=Paper=
{{Paper
|id=Vol-2025/paper_sami40_2
|storemode=property
|title=Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-2025/paper_sami40_2.pdf
|volume=Vol-2025
|authors=Bernd Waschneck,Lee Wei Fong Brian,Koh Chey Woon Benny,Christoph Rippler,Gottfried Schmid
}}
==Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing==
https://ceur-ws.org/Vol-2025/paper_sami40_2.pdf
Unified Frontend and Backend Industrie 4.0 Roadmap for
Semiconductor Manufacturing
Bernd Waschneck∗ Lee Wei Fong Brian Koh Chey Woon Benny
Graduate School advanced Infineon Technologies Asia Pacific Pte Infineon Technologies (Malaysia) Sdn.
Manufacturing Engineering (GSaME) Ltd Bhd.
- Universität Stuttgart Singapore 349282 75350 Melaka
70569 Stuttgart, Germany WeiFong.Lee@infineon.com CheyWoonBenny.Koh@infineon.com
bernd.waschneck@gsame.
uni-stuttgart.de
Christoph Rippler Gottfried Schmid
Infineon Technologies AG Infineon Technologies AG
93049 Regensburg, Germany 93049 Regensburg, Germany
christoph.rippler@infineon.com gottfried.schmid@infineon.com
ABSTRACT Industrie 4.0 is a set of contemporary automation and data sci-
Industrie 4.0 or digitalization of manufacturing currently create un- ence technologies, as well as organizational paradigms for manu-
certainty and unrest in the manufacturing industry as many players facturing in the 21st century. The core of Industrie 4.0 are Cyber-
do not know when, how or whether a disruptive change in industry Physical-Systems (CPS), which connect the physical and the virtual
will happen. Many published high-level strategies stay vague and world [5]. McKinsey & Company clusters the disruptive technolo-
leave practitioners unsure what to expect. Breaking Industrie 4.0 gies which enable this concept under four headlines [2]:
down into tangible pieces and steps is necessary for transporting • Data, computational power and connectivity,
the vision into reality. In this paper we develop an assessment • Analytics and intelligence,
and roadmap for Industrie 4.0 in semiconductor manufacturing - • Human-machine interaction,
the FINCA model. The model covers semiconductor frontend and • Digital-to-physical conversion.
backend manufacturing. It was successfully applied and tested at The high number of different technologies associated with Industrie
one of Europe’s largest semiconductor manufacturers, the Infineon 4.0 leads to the question of prioritization of different approaches
Technologies AG. Results from the assessment are presented in this at companies. In a fast moving field, with standardization still on-
paper. going, companies are reluctant to make investments in new tech-
nologies. High-level strategies offer little orientation as they do
CCS CONCEPTS not get specific enough to derive concrete recommendations. The
• Applied computing → Reference models; Enterprise infor- fear of investing into the wrong technology slows down innova-
mation systems; • General and reference; • Computer systems tion tremendously. Strategies need to be broken down into smaller
organization → Embedded and cyber-physical systems; parts to provide tangible steps towards the implementation of an
Industrie 4.0 vision.
KEYWORDS There are several assessments and roadmaps for Industrie 4.0
Industrie 4.0, Digitalization, Automation, Roadmap, Semiconductor and digitalization available (section 3). Still, no framework can
Manufacturing directly be applied to semiconductor manufacturing. Most assess-
ments are general and not industry-specific which leaves room
for interpretation and leads to subjective results of the assessment.
1 INTRODUCTION Additionally, no framework is currently available which can be
Industrie 4.0, digitalization or digital transformation create a spirit applied to semiconductor frontend and backend to compare the
of optimism but also a high uncertainty in the manufacturing in- level of digitalization in these manufacturing steps.
dustry. On a general level the three terms have the same meaning: In this paper, we present a framework for Industrie 4.0 in semi-
The introduction of digital technology into manufacturing. Many conductor manufacturing. The framework can be applied to fron-
consultancies and research institutions expect a high impact on tend and backend production. It can be used as assessment and
manufacturing by the so-called fourth industrial revolution. Fraun- roadmap for further development of the manufacturing site. The
hofer IPA estimates an average cost reduction potential of about purpose of the framework is
30% [3].
• to foster a common understanding between Industrial Engi-
∗ corresponding author neering, IT and Business on the existing capabilities,
• to create a vision for further development in semiconductor
SamI40 workshop at i-KNOW ’17 October 11-12, 2017, Graz, Austria
Copyright ©2017 for this paper by its authors. Copying permitted for private and manufacturing,
academic purposes. • to identify gaps at manufacturing sites,
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
• to support benchmarking between semiconductor manufac- for comparison of standards and identification of gaps in standard-
turing companies, and ization. RAMI 4.0 has successfully been applied to semiconductor
• to enable a fast assessment of acquired sites within integra- manufacturing [19].
tion projects. There are several Industrie 4.0 assessments and roadmaps avail-
able [1, 4, 6, 16]. Still, all of them are on a general level and cannot
In the section 2, we will describe the semiconductor manufac- be directly applied to semiconductor manufacturing. Our model is
turing process and the difference between frontend and backend. guided by the methodology of the VDMA Maturity model [1].
In section 3, existing frameworks, assessments and roadmaps for For technology development and the continuous shrinking of
Industrie 4.0 will be presented. We also highlight some existing semiconductor devices (Moore’s law) the International Technology
roadmaps for the semiconductor industry. None of the existing Roadmap for Semiconductors (IRTS [20] and ITRS 2.0 [7]) played a
frameworks offers a detailed semiconductor specific assessment crucial role. ITRS has a section on Factory Integration (FI, Manufac-
and roadmap which can be used for frontend and backend manu- turing IT) which provides guidance. However, ITRS is not updated
facturing. Therefore, we developed the FINCA Model which will be any more and is not linked to recent developments such as Industrie
presented in section 4. In section 5, the model is applied to frontend 4.0. The successor of the ITRS, the International Roadmap for De-
and backend sites of the Infineon Technologies AG and results are vices and Systems (IDRS [13]) which is part of the IEEE rebooting
discussed. In the conclusion (section 6), further research directions computing Initiative [15], is currently more focused on semicon-
and applications are presented. ductor technology. However, IDRS has not yet published influential
material on digitalization in semiconductor manufacturing.
2 SEMICONDUCTOR MANUFACTURING The increase in wafer size has always lead to substantial changes
PROCESS in manufacturing engineering at semiconductor plants. However,
the switch to 450mm wafer-size has been delayed and is not ex-
The semiconductor manufacturing process starts in the frontend.
pected within the next 2-3 years [12].
Structures in the sub-µm range are processed on raw wafers, which
Current initiatives mostly focus on the application of specific
are thin slices of crystalline silicon. The manufacturing process
technologies in semiconductor manufacturing without providing a
requires a cleanroom as dust or other particles can destroy the
full picture. Here, the focus is on intelligent algorithms [8, 10] and
sub-µm structures during the fabrication process. From a manu-
big data [14]. For specific areas in semicondcutor manufacturing
facturing point of view, frontends are complex job shops (for a
detailed roadmaps exist, e.g. for dispatching [18].
detailled description see [18]). This production type is usually used
All in all, the existing frameworks lack scope, are too general in
for custom-made items but semiconductor manufacturing is a mass
their recommendations or do not focus on digitalization.
production with a strong economy of scale. Industrial mass pro-
duction is mostly done in assembly lines but this concept is not
suitable for semiconductor manufacturing due the nature of the 4 THE FINCA MODEL
physical processes on the wafer. The FINCA model is an Industrie 4.0 assessment and roadmap
Semiconductor frontends are considered high-tech with complex for the semiconductor industry for both frontend and backend
processes and high levels of automation and digitalization. They are manufacturing. It was developed at Infineon Technologies AG. The
very capital intensive and mostly located in advanced economies. main properties are already encoded in the abbreviation FI-N-C-A:
After the frontend the wafers are brought into an intermediate • Factory Integration (FI):
storage facility, the so-called die bank. From the die bank the wafers FI refers to all IT services necessary to run a semiconductor
are taken to the backend, the second and final manufacturing step. production. In some companies the responsible organization
At the backend, the wafers are cut into separate dies. The dies is called “Manufacturing IT” and can be under IT or a differ-
are bonded to a leadframe, which connects the chip to electrical ent central function, local factories or cluster management.
contacts on the outside of the package. After the bonding, the chips Among different tasks, FI’s mission is to ensure standard-
are packaged and sealed in order to make them robust against ization within the company. At Infineon Technologies AG,
environmental impacts. The final product is now ready for sale. FI is under the corporate supply chain function and has the
In contrast to the frontend, the backend is traditionally a more mission to standardize across regions and manufacturing
mechanical and labor-intensive process rather located in low-cost levels while maintaining and even increasing capabilities of
countries. Latest backend technologies which comprises of assem- the manufacturing system landscape.
bly and final test became more sophisticated and more complex. • Normalized:
Capabilities are, wherever possible, independent from region,
3 RELATED WORK: INDUSTRIE 4.0 manufacturing levels (frontend, backend) and products. Ide-
ASSESSMENTS, FRAMEWORKS, ally any frontend site can be compared to any backend site
using the normalized capabilities. There are five levels for
BENCHMARKS AND ROADMAPS FOR THE
each category going from zero (no capability or no system
SEMICONDUCTOR INDUSTRY to support paper/manual process) to four (capability imple-
The Platform Industrie 4.0 released the Reference Architecture mented in professional IT system and used to the fullest
Model Industrie 4.0 (RAMI 4.0) [17]. RAMI 4.0 focuses on interfaces extend in regards of industry standards). Each category can
and standardization. The model has a broad scope. It is suitable be split into several sub-categories that need to be assessed
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Industrie 4.0 Level
7 Dimensions of Automation
Level 0 Level 1 Level 2 Level 3 Level 4
Workflow
Automation Process rules are defined Changes are documented Standalone system with Standalone system with Integrated system with
[paper] Semi Auto decision Auto decision proposal Auto decision making
proposal
WIP Flow Partially simulation [Lot Partially simulation Snap Shot simulation Snap Shot simulation Real Time simulation
Management Start] & manual [whole process] & manual [whole process] & Real [whole process] & Real [whole process] & Real
scheduling, dispatching & scheduling, Snap-Shot Time scheduling, Time scheduling, Time scheduling,
recoding dispatching & system dispatching & system dispatching & system dispatching & system
recoding recoding recoding recoding
Process Control Paper document, No Paperless document, Paperless document, Paperless document, Paperless document,
Automation recording, Manual Manual recording, Manual recording,Semi Manual recording, Online Auto recording, Online
control with No manual control with auto control with control with Evaluation control with Automatic
processing of data Storage of data for Analyzing data for for process planning process planning /
documentation process monitoring /control control
Manufacturing
Limited [<50%] Data Limited [<70%] Data Limited [<90%] Data Limited [<100%] Data FULL Data Availability /
Data Availability / Accuracy, Availability / Accuracy, Availability / Accuracy, Availability / Accuracy, Accuracy, with Automatic
Management with Manual data with Semi Auto data with Automatic data with Automatic data & Real time data
provision from provision from provision from provision from provision from
Product/Planning To MES Product/Planning To MES Product/Planning To MES Product/Planning To MES Product/Planning To MES
System. System. System. System. System.
Material Manual storage & retrival Manual storage & retrival Manual storage & retrival Automated storage & Automated storage &
Handling with Manual transport with Automated with Automated retrival with Automated retrival with Automated
delivery & Loading system transport delivery with transport delivery with transport delivery with transport delivery with
Semi auto Loading Automated Loading Automated Loading Automated Loading
system system system system [Linked up]
Material
Identification & Manual Identification, Auto Identification of Auto Identification of Auto Identification, Auto Identification,
Tracking validation & traceability. Product [Lot Level], Auto Mounted Material, Auto validation & traceability validation & traceability
Validation of employee Validation of employee [Strip Level]. [Single Device].
qualification . qualification .
Equipment No communication, SEC/GEM Connection or Automated Retrieval of Automated transfer of Load & Go indetification
Manual triggering for Other EQ Connection (Eg : data from machine, Auto Logistic data, Automated od Setup, Predictive
Automation Setup / Change over iTec/Tec), Machine Alarm Triggering for Setup / Release, Flexible Maintenance.
retrieval, Semi Auto Change over schedule of Maint base
Identification of on production situation.
setup/change over
Figure 1: Overview of the different dimensions and their maturity levels in the FINCA model.
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Automated
System decides what will happen decisions
Anticipate what will happen Prediction
Understand what is happening Knowledge
See what is happening Information
Connect the data Connection
Pre-requisite Stabilization & Standardization
Figure 2: A general model of different Industrie 4.0 maturity levels used at Infineon Technologies AG.
individually and aggregated using a simple numerical aver- • Information: From data to visualized information, e.g. KPIs
age (no weighting). and cockpits. System processes data to gain information and
• Capability: to create transparency.
Focusing entirely on capabilities and availability (rollout %) • Knowledge: Classification of events based on information
of those capabilities in one location/sub location (whatever that may lead to triggered actions or automatic generation
makes sense in terms of an existing homogeneous capability of proposals for action.
landscape). Out of scope are architecture, technology stack, • Prediction: Predict future events by simulation, machine
service levels, application names, source of the applications learning or complex mathematical/statistical models.
(build vs. buy) and infrastructure. Applications are only used • Automated decisions: Autonomous systems base their
in an abstract way like application classes e.g. “Manufactur- decisions on anticipated events and an awareness for their
ing Execution System (MES)”. Application roadmaps, sta- environment.
bility and architecture changes are only considered if they
add/remove capabilities.
• Assessment: To score a sub category, certain criteria have to be taken into
The FINCA model has to be assessed and filled by the busi- consideration. They are called differentiators. Those differentiators
ness owner of a site or sub-site, usually supported by busi- are specific features and their existence (or their extend) in a factory
ness domain experts, FI domain experts and FI business ana- can be used to rate a capability.
lysts. Business process experts and FI business analysts are in For example, the differentiator “tool connectivity” can be used to
charge to keep the normalization of all dimensions (the grid) rate the APC/FDC (Advanced Process Control / Fault Control and
up to date, so the comparison independent of manufacturing Classification) capability of a site. The tool connectivity determines
levels or region is always possible. to a great deal the amount of data that is available in the first place
to allow for process control and monitoring.
The FINCA Model consists of seven dimensions and several sub As some factories do not have a consistent level e.g. some lines
categories. Each dimension can achieve a value from level zero (low have more automation capabilities than other lines in the same
capability) to level four (maximum in terms of desired capability). factory, the level of a sub category can be broken down into multiple
An overview of the dimensions is given in Fig. 1. Every dimension rollout scenarios. As some machines in a factory have a better
is described more precisely with the number of sub categories connectivity than others a coverage/distribution/rollout percentage
that are to be rated during the assessment. While all levels are factor has to be applied. For example, if 80% of a factory’s machine
separately defined, they follow a general guideline with different park has an availability of 50% of the critical parameters covered
maturity levels. The different levels are depicted as a knowledge in APC/FDC (equals level four) and 20% is connected but has a
pyramid in Fig. 2. The foundation of the pyramid is “Stabilization coverage below 50% (equals level three), the overall rating for this
& Standardization” and goes up to “Automated decisions”: sub category is (80 · 4 + 20 · 3)/100 = 3.8.
Not always all five levels are available, in that case only existing
levels as per description have to be used.
• Stabilization & Standardization: Process is according to
Once each sub category has a calculated value based on the
standard and running stable. First, local data collection is in
differentiators and the distribution of coverage across the levels,
place.
the overall dimension level is to be calculated as the average (non
• Connection: Data sources are connected, standardized and
weighted) of its sub categories levels.
can be accessed globally.
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
In the following, the capability categories and their sub-categories 4.5 Material Handling
are presented. An overview of the capability categories is given in Material handling has three sub-categories
Fig. 1.
• Storage & Retrieval System
• Transport & Delivery System
4.1 Workflow Automation • Loading System [Robotics]
Workflow automation has seven sub-categories The definition of the levels is given in table 9.
• Deviation Management System
• WIP Routing (Workflow, Lot Route, . . . ) 4.6 Material Identification and Tracking
• Exception Management (Workflow) Material identification and tracking has nine sub-categories
• Subcon [External] / Inter Site [Internal] Management
• Product (WIP)/ Device (Lot, Strip, Chip) Identification, Vali-
• Small Lot Size Mastering [Lot Size 1]
dation & Traceability
• High Automation Load & Go
• Production Material & Wafer Material Identification, Valida-
• Experiment Management System for Sample and Engineer-
tion & Traceability
ing Lots
• Tool Identification, Validation & Traceability
The definition of the levels is given in table 1. • Carrier / Container Identification, Validation & Traceability
• Equipment Identification & Validation
4.2 WIP Flow Management • Operator Identification & Validation
WIP flow management has five sub-categories • Non-Productive Products / materials / tools [durables] /
Equipment
• Forecasting for Volume • Unified Material Mapping
• Dispatching • Split & Merge
• Scheduling
The definition of the levels is given in table 10 and 11.
• Work Area Control
• Capacity Planning
4.7 Equipment Automation
The definition of the levels is given in table 2. Equipment automation has six sub-categories
• Equipment Interface
4.3 Process Control Automation • Equipment Data
Process control automation has eleven sub-categories • Automated Setup/Change Over
• Documentation & Documents • Equipment Health Monitoring
• Dynamic Parameters • Maintenance
• Check Sheets • Input loading/ Output loading
• Work-In-Progress Data The definition of the levels is given in table 12 and 13.
• Sampling & Buyoff
• Recipe Handling 5 APPLICATION OF FINCA TO
• Process Time Window / N2 Cabinet SEMICONDUCTOR FRONTEND AND
• Statistical Process Control (SPC)
BACKEND MANUFACTURING SITES
• Statistical Bin Analysis/ Automatic Lot Release
• Advanced Process Control/ Fault Detection and Classifica- The FINCA model has been tested by semiconductor production
tion experts of the Infineon Technologies AG. The model has success-
• Metrology fully been applied as internal benchmark. The results were used
to identify best practices and lead factories in certain areas. Next
The definition of the levels is given in table 3 and 4. steps for development of the sites could be identified.
As an example for the application of the model the aggregated
4.4 Manufacturing Data Management results of one frontend and one backend site the Infineon Tech-
Manufacturing data management has eight sub-categories nologies AG are discussed. The aggregated outcomes are shown
in Fig. 3. The axis have been rescaled, but still allow for a relative
• Master Data Systems Availability
comparison and discussion.
• Master Data Systems Change/ Release
The semiconductor frontend is relatively advanced in terms of
• Master Data Static Systems Accuracy
Industrie 4.0. Frontends of the Infineon Technologies AG have a
• Master Data Dynamic Systems Accuracy
very high degree of automation. The Infineon site in Dresden is the
• Operational Production Reporting
200mm-wafer-size frontend with the highest degree of automation
• Aggregated Reporting
[11]. Traditionally, backends have a lower degree of automation
• Data Analysis
which can also be seen in this example. Still, backends are catching
• Lot Release
up as rising wages and energy prices in low cost manufacturing
The definition of the levels is given in table 5, 6, 7 and 8. locations put semiconductor manufacturers under pressure [9].
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Workflow Automation Workflow Automation
Equipment WIP flow Equipment
WIP flow Management
Automation Management Automation
Material Identification Process Control Material Identification Process Control
& Tracking Automation & Tracking Automation
Manufacturing Data Manufacturing Data
Material Handling Material Handling
Mgmt Mgmt
Application to a Frontend site of the Application to a Backend site of the
Infineon Technologies AG Infineon Technologies AG
Figure 3: The FINCA model applied to a frontend manufacturing site and a backend manufacturing site. The axis are uniformly
rescaled and do not show the absolute results of the model. A relative comparison is still valid.
According to a McKinsey & Company analysis Industrie 4.0 offers industries. It is especially suitable for job shop production systems
just the right tools for these productivity gains in backend [9]. with large amounts of standardized products. The authors invite
Digitalization in capital-intensive frontends has started in the other industries to apply and test the model in their scope and
early 1980s. The early introduction of Manufacturing Execution welcome the exchange of experiences with FINCA.
Systems (MES) has lead to legacy systems in production. The learn-
ing from the frontend MES could be applied to the backend where
ACKNOWLEDGMENTS
introduction started significantly later. This difference can be seen
in the FINCA dimension Manufacturing Data Management: The Thanks to all who contributed to the Framework: E Chui Geok,
frontend site scores relatively low, while this is a strong dimension Frank Banzhoff, Frank Lehmann, Lim Shaw Ming Daniel, Michael
for the backend. This reflects the effort at the backend sites in the Foerster, Joerg Domaschke, Woi Teck Khiong, Walter Laure, Hans-
recent years to introduce a solid foundation for digitalization. Juergen Wimberger, Sim Wee Sien, Yeo Danny, Teh Min Kiap,
The assessment has provided useful insights for the next steps at Christian Knoell, Michael Brueggemann, Daniela Eknigk, Mathias
both sites. Best practices or tools at different sites could be identified Haeuser, Chan Wai Ling, Nina Trude-Kuschel, Andrew Low, Goh
and transferred to other manufacturing locations. Kian Thong, Marco Tschemmer, Harald Heinrich, Tan Jee Liang Jef-
frey, Karl Horst Hohenwarter, Klaus Sandtner, Dirk Loeffelmacher,
6 CONCLUSION Gustl Kreuzberger, Ronald Bianchin, Torsten Quaas, Tong Soon
Hock Adrian, Olaf Herzog.
In this paper we presented an assessment and roadmap for Industrie
A part of the work has been performed in the project Power
4.0 for both frontends and backends. The FINCA model has been
Semiconductor and Electronics Manufacturing 4.0 (SemI40), under
successfully applied at Infineon Technologies AG. It has proven
grant agreement No 692466. The project is co-funded by grants
itself to be a useful tool at evaluation and roadmapping for future
from Austria, Germany, Italy, France, Portugal and - Electronic
improvements.
Component Systems for European Leadership Joint Undertaking
With this publication the authors want to foster the exchange
(ECSEL JU).
with science as well as other semiconductor companies. In science,
This work was supported as part of the joint undertaking “SemI40”
the FINCA model can be used as guideline how semiconductor
by the German Federal Ministry of Education and Research under
manufacturers envision manufacturing in the future. The FINCA
the grant 16ESE0074. Results and statements in this paper reflect
model assists researchers to find open challenges and problems.
the viewpoint of the authors.
New technologies and approaches from science can help semi-
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A APPENDIX
Level 0 Level 1 Level 2 Level 3 Level 4
Deviation Management System
Differentiators: Detection [Auto / Manual], Containment [Auto / Manual], Release [Auto / Manual]
Process to handle devi- Manual detection with Auto detection with de- Following harmonized Auto detection with
ation is defined, paper auto hold, auto detec- fined action / contain- containment action auto hold for non
recording of deviation, tion with manual hold ment for quality and through standardized quality related areas,
no deviation system in (standalone), manual yield areas (auto hold) deviation flow at FE / integration of FE-BE
place detection with manual BE deviation systems (BE:
hold List down three Lots
before and after if
problem detected)
WIP Routing (Workflow, Lot Route, . . . )
Differentiators: Decision [Auto / Semi Auto / Manual], # of Criteria [Single, Multiple]
Rule defined Manual decision by line Manual decision by en- Automated proposal by Automated proposal by
personnel gineers system but decision by system and decision by
human (Eg: Subcon se- system (Eg: Path selec-
lection based on load) tor)
Exception Management (Workflow)
Differentiators: Decision [Auto / Semi Auto / Manual], # of Criteria [Single, Multiple], Complexity [simple, complex
models], Traceability
Rule defined, no trace- Manual decision, simple Manual Decision, sim- Automated proposal by Automated proposal
ability of decision mak- models ple models, traceability system but decision by by system and de-
ing of decision making human (Eg : Subcon se- cision/execution by
lection based on load), system (Eg: Path Selec-
complex models, trace- tor), complex models,
ability of decision mak- traceability of decision
ing making
Subcon [External] / Inter Site [Internal] Management
Differentiators: Data Transfer [paper, File Transfer], Visibility [Black Box, Sub Operation, Sub Step], Data availability
Data exchange through Data exchange through Data exchange through Data exchange through Subcon MES is fully
paper file transfer (in & out file transfer for sub step file transfer for sub step, integrated to company
info) info (Eg: Subcon opera- process & equipment MES (including report-
tion points) info ing), real time view of
lot status, real time de-
viation control
Small Lot Size Mastering [Lot Size 1]
Differentiators: Data Transfer [paper, File Transfer], Visibility [Black Box, Sub Operation, Sub Step], Data availability
FE: Full wafer cassette FE: No full wafer FE: Compound Lot, BE: FE: - , BE: Lead frame lot Die level lot size pro-
processing, BE: Stan- cassette processing, BE: Sub Standard Lot size size process cess.
dard lot size (e.g. 25 Standard lot size (e.g. (e.g. Magazine)
Wafer) process for all 25 Wafer) process for
processes certain processes
High Automation Load & Go
Differentiators: Loading [Auto / Semi auto / Manual]
Manual loading Manual loading linked Semi auto loading Auto loading [with Auto loading [Full au-
with MES linked with MES manual robot feeding], tomation], linked with
linked with MES MES
Table 1: Workflow Automation
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Experiment Management System for Sample and Engineering Lots
Differentiators: Number of capabilities (auto split/merge lot, recipe name and parameter overwriting, APC overwriting,
. . .), Existence of an experiment management system
Manual creation & re- Fixed Route upon Fixed route upon re- Flexible route editing af-
lease, manual mainte- release, existing exper- lease some capabilities ter release all capabili-
nance, fixed/static alter- iment management ties
nate item (Route, Bill system, basic ca-
of Material, Tool plan...) pabilities (routing,
upon release, no experi- equipment/equipment-
ment management sys- setup/tools)
tem
Forecasting for Volume
Differentiators: Data Feed [Manual / Auto], Frequency, Scope [work center / line / factory], Method [Simulation /
Mathematic Formula]
FE: Simulation & mathematical optimization, BE: Mathematical optimization
Manual data feed, Manual data feed, daily Semi-automatic data Semi-automatic data Automatic data feed,
weekly forecasting, forecasting, work center feed, 6hrs - 8hrs fore- feed, 6hrs - 8hrs fore- 4hrs - 6hrs forecasting,
work center forecasting forecasting, manual re- casting, line forecasting, casting, whole factory, whole factory, auto re-
manual reporting porting manual Reporting auto reporting porting
Dispatching
Differentiators: Integrated line control [one system apply to whole supply chain], Compliance [work center / line /
factory], Flexibility [rules definition by Equipment / Work center/ line], Timeliness
<50% Compliance >50% Compliance >80% Compliance >90% Compliance 100% Compliance [Fully
Automated], real time,
integrated line control,
full flexibility
Scheduling
Differentiators: Integrated line control [one system applied to whole supply chain], Compliance [work center / line /
factory], Flexibility [rules definition by Equipment / Work center/ line], Timeliness, Data integrity, Scope [lot start /
whole line]
Paper recording of System recording of System warning of cre- Automated predictive Automated cre-
creation/update sched- creation/update sched- ation/update due, sys- creation/update ation/update schedule
ule (fixed time, volume ule (fixed time, volume tem stop of mainte- based on capacity op-
based) based), system warning nance due (integrated to timization (integrated
of maintenance due MES) to resource, tools, spare
parts demand, WIP)
Work Area Control [Radar]
Differentiators: Users [Operator / Supervisor / Engineers], Scope [work center, Line, Equipment], Information [4M -
Man, Machine, Method, Material], Timeliness, View consolidation [One View, Multiple, easy access, mobility]
View of critical line con- Snap-shot dashboard Snap-shot dashboard(1 Real-time dashboard Real-time Dashboard (1
trol information at the (multiple views) of view) of critical line con- (multiple views) of view) of critical line con-
equipment critical line control in- trol information (all Sys- critical line control in- trol information (all sys-
formation (all systems) tems) formation (all systems) tems)
Capacity Planning
Manual Single Work Center Multiple Work Center & Complete factory level, Complete factory level,
only (Bottle Neck), Line, manual manual and partial auto auto
manual
Table 2: WIP Flow Management
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Documents
Differentiators: Paper / paperless, Search for correct Doc, Revision management
Paper documents on Paperless documents Paperless documents Paperless document on Paperless document
equipment, manual on equipment, manual on equipment, semi au- equipment, automated in system, automated
search for the correct search for the correct tomated search for the search for the correct search for the correct
document (standalone), document (standalone), correct document (non document (integrated - document (integrated
manual control to manual control to integrated/standalone), one transaction), man- - one transaction),
display the latest display the latest manual/automated ual control to display automated control
revision revision control to display the the latest revision to display the latest
latest revision revision
Dynamic Parameters
Differentiators: Paper / paperless, Search for correct Document, Revision management
Paper documents on paperless documents Paperless documents Paperless info on equip- Paperless info in system,
equipment, manual on equipment, manual on equipment, semi ment, automated search automated search the
search for the correct search for the correct automated search for for the correct info (in- correct info (integrated
info (standalone), man- info (standalone), man- the correct info (non tegrated - one transac- - one transaction), auto-
ual control to display ual control to display integrated/standalone), tion), manual control to mated control to display
the latest revision the latest revision manual/automated display the latest revi- the latest revision
control to display the sion
latest revision
Check Sheet (Reminder to check tasks, anti-mix, Setup Yield, Test Program)
Differentiators: Paper / Paperless / Online control, Validation
paper check sheet with paper check sheet with paperless check sheet paperless check sheet online control
no validation validation, four eyes val- with validation, four with validation, four
idation eyes validation, defined eyes validation, defined
ranges ranges, warning/hold if
out of range
WIP Data (Equipment Data Collection, Lot Info)
Differentiators: Paper / paperless /online control, Validation
paper WIP data collec- paperless WIP data col- paperless WIP data col- paperless WIP data col- online control [auto col-
tion lection lection with validation, lection with validation, lection of WIP data]
defined ranges warning/hold if out of
range
Sampling & Buyoff (Products)
Differentiators: Paper / paperless / nothing, Triggering, Sampling Type [Static / Dynamic]
paper based, manual paperless, manual trig- paperless, automated paperless, automated paperless, automated
triggering, static sam- gering, static sampling, triggering, static sam- triggering, static sam- triggering, dynamic
pling, 100% sampling fix sampling rate, execu- pling, fix sampling rate, pling, fix sampling rate, sampling, execution
rate, execution [man- tion [manual] execution [manual] execution [automated] [automated]
ual]
Recipe Handling (Tester recipe, Handler recipe, Assembly Recipe)
Differentiators: Recipe Release, Recipe select / download, Recipe Validation [Body check]
manual select from local semi auto select from lo- manual download of semi automated down- automated download
m/c, manual adjustment cal m/c, manual adjust- recipe from central stor- load of recipe from cen- of recipe from central
after download ment after download age, manual adjustment tral storage, manual ad- storage (one transac-
after download justment after down- tion), no adjustment
load after download
Process Time Window / N2 Cabinet (Min / Max time control)
Differentiators: Data Collection, Data Validation, Decision Making
no recording manual recording, man- automated recording, automated recording, automated recording,
ual validation manual validation automated validation automated validation
[min max] [pre-warning before
and during process],
automated decision
Table 3: Process Control Automation, part 1
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Statistical Process Control (SPC)
Differentiators: Data Collection, Data Validation, Decision Making (Lot Hold/ Tool Stop/ Trigger re-measurement)
manual data collection, manual data collection, manual data collection, automated data collec-
manual validation, man- manual validation, auto- automated validation, tion, automated valida-
ual decision making mated decision making automated decision tion, automated deci-
(lot hold) making (lot hold) sion making (lot hold,
tool stop, trigger re-
measurement)
Statistical Bin Analysis/ Automatic Lot Release (ALR)
Differentiators: Data Collection, Analysis level, Validation, Decision Making)
manual input, h-bin manual input, h-bin manual input, h-bin automated input (from automated input (from
analysis only, manual analysis only, auto analysis only, auto test/handler summary), test/handler summary),
Defect Density Manage- Defect Density Manage- Defect Density Manage- h-bin & s-bin analysis automated analysis of
ment System trigger, ment System trigger, ment System trigger, (offline ALR), manual s-bin (ALR), automated
manual validation manual validation automated validation Defect Density Manage- Defect Density Manage-
(lot hold) ment System trigger, au- ment System trigger, au-
tomated validation (lot tomated validation (lot
hold) hold)
Advanced Process Control/ Fault Detection and Classification
Differentiators: Tool Connectivity, Online Reaction, Out-of-Control Action Plan (OOCAP), Regular review process
implemented
tools not connected [no tool connected [apc 1st online reaction [tool 50% critical parameters >90% critical param-
apc data flow] data flow], some lim- stop, lot hold, inhibit online reaction [tool eters online reaction
its defined, e-mail next lot] has been estab- stop, lot hold, inhibit [tool stop, lot hold,
notification lished with oocap. next lot] has been estab- inhibit next lot] has
lished with oocap. been established with
oocap. Regular review
process implemented.
Metrology
Differentiators: Scope [all measurement], Virtual for level four
SPC, physical measure- SPC, physical measure- SPC, physical measure- APC, linked with MES virtual metrology
ment at define time in- ment at a control inter- ment at a control inter-
terval val [event base] val, linked with MES
Table 4: Process Control Automation, part 2
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Master Data Systems Availability
Differentiators: Standardization local, Data coverage (compared to overall site’s master data content), Timeliness
non harmonized, no - limited local change - high level of standard-
use of global master of global master data ization global/local, no
data sets, only cascade sets, 4M (Man, Machine, local change of global
from global to local on Method, Material) master data sets, 4M
call partially available in (Man, Machine, Method,
MES, batch/delayed Material) fully available
cascade of global to in MES, global immedi-
local ately cascade to local
Master Data Systems Change/ Release
Differentiators: Maintenance [Manual / Auto], Release [Manual / Auto], Personal efficiency of the staff using the system,
Capability of mass update automation, Workflow support (new workflow, workflow controlled data + performance
management), Flexibility of data entry, Integrated effect analysis capability for change management, Analysis capability,
Rollback capability
manual maintenance/ semi-auto data changes auto mainte- semi-auto data changes auto mainte-
synchronization/ en- from global plan- nance/synchronization, from global plan- nance/synchronization,
richment, manual ning/product to MES manual release, mapped ning/product to MES auto release, auto data
release, manual data (non assisted) data structures between (assisted), auto enrich- changes from global
changes from global global and local with ment of master data planning/product to
planning/product to adaptions and aggre- locally high level of MES, equivalent data
MES, not connected gation, ability to do analysis capability structures between
data structures between mass-change for global implemented global and local (fast
global and local (tedious change for non depen- sync), ability to do
sync), analysis capabil- dency items, ability mass-changes for items
ity not set up, rollbacks to do mass-release for of dependency, ability
are not supported global change for non to do mass-release
dependency items low for global changes for
level of analysis capabil- items of dependency,
ity implemented, some full rollback capability
manual enrichment of on mass and individual
master data locally changes full object
dependent level of
analysis capability im-
plemented, not required
enrichment of master
data locally
Master Data Static Systems Accuracy
Differentiators: Integrity [accuracy / timely]
low data integrity, no in- - high data integrity, se- high data integrity, se- high data integrity, in-
formation on integrity lect/pick lists assisted lect/pick lists assisted formation on integrity
available data entry for all avail- data entry for reduced available (plausibility
able selections selections (segment rel- check) measurable
evant)
Master Data Dynamic Systems Accuracy
Differentiators: Integrity [accuracy / timely]
low data integrity, no in- select/pick lists assisted high data integrity, se- - high data integrity, in-
formation on integrity data entry generated lect/pick lists assisted formation on integrity
available, no aides (pick manual input data entry generated available (plausibility
lists) from static Master Data check) measurable,
highly consistent with
static Master Data
Table 5: Manufacturing Data Management, part 1
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Operational Production Reporting
Differentiators: Standardization [Local, Global], Flexibility [Fix, flexible], Data Storage [Equipment, Local, Central],
Integrity [accuracy / timely], Drill down functionality/capability, Automated report generation, Interlinking with mobile
devices
user generated reports central generated cus- mixture of cluster - 1. regular report 1. on time
by segment/site/user tomized reports by seg- wide (FE & BE) and 2. cluster-wide harmo- 2. harmonized reports
groups (Business ment/site/user groups, customized reports nized reports between FE & BE
Objects, excel), no no standardization on by segment/site/user a) same formula, data a) same formula, data
standardization of cluster level - FE & BE, groups, no standard- source source
reporting & manually no link to mobile de- ization between FE & b) same tool b) same tool
generated, no link to vices BE, no link to mobile c) with different level of c) with different level of
mobile devices devices aggregation aggregation
3. no standardization be- 3. can be easily cus-
tween FE & BE tomized & automated
4. partially interlinking reporting
to mobile devices 4. drill down functional-
ity is available & easy to
use
5. interface to manufac-
turing reporting
6. able to fulfill all levels
of reporting from man-
agement to engineering
7. fully interlinking to
mobile devices
Aggregated Reporting
Differentiators: Standardization [Local, Global], Flexibility [Fix, flexible], Data Storage [Equipment, Local, Central],
Integrity [accuracy / timely], Drill down functionality/capability, Automated report generation, Interlinking with mobile
devices.
user generated reports central generated cus- mixture of cluster - 1. regular report 1. on time
by segment/site/user tomized reports by seg- wide (FE & BE) and 2. cluster-wide harmo- 2. harmonized reports
groups (bo, excel), ment/site/user groups, customized reports nized reports between FE & BE
no standardization of no standardization on by segment/site/user a) same formula, data a) same formula, data
reporting & manually cluster level - FE & BE, groups, no standard- source source
generated, no link to no link to mobile de- ization between FE & b) same tool b) same tool
mobile devices vices BE, no link to mobile c) with different level of c) with different level of
devices aggregation aggregation
3. no standardization be- 3. can be easily cus-
tween FE & BE tomized & automated
4. partially interlinking reporting
to mobile devices 4. drill down functional-
ity is available & easy to
use
5. interface to manufac-
turing reporting
6. able to fulfill all levels
of reporting from man-
agement to engineering
7. fully interlinking to
mobile devices
Table 6: Manufacturing Data Management, part 2
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Data Analysis
Differentiators: Correlation along the Supply Chain, Usages of multiple relevant data sources/capability, Data com-
pleteness & availability, Data accuracy, Data integrity, Access speed, On-line data access, Stability, Handling of high
volume data, Robustness & performance capability, Fast & interactive analysis capability/functionality, Coverage in
terms of statistical methods (existing/available), Flexible to interact between different software system, Automation
capability
Data correlation along Data correlation within Data correlation within Data correlation be- Full Data correlation
the supply chain is not FE or BE supply chain FE or BE supply chain tween FE & BE supply between FE & BE
possible. Data availabil- is possible. Data avail- is possible. Data avail- chain is possible. Data supply chain. Full
ity for certain facili- ability for all facilities ability for all facilities availability for all Data availability for
ties along the supply within FE or BE sup- within FE or BE sup- facilities within FE & all facilities within
chain. Data complete- ply chain. Data com- ply chain. Data com- BE supply chain. Data FE & BE supply chain
ness & availability poor pleteness & availabil- pleteness & availabil- completeness & avail- (including relevant
& not link to analysis ity moderate & partially ity good & linked to ability good & linked data from Silicon
system. Low data accu- linked to analysis sys- analysis system. Moder- to analysis system. Foundry/Outsourcing
racy with no monitor- tem. Low data accuracy ate data accuracy with Good data accuracy And Test (OSAT) with
ing capability. No on- with manual monitor- manual monitoring ef- with semi-automated reference to contract).
line access. Slow per- ing effort. No on-line ac- fort. Low on-line ac- monitoring. Partial Excellence data com-
formance of data access cess. Moderate perfor- cess. Good performance on-line access. Good pleteness & availability
& unstable software so- mance of data access of data access & soft- performance of data & fully linked to analy-
lution. Statistical meth- & software solution ful- ware solution fulfills for access & software sis system. Full on-line
ods are not state of the fills for simple analysis most of the analysis solution fulfills for all access. Excellence data
art and not standard- tasks. Statistical meth- tasks. Statistical meth- of the analysis tasks. accuracy with fully
ize within software so- ods are not state of the ods are state of the Statistical methods automated monitoring
lution. Handling of high art and not standard- art and available in ex- are state of the art & reaction to deviations.
volume data is not possi- ize within software so- isting non-harmonized and available in exist- Excellence performance
ble. Offline analysis soft- lution. Handling of high software solution. Tech- ing non-harmonized of data access & soft-
ware is not aligned be- volume data is not possi- nology of software sys- software solution. ware solution fulfills for
tween FE & BE. Inter- ble. Offline analysis soft- tem is not state of the Technology of software all of the analysis tasks.
action to other solution ware is not aligned be- art. Handling of high system is partially state Statistical methods are
system is not possible. tween FE & BE. Inter- volume data is not possi- of the art. Handling of state of the art and
No automation capabil- action to other solution ble. Offline analysis soft- high volume data is par- within harmonized
ity. system is not possible. ware is partially aligned tially possible. Offline software solution.
No automation capabil- between FE & BE. Inter- analysis software is par- Technology of software
ity. action to other solution tially aligned between system is state of the art.
system is partially possi- FE & BE. Interaction to Ability to handle high
ble. Low automation ca- other solution system volume data according
pability is partially possible. to requirement. Offline
Moderate automation analysis software is
capability. fully aligned across FE
& BE. Full interaction
to other solution sys-
tem. Full automation
capability.
Table 7: Manufacturing Data Management, part 3
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Lot Release
Differentiators: Data completeness, Data integrity/accuracy, Decision Making, Meet quality requirements, Linkage to
other software system (eg. QMP/DDM, archive viewer, Esquare, analysis software . . . ), Automated configuration & han-
dling of different type of configuration, Storage capability, Process reporting & analysis capability, Speed/performance/
stability, Inter-site/production capability
data completeness & data completeness & data completeness & data completeness & data completeness &
availability, poor & no availability, moderate availability, moderate availability, good & availability, excellence
linkage to software sys- & limited linkage to & limited linkage to full linkage to software & full linkage to soft-
tem, automated deci- software system, auto- software system, auto- system, automated ware system, fully
sion making not pos- mated decision making mated decision making decision making par- automated decision
sible, software system not possible, software not possible, software tially possible, software making based on estab-
not meeting quality re- system partially meet- system meeting quality system meeting quality lished rules, software
quirement, linkage to ing quality requirement, requirement, linkage to requirement, linkage system meeting quality
other software system linkage to other soft- other software system to other software requirement, linkage to
not possible, manual ware system partially partially possible, semi- system available, semi- other software system
configuration & limited possible, manual con- automated & handles automated & handles available, automated &
in terms of complex- figuration & limited in partially complex partial complex config- handles fully complex
ity, no storage capabil- terms of complexity, configuration, limited uration with limited configuration with FE &
ity, no process report- no storage capability, storage capability, lim- FE & BE linkage, good BE linkage. excellence
ing & analysis capabil- no process reporting ited process reporting storage capability, good storage capability excel-
ity, slow performance & analysis capability, & analysis capability, process reporting & lence process reporting
& unstable, no inter- slow performance & moderate performance analysis capability, & analysis capability,
site/production linkag, unstable, limited inter- & stable, limited good performance & excellence performance
no FE & BE interlinked, site/production linkage, inter-site/production stable, partial inter- & stable, full inter-
no standard software no FE & BE interlinked, linkage, no FE & BE site/production linkage, site/production linkage,
system between FE or no standard software interlinked, partially partial FE & BE inter- fully interlinked FE
BE, no different levels of system between FE or harmonized software linked, harmonized & BE, harmonized
users administration BE, no different levels system for FE or BE, no software system for FE software system across
of users administration different levels of users or BE, different levels FE & BE. different levels
administration of users administration of users administration
partially available fully available
Table 8: Manufacturing Data Management, part 4
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Storage & Retrieval System
Differentiators: Manual/ Assisted / Auto, Manual record/ Standalone / Link to MES, Link to Transportation System
automated storage & re- automated storage
trieval system, linked & retrieval system,
to MES, no link to linked to MES, linked to
transport system, (FE: transport system, (FE:
stocker) stocker)
Transport & Delivery System
Differentiators: Manual / Auto Transport System, Standalone / Link to Storage System, Link to Dispatching System,
To Drop Point / Equipment, Link to Scheduling System
automated transport automated transport
system (conveyer, system, (conveyer, AGV,
AGV), linked to MES, Automated Material
linked to storage sys- Handling System),
tem, to drop point / linked to MES, linked
Equipment to storage system, to
drop point / Equipment,
linked to scheduling
system
Loading System [Robotics]
Differentiators: Manual / Auto Transport System, Standalone / Link to Storage System, Link to Dispatching System,
To Drop Point / Equipment, Link to Scheduling System
auto link to MES [closed
loop] & scheduling
Table 9: Material Handling
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Product (WIP)/ Device (Lot, Strip, Chip) Identification, Validation & Traceability
Differentiators: Level [Lot/ Device / Wafer / Strip / Chip], Identify [Manual / Semi Auto / Auto], Validate [Type, ID,
Shelf Life, Floor Life], Trace [Manual / Semi Auto / Auto], Equipment Internal wafer tracking
manual identification automated identifi- automated identifi- Automated identifica- automated identifi-
on lot level, manual cation on lot level , cation on lot level, tion on strip level, FE: cation on strip level,
validation of in/out- manual validation of magazine, reel, automated identifica- automated identifica-
quantity in/out-quantity FE: automated identifi- tion on wafer level, tion on single device
cation on wafer level, automated validation level after simulation,
automated validation of in/out - quantity, equip- automated validation
in/out-quantity ment internal wafer on strip and single
tracking device level,
FE: chip level traceabil-
ity [only applicable for
some process steps]
Material consumption & Wafer Material Identification, Validation & Traceability
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [Type, ID, Shelf Life, Floor Life], Trace [Manual / Semi
Auto / Auto]
manual identification manual identification semi auto identification semi auto identification automated identifi-
(sticker), manual record- (sticker), manual record- (barcode), semi auto (barcode), automated cation (Equipment :
ing (paper), manual val- ing (system), auto recording (barcode), recording (m/c reader), RFID/barcode), auto-
idation (BOM, floor life, validation (BOM, floor auto validation (BOM, automated validation mated recording (m/c
shelf life) life, shelf life) floor life, shelf life) (Equipment : BOM, reader), automated
floor life, shelf life) validation (Equipment:
BOM, floor life, shelf
life, consumption)
Tool Identification, Validation & Traceability
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [Type, ID, Life span, Maintenance cycle], Trace [Manual
/ Semi Auto / Auto]
manual identification manual identification semi auto identification semi auto identification automated iden-
(sticker), manual record- (sticker), manual record- (barcode), semi auto (barcode), automated tification (EG :
ing (paper), manual val- ing (system), automated recording (barcode), recording (m/c reader), RFID/barcode), au-
idation (group, ID) validation (group, ID) automated validation automated validation tomated recording (m/c
(group, ID) (group, ID) reader), automated
validation (group, ID,
lifespan)
Carrier / Container Identification, Validation & Traceability
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [Type, ID, Life span, Maintenance cycle], Trace [Manual
/ Semi Auto / Auto]
same as above
Equipment Identification & Validation
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [Type, ID, Life span, Maintenance cycle], Trace [Manual
/ Semi Auto / Auto]
same as above
Table 10: Material Identification and Tracking, part 1
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Operator Identification & Validation
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [qualify / not qualify], Trace [Manual / Semi Auto/
Auto]
manual identification, manual identification, semi auto identification semi auto identification automated identifi-
manual recording (pa- manual recording (sys- (manual login + bar- (single sign-on), auto- cation (Equipment:
per), manual validation tem), auto validation code), semi auto record- mated recording, auto- RFID) automated
(certification) (certification) ing (barcode), auto vali- mated validation (certi- recording (Equipment :
dation (certification) fication) M/C reader) automated
validation (certification)
Non Productive Products / Materials / Tools [durables] / Equipments
Differentiators: Identify [Manual / Semi Auto / Auto], Validate [Type, ID, Life span, Maintenance cycle, Floor life,
Shelf Life], Trace [Manual / Semi Auto / Auto]
manual identification manual identification semi auto identification semi auto identification automated identifica-
(sticker), manual record- (sticker), manual record- (barcode), semi auto (barcode), automated tion (eg : RFID/barcode),
ing (paper), manual val- ing (system), auto recording (barcode), recording (M/C reader), automated recording
idation (BOM, floor life, validation (BOM, floor auto validation (BOM, automated validation (M/C reader), au-
shelf life) life, shelf life) floor life, shelf life) (Equipment : BOM, tomated validation
floor life, shelf life) (Equipment : BOM,
floor life, shelf life,
consumption)
Unified Material Mapping
Differentiators: Scope [Full / partial supply chain]
no identification standalone system, par- standalone system, par- linked with MES, par- linked with mes, full
tial supply chain imple- tial supply chain imple- tial supply chain imple- supply chain implemen-
mentation, manual iden- mentation, auto identifi- mentation, auto identifi- tation, auto identifica-
tification cation cation tion
Split & Merge
Differentiators: Compliance [Manual / Auto], Execution [Manual / Auto]
no rules applied rules in place, manual auto validation by sys- auto splitting by system auto merging by sys-
validation of rules by tem tem according to de-
line personnel fined rules
Table 11: Material Identification and Tracking, part 2
Unified Frontend and Backend Industrie 4.0 Roadmap for Semiconductor Manufacturing i-know ’17, October 11.-12., 2017, Graz, Austria
Level 0 Level 1 Level 2 Level 3 Level 4
Equipment Interface
Differentiators: No Connection / Serial / Ethernet, File Transfer/ SECS/GEM / Interface A
no connection serial / GPIB / USB, file SECS/GEM - serial port, SECS/GEM - ether-
transfer, legacy protocol (min 9600 baud rate - net (HSMS - high
low data bandwidth) speed SECS messag-
ing services, high
data rate - 10mb/sec)
SECS/GEM, Interface A
(extreme high data rate
- > 100mb/sec)
Equipment Data
Differentiators: Status [Up/Down], Event [Alarms / Start / Stop], Parameter [Input / Output], Result [Pass / fail],
Frequency [Real time for the smallest Unit]
status - up/down status - signal from status - SECS/GEM, status - SECS/GEM,
(tower light), event - equipment to external, event - unlimited alarm event - automated
alarm (within equip- event - limited pre-set list from equipment, alarm list from equip-
ment), result - complete list (manual selection), result - complete cy- ment, result - complete
cycle/stop, parameter - result - complete cy- cle/stop, parameter cycle/stop, param-
internal view only cle/stop, parameter - in- - RMS capable, tool eter - RMS & APC
ternal view only start/stop (input/output) capable,
tool start/stop
Automated Setup/Change Over
Differentiators: Triggering [Auto / Manual], Identification [Auto / Manual], Change over [Auto / manual]
mechanism - manual, mechanism - manual, mechanism - auto mechanism - auto mechanism - auto
tool - manual, lot man- tool - manual, lot man- change by recipe con- change, tool - auto change, tool - auto
agement - no, recipe - agement - manual key trol, tool - manual, lot change, lot manage- change, lot manage-
no in lot ID, recipe - man- management - scan ID, ment - by host control, ment - by host control,
ual recipe selection lot ID, recipe - RMS recipe - RMS auto recipe - RMS auto
manual download download download, automated
release [inline buy off],
automated calibration,
automated parameter
adjust
Equipment Health Monitoring
Differentiators: # of critical parameters to be monitored, Availability
no monitoring, indica- monitoring [snap shot], monitoring [snap shot], monitoring [snap shot], monitoring [real time],
tor / counter only only equipment status, equipment status & crit- equipment status, criti- equipment status, crit-
simple health moni- ical alarm, equipment cal alarm & critical pa- ical alarm & critical
toring on machine (eg: with intelligent sensor rameter, real time APC, parameter, linked with
timeout: servo motor to provide local heath health data from ma- lot ID, real time APC,
and communication monitoring - equipment chine used to have intel- health data from ma-
within the equipment) related ligent process control - chine used to have intel-
offline and not real-time ligent process control -
(end of a day) offline and real-time (ev-
ery lot)
Table 12: Equipment Automation, part 1
i-know ’17, October 11.-12., 2017, Graz, Austria Bernd Waschneck et al.
Level 0 Level 1 Level 2 Level 3 Level 4
Maintenance
Differentiators: Reactive, Proactive, Preventive, Predictive, Assisted Maintenance, Close Loop, Maintenance Monitoring
run to fail [break down] time & volume based time & volume based time & volume based predictive modeling,
maintenance, fixed maintenance, fixed maintenance, inte- automated scheduling
schedule / volume schedule / volume, grated to SAP & based on production
integrated to SAP, MES situation e.g. loading,
equipment with intelli- integrated to SAP &
gent sensor to provide MES
local heath monitoring
- equipment related,
advice what needs to be
changed before critical
failure
Input loading/Output loading (only backend)
Differentiators: Batch size, Validation Capability
single input / single out- batch loading at input batch loading at input & robotic handling)
put loading, manual val- & output manual valida- output, auto validation,
idation tion support automated
loading/unloading
(Automated Material
Handling System, AGV,
overhead track
Table 13: Equipment Automation, part 2