and app’sScaleable apps and hardware, no redesign as system growsSupport ease of introduction of new applicationsModular applications with interfaces to allow data exchangeSupport download of FDC models to equipAbility to use standard commands to stop processing at various intervals
% wafers processed while equipment is out of spec
Potential Solution:Guidelines and Standards Target
ITRS Requirement:Equip Table Target
• ITRS Requirements include:• Defect Reduction: Particle density (particles / m2) tied back to yield• Overall Equipment Efficiency – reduces MTTD (diagnose)•Add process repeatability
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Run to Run ControlOptimize performance to equipment processing specification
Category Purpose Capability Definition
Equipment Requirements System
Requirements
Roadmap Requirements
Run to Run Control
Realize the process specification
Independent of input conditions (wafer or previous process results)
Independent of some equipment conditions
Adjust process equipment process based on actual metrology results
Reporting of metrology data to host system
Supply data to determine relationship of end processing results to adjustable process parameters.
Historical detailed data required from equipment sensors
Ability to adjust key recipe parameters at various intervals via host command
Immediately After this step/wafer After this lot
Need to be able to correlate data to material (chamber level process recipe, lot, slot, wafer id) all the time from every tool – can’ t do this today
Redundant hardware, auto fail-over for both hardware and app’sScaleable apps and hardware, no redesign as system growsSupport publish / subscribe architecture to ease introduction of new applicationsStandard app interfaces
Coefficient of variation of key process parametersCv = sigma/mean
ITRS Requirement:Equip Table Target
Potential Solution:Guidelines and Standards Target
• Primary ITRS Requirements is Coefficient of Variation for (ITRS examples):• Litho – gate CD control (nm), Overlay Control (nm)• Diffusion – Oxide thickness and thickness control
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Run to Run ControlOptimize performance to equipment processing specification
Category Purpose Capability Definition Equipment Requirements FICS Req’ts
Run to Run Control
Realize the process specification
Independent of input conditions (wafer or previous process results)
Independent of some equipment conditions
Adjust process equipment process based on actual metrology results
Communicate changes to the FDC system to avoid inadvertent pauses to production
Reporting of metrology data to host system
Supply data to determine relationship of end processing results to adjustable process parameters.
Historical detailed data required from equipment sensors
Ability to adjust key recipe parameters at various intervals via host command
Immediately After this step/wafer After this lot
ITRS Requirement:Equip Table Target
Potential Solution:Guidelines and Standards Target
•Research Required:• modeling – e.g. multivariate control – relation of variables by process/tool (what key parameters affect output?)
• Receive data•Calculate optimal parameter
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Integrated MetrologyReduce module level Throughput Time (TPT)
Category Purpose Capability Definition Equipment Requirements Roadmap Requirements
Integrated Metrology
Decrease module level TPT
Integrate metrology into the process equipment
Includes hardware and software
Hardware integration of process and metrology equipment
Don’t increase footprint Interoperability
Software integration of metrology and process equipment
Single SECS/GEM interface for integrated metrology and process equipment
“Smart Integration
Need to match IMM with each other & stand-alone equipment (repeatability)
Reliability/quality req’ts (support recalibration)
Reduction of: Throughput time Time for metrology
feedback loop Wafer handling and
AMHS time Floor space
ITRS Requirement:Equip Table Target
Potential Solution:Guidelines and Standards Target
• Primary• Factory Cycle time [days] per mask layer (hot lot and non-hot lot)• AMHS system throughput (moves / hour)
• Secondary• Floor space effectiveness (activities / hour / m2 or WIP turns / m2)
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Fault Detection and Classification (1/2)Level 0 FDC Assumptions:• FDC occurs outside the tool• Data collection through SECS interface for integrated sensors
• Use Trace data collection (S6F1) or poll (S1F3/4)
• Data collection frequency 1-3 Hz through the SECS interface
• IC Makers integrate sensors and use proprietary interfaces where needed.
• Tools need graceful shutdown options at various intervals (some exist, implementations vary)
• Equipment parameter control & fault detection – ensure there are triggers to support immediate reaction
Outside of Tool•FDC Modeling•FDC control configuration•External sensor and tool data integration by IC Maker
ProcessEquipment
Step N
UI
FDCModule
FDCModule
Host System
FDC
Dat
a
FDC
Control
SECS Interface used for most data collection and all control
External Sensor Integration Optional
• Graceful Shutdown options required
• Detailed wafer, recipe and chamber data required
IC Maker integrated External Sensor
It is vital that the tools be able to report the chamber level process recipe, recipe step, lot number, slot number and wafer ID at the very beginning of wafer processing
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Fault Detection and Classification (2/2)
ProcessEquipment
Step N
UI
Slow FDCModuleHost System
FDC
Sig
nal FD
C C
ontrol
Outside of Tool•Host determines actions based on type of fault
•Host issues control command
SECS Interface used to control tool in the event of a fault
Inside the Tool• FDC Models configured• FDC host signals configured
• FDC actions may also be configured
EES
EE
Inte
rfac
e• Historical and Summary data storage and analysis
• Detailed wafer and chamber data tracked
Level 1 FDC Assumptions:• Some FDC may occur inside the tool (IC maker’s discretion)
• Enables real-time control loops• IC Maker configures in-tool FDC control model and actions to be taken based on process via standard interface (if it exists)
• Tool determines when model is violated, controls tool, and notifies host (in tool FDC case)
• Historical and Summary Data collection through standard EE Interface (with high level linkage data)
• Tools needs graceful shutdown options at various intervals
• Immediately, after this step, after this lot
• May also have off tool FDC and health monitoring in parallel to on tool FDC
• OPEN: How does this interact with wafer to wafer control (FDC model may need to change with each wafer)
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Run to Run Control (1/3)
MetrologyEquipment
UI ProcessEquipment
UI
MetrologyEquipment
Step N-1 Step N+1
Step N
Feedback Control - Use post metrology feedback data to adjust processing for the next lot
Feed Forward Control - Use preprocess metrology data to adjust processing for that lot
UI
Ru
n t
o R
un
Co
ntr
ol
Host System
EquipController
EquipController Equip
Controller
Metro data collected via SECS interface
SE
CS
SE
CS
SE
CS
Models and Recipe Adjustment
• Parameterized recipes required
• Detailed wafer and chamber level data required
Metro data collected via SECS interface
Detailed wafer and chamber data required
Level 0 L2L Run to Run Assumptions:• IC Maker configures control model based on process• Recipe adjustment calculations made using metrology data and other
data from the equipment or process.• Recipe adjustment occurs outside the tool (recipe adjusted by the host
and downloaded to the equipment)•Parameterized recipes supported on some equipment
• Metrology data collected through the SECS interface
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Run to Run Control (2/3) (Lot to Lot Case)
MetrologyEquipment
UI
ProcessEquipment
UI
MetrologyEquipment
Step N-1Step N+1Step N
UI
Feedback Control - Use post metrology feedback data to adjust processing for the next lot
Feed Forward Control - Use preprocess metrology data to adjust processing for that lot
Ru
n t
o R
un
Co
ntr
ol
SE
CS
SE
CS
SE
CS
Recipe ParameterControl
Recipe Adjustment (Parameterized recipes required)
Metro data collected via EE interface
Host SystemEquip
ControllerEquip
ControllerEquip
Controller
Factory Network
EE
EE
EE
EEDatabase
Dat
abas
eA
dap
tor
EES
Recipe Adjustment Models and Calculations
Recipe Recommendations
Metro data collected via EE interface
Detailed wafer and chamber data required
Modular apps with open interfaces
Proposed Guidelines
Level 1 L2L Run to Run Assumptions (non integrated metrology case)•IC Maker configures control model based on process•Recipe parameter value calculations made using metrology data and other data from the equipment or process (occurs in the EEC).•Recipe parameter values are applied to base recipes inside the tool
• Parameterized recipes utilized (supported on all equipment via SEMI standard)• Recipe parameters are recommended to the Host by the EEC• Recipe parameters downloaded to the equipment via the Host• Still need recipe download capability for base recipes
•Metrology data collected through the EE interface•Executed values reported from equipment to EEC (with high level linkage data)
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Run to Run Control (3/3) (Wafer to Wafer Case)
IntegratedProcess andMetro Equip.
UI
SE
CS
Parameterized recipes required
Host System
EquipController
Factory Network
EE
EEDatabase
EES
Recipe Adjustment Models, Calculations, Control
Integrated Metro data and detailed wafer and chamber data collected via EE interface
Modular apps with open interfaces
Integrated MetrologyModule (not Bolt on)
Integrated SECS and EE Interfaces for
Process and Metrology
EE Network
Recipe and Model Selection and Download via
SECS Interface
Level 2 W2W Run to Run Assumptions (IM only case)•Lot to Lot capabilities are same as level 1•IC Maker configures control model based on process and downloads like a recipe via some download standard.•Recipe parameter value calculations made using metrology data and other data from the equipment or process (occurs in the tool).•Recipe parameter values are applied to base recipes inside the tool
• Parameterized recipes utilized (supported on all equipment via SEMI standard)• Still need recipe download capability for base recipes
•Metrology data collected through the EE interface•Any modification to the process parameters reported from equipment to EEC (with high level linkage data)
•OPEN: Should internal communication between process part and metrology part be standardized?
•IM means that the Metrology part is integrated with the process part of the tool• Both Hardware and Software
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Integrated Metrology (1/1)Guideline:
– Hardware integrated process and metrology tools shall also integrate their data collection and control systems.
Process Tool
MetrologyTool
MetrologyTool
Off ToolControl System
Standalone IntegratedIn-Line
DualSECS/GEM
Lines
SingleSECS/GEMLine
Individual SECS/GEM
Lines
Off Tool Control System Off Tool System Control
Process Tool
MetrologyTool
Process Tool
Control Network
SingleEECLine
DualEECLines
Individual EECLines
EEC Network
Off ToolEEC System
Off ToolEEC System
Off ToolEEC System
OK OKNG
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Perform Experiment / Acquire “X” input data
TimeNot to scale
Integration Time for Equipment Control Systems (Run to Run algorithms) Must Decrease
Assumptions:• Run to Run algorithms are developed (not purchased)• Production tool time available for performing
experiments• Run to Run Framework exists. Just need to add new
algorithm• Able to reuse of business logic from other run to run
algorithms• Wafer-level data available• Tool parameters can be modified• Process is stable
Design of Experiment
Acquire “Y” output data
Analyze Results and create Process Model
Build run to run algorithm into the system
Functional Testof run to run algorithm
Release to Factory Floor
Solutions to decrease:•If fundamental process models exist, then use historical data to decrease time to create new algorithm•Wafer Level Tracking; Slot tracking, & Storage/Retrieval of all data with Wafer ID reference•Integration of data analysis & Run to Run (APC) Framework•If data is available, then start with Analyzing Results
•Decrease to 4 weeks•Must have enough variability in data
•Solutions unknown to decrease below 4 weeks
Total Time (expected to decrease)
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Engineering Chain
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New Products Need Faster Customer Delivery Challenge: Customers want new products delivered much faster Key Concept: The Engineering Chain integrates rapid, accurate,
flexible data exchange from design to new chip delivery to the customer to ensure customer cycle times are met Engineering Chain = Design Reticle Integration Customer High Volume Different from supply chain management which focuses on volume production
ProductDesign
Mask Fabrication
Process Development Packaging
and TestCustomerEvaluation
VolumeRun
Design Fix
Design Improvement
Data Transfer
This is a Supply Chain Task
Data Transfer
Data Transfer
Data Transfer
Wafer Fab
Planning and parallel activities to deliver
Not Engineering or Supply Chain
Part of Supply Chain
Part of Supply ChainLegend
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Engineering Chain vs. Supply Chain
Engineering Chain Focus is on rapid new product, new
process, and new procedures Success indicators include:
Design successes Time and cost to introduce new and
changed parts Performance repeatability in high volume
manufacturing Customer serviceability Quality of reticle, wafer, and final chip Maximize and manage IP use
Information flow to support Idea → Design → Mask → Fab → Test
Requires engineering data exchange “APC for the entire chain” A collaborative workflow
Supply Chain Focus is on efficient high volume
production Success indicators include:
Low wafer and parts cost Time and cost to make all parts for mass
production Reduction in cost of inventory
Flow of raw materials to finished goods
Requires exchange of schedule and inventory data.
Workflow is well understood; Low volume of data exchanged
Both
Phases and elements include Source, Plan, Make, and Deliver
Efficiency, speed & Cost are essential
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Critical Cycle Time and Cost Issues• Data translations• Data volume• Precise knowledge of design intent• Precise awareness of mask/process
capabilities
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Potential Solutions to Accelerate New Products1. Faster data exchange using standard data models and structures
between major operations2. Improved methods and capabilities to match the process to the product
on time3. Improve execution and process control systems, analogous to the chip
fab, in Mask Shops to deliver masks with 0% excursions ( requires improved systems, richer equipment data, etc.)
Supply Chain (O2D)
Sales SCP MES
Factory ShippingWO
WIP
Order
Promise
Design
Commerce Data
Engineering Data
Engineering Chain (T2M)
e-Diag
Maintenance
Support
EE Data
EES
APC
Recipe
Eqpt. Configurati
on
Mass Production
Product Development
Process Devmn’t YMSMask
Devmn’t
Eqpt.Devmn’t
Eqpt. Supplier
28Source: JEITASource: JEITA
Pattern data are excluded from V1.0
Logic, circuit design
EB conversion
EB
exposure
Mask shipment
Mask order sheetInspection specification
Transport
Mask
Acceptance Incomming QA
DRC
Frame generation, frame specification
Design department
Production control department
Mask manufacturing department
Wafer manufacturing department
In-house processing
Frame specification
Inspection data
Mask order sheetInspection specification
Wafer fab.
Mask fab.
1
3
2
5
4
Design
Process engineering department
Data server
GDSII data transfer server
ORC OPC generation
Inspection data
Inspection Recipe
Dummy generation
Mask Inspection
Specification code registrationFrame specificationapproval, issue
Pattern design
Dummy OPC Frame 1 3
2 4
5
StandardizationScope
Order Entry
Recipe Maintenance
Defect/Repair/Reviewclustering
SEMI-WG-C
SEMI-WG-B
SEMI-WG-A
Engineering Chain Potential Solutions: SEMI Reticle Data Management Task Force
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Mask Shop Metrics
A key addition to the 2003 roadmap is the inclusion of Mask Shop metrics from a Factory Information and Control System perspective
The 2003 metrics represent a 1st revision of analysis into this area.
In addition, we have included more detailed and refined mask shop metrics that are not quite ready for the 2003 publication, however, represent solid directions for 2004.
Mask file sizes per litho layer are increasing exponentially. This is causing the time to process the data required to write the masks to also increase exponentially.
While some of this cycle time can be reduced by advances in computing power, we believe that additional capabilities [algorithms, standardized data, etc.] are needed to keep mask cycle times and associated costs in check
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Year of Production 2003 2004 2005 2006 2007 2008 2009 2012 2015 2018
Wafer Diameter 300mm 300mm 300mm 300mm 300mm 300mm 300mm 450mm 450mm 450mm
Optical Mask Data File size per Layer (GB) from Litho
144 216 324 486 729 1094 1640 N/A N/A N/A
EUVL Mask Data File size per Layer (GB) from Litho
N/A N/A N/A N/A N/A 730 1096 2466 5550 12490
Time to send and load tape-out data into Mask Shop data system (hours)
5-10 6-12 6-12 6-12 6-12 6-12 6-12 6-12 6-12 6-12
Time for OPC calculations and data preparation for mask writer (days)
2.5- 5.5
4-8 4-8 4-8 4-8 4-8 4-8 4-8 4-8 4-8
OPC Time only (days) 2-4 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6
2003 Current Mask Shop Metrics
FI Metric Explanation
Time to send and load tape-out data into Mask Shop data system (hours)
Time in hours to send data from mask designer to mask shop and load it into the OPC application.
Time for OPC calculations and data preparation for mask writer (days)
Time in hours to perform OPC calculations + Time in hours to convert the output of the OPC engine to the format the mask writer understands + Time in hours to transmit the data into the mask writing system
OPC Time only (days) Time for OPC calculations only is the time in hours to perform the OPC calculations once the OPC application has received the tape-out data from the mask designer
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Year of Production 2003 2004 2005 2006 2007 2008 2009 2012 2015 2018
Wafer Diameter 300mm 300mm 300mm 300mm 300mm 300mm 300mm 450mm 450mm 450mm
Optical Mask Data File size per Layer (GB) from Litho
144 216 324 486 729 1094 1640 N/A N/A N/A
EUVL Mask Data File size per Layer (GB) from Litho
N/A N/A N/A N/A N/A 730 1096 2466 5550 12490
Data Transfer from Designer to OPC (hours)
5-10 6-12 6-12 6-12 6-12 6-12 6-12 6-12 6-12 6-12
OPC Time only (days) 2-4 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6
Send OPC results to Mask Developer (hours)
5-20 7.5-30 7.5-30 7.5-30 7.5-30 7.5-30 7.5-30 7.5-30 7.5-30 7.5-30
Mask Data Prep (hours) 10-18 15-27 15-27 15-27 15-27 15-27 15-27 15-27 15-27 15-27
Loading mask data into mask writer (hours)
2-4 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6 3-6
2004 Proposed Mask Shop Metrics(Work in Progress – Metrics will be updated in the 2004 version to show better details)
Key Notes:• These metrics show greater detail on the mask shop cycle time components and
will be updated and refined for 2004.• Starting in 2005, mask processing time starts to grow exponentially with the file
size and will take 250 to 511 days to process for each layer (see slide 34) unless improved computing power and new solutions are used.
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2004 Proposed Mask Shop Metrics(Work in Progress – Metrics will be updated in the 2004 version to show better details)
Metric Explanation
Data Transfer from Designer to OPC (hours)
Time in hours to send data from mask designer to Optical Proximity Correction (OPC) application.
OPC Time only (days) Time in days to perform the Optical Proximity Correction (OPC) calculations once the OPC application has received the tape-out data
Send OPC results to Mask Developer (hours)
Time in hours to send data to Mask Developer
Mask Data Prep (hours) Time in hours to convert the output of the Optical Proximity Correction (OPC) engine to the format the mask writer and mask inspection equipment understand
Loading mask data into mask writer (hours)
Time in hours to transmit the data into the mask writing equipment
Key Notes:• These metrics show greater detail on the mask shop cycle time components and
will be updated and refined for 2004.• Starting in 2005, mask processing time starts to grow exponentially with the file
size and will take 250 to 511 days to process for each layer (see slide 34) unless improved computing power and new solutions are used.
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Mask Operations - Cycle Time Reduction Required1. Data Transfer from Designer to OPC Application
2. OPC Calculations 3. Send OPC results to Mask Developer (at network transfer rate of 0.5 GB/hour)4. Mask Data Prep 5. Loading mask data into mask writer
Timing for Potential SolutionsResearch 2004-2005Development 2006
Qualification/Pre-Production 2007
Circuit DesignDesign
rule checkerOPC
rule checker
OPC ApplicationMask Data Prep (prepare data for
mask writer)Mask Writer
Potential Solutions
• OPC rule checker for circuit design to ensure it is possible to decorate the mask with OPC to provide the correct lines once imaged
• Better data structures (hierarchical), compaction & bigger data pipes to decrease time for data transfer from OPC to Mask Data Prep
• Need improved standard for specifying the mask specifications to decrease time to load data to Mask Writer
• Leverage learning from operational simulation modeling in mask operations to reduce data and manufacturing cycle times
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Mask Files and Cycle Times will Increase Exponentially unless New Solutions are Found
200
300
400
500
File
Siz
e in
GB
per
mas
k la
yer
7,500
10,000
Key Notes:• Starting in 2005, mask processing time starts to grow exponentially with the file
size and will take from 250 to 511 days to process for each layer unless improved computing power and new solutions are used.
2003 2006 2009 2012 20182015
Wo
rst
Cas
e M
ask
Dat
a P
rep
arat
ion
C
ycle
Tim
e (d
ays)
100
5,000
0 0
2,500
12,500
• Target: Keep mask production cycle times at 2004 levels (4-9 days per mask layer)
• Solutions are needed to keep cycle times from exploding
LegendBest Case Cycle Time
Worst Case Cycle Time
File Size
35
AMHS Direct Transport
36
AMHS is Changing to an On-Time Delivery System
Intra and InterSeparate System
Unified System(Dispatcher Base)
Unified System(Scheduler Base)
TransferThroughput
Transfer Time(Ave & Max)
Punctuality(On-Time)
Intra-Bay
Inter-Bay
Intra-Bay
Push Pull
Re-RouteAve & Max
Time
Wafer LevelTracking
CapacityPlanning
On-TimeDelivery
AMHSAMHS Key IndicatorKey Indicator
EquipmentView
Lot View
H/W Efforts
S/W Efforts
Reduce WIP
Schedule WIP
37
Direct Tool to Tool Transport Is Needed by 2004
Objectives: Reduce product processing cycle time Increase productivity of process tools Reduced storage requirements (# of stocker) Reduced total movement requirements
Priorities for Direct Delivery: Super Hot Lots (< 1% of WIP) & Other Hot Lots (~5% of WIP) Ensure bottleneck equipment is always busy
Capability Needs Tools indicate that WIP is needed ahead of time Event driven dispatching Transition to a delivery time based AMHS Integrated factory scheduling capabilities
Timing Research Required 2001-2003 Development Underway 2003-2005 Qualification/Pre-Production 2004-2006
S1 S2
T1 T2
S3 S4
T3 T4
S5 S6
T5 T6
S7 S8
T7 T8
S1 S2
T1 T2
S3 S4
T3 T4
S5 S6
T5 T6
S7 S8
T7 T8
Fully Connected OHV
OHV with Interbay Transport
Partially Connected OHVWith Conveyor Interbay
Many Direct Transport Concept Options
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Equipment Tracking System
Scheduling &
Dispatching System
b
Integrated FICS to Support Direct Transport
ProcessEquipment
UI
OHV
AMHS ControlSystem
Equipment Controllers
Information Bus
Processing nearly complete SECS/GEM
1a. Load port event signals carrier leaving OR1b. Equipment event indicates that processing
is nearly finished for priority lot2. PM schedule checked to verify no PM is
due3. Equipment Tracking System ensures
downstream tool is held available4. Dispatcher selects priority lot for
processing5. AMHS routes carrier directly to process
equipment
aProcess Chamber
GOAL: Reduce priority lot (“Super Hot Lots” & Other Hot Lots) cycle time through direct tool-to-tool moves without return to stocker
Requires integrated equipment, MES (to maintain lot priority), scheduling/dispatching, PM schedule, Factory Operations and AMHS
ProcessEquipment
UI
OHV
Process Chamber
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Research Opportunities
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Fab Operations and Design Modeling Laboratory
300 mm discrete event simulation models currently available for download from Sematech are not accurate
Events associated with process tools are represented with reasonable fidelity, but events associated with fab planning / control systems are approximations.
This simulation approach exposes the industry to significant economic risks, as design and operating decisions are based on simulation models that are known to be inaccurate.
Computing, software, and communication technologies have developed to the point where a new approach to fab simulation modeling is feasible.
Fab operations (process tools, AMHS, lots, operations, setups, quals, etc) can be modeled explicitly (simulated) in software that interfaces directly with “real” fab planning and control systems.
The industry needs a laboratory where the technologies and development issues associated with a true 300mm “virtual fab” can be addressed in a neutral, pre-competitive setting.
Employ available commercial software systems for fab planning and control. Develop and demonstrate the associated engineering tools for rapidly configuring this
virtual fab (e.g. alternative fab layouts or AMHS strategies.)
Concern: Most of the commercially available tools do not support today’s needs. How to we plan for the future when current tools do not support current capabilities?
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Future Research Data mining approach for managing Process Control &
Factory Operations data What are the key data items that data mining solutions must be able to
extract & provide ?
Modeling for Fault Detection and Run to Run Control What parameters are key to control (by process / by tool type)? What input parameters impact the output & how do they relate to one
another (multivariate control)?
Factory workflow control What business rules are needed between integration of key factory
systems (MES, MCS, Scheduler, Dispatcher, Equip Tracking) to optimize processing?
Operational scenarios showing equip / FICS / AMHS interactions to support Tool-to-Tool moves (Direct Transport)
Include exception handling
Opportunities / improvements for Mask Operation cycle time What specific improvements can be made to address the opportunities
identified by ITRS? What other opportunities are available to reduce cycle time or cost of
Mask Operations?