ISO/TR 23247-100:2025
(Main)Automation systems and integration — Digital twin framework for manufacturing — Part 100: Use case on management of semiconductor ingot growth process
Automation systems and integration — Digital twin framework for manufacturing — Part 100: Use case on management of semiconductor ingot growth process
This document describes a digital twin for monitoring and controlling the semiconductor ingot growth process. The use case is analysed and designed using the ISO 23247 series. The result is a systematic view of the use case implementation and a high-level design of the digital twins, which can be directly implemented using the readily available tools and languages, including those supported by the relevant standards.
Systèmes d'automatisation industrielle et intégration — Cadre technique de jumeau numérique dans un contexte de fabrication — Partie 100: Cas d’utilisation de la gestion du procédé de croissance des lingots de semiconducteurs
General Information
Standards Content (Sample)
Technical
Report
ISO/TR 23247-100
First edition
Automation systems and
2025-05
integration — Digital twin
framework for manufacturing —
Part 100:
Use case on management of
semiconductor ingot growth process
Systèmes d'automatisation industrielle et intégration —
Cadre technique de jumeau numérique dans un contexte de
fabrication —
Partie 100: Cas d’utilisation de la gestion du procédé de
croissance des lingots de semiconducteurs
Reference number
© ISO 2025
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ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Overview . 2
5 Operational sequences . 4
5.1 Process flow .4
5.2 Phase 1: Select and preparation .5
5.3 Phase 2: Melting and dopant addition.5
5.4 Phase 3: Seeding and crystal growth .6
5.5 Phase 4: Ingot pulling and initial testing .6
5.6 Phase 5: Documenting.7
6 Mapping to the framework . 7
6.1 Overview .7
6.2 Implementation using the framework .8
6.3 Mapping of the process digital twin to the digital twin entity .10
6.4 Mapping of ingot growth equipment digital twin to digital twin entity . 12
6.5 Mapping of ingot digital twin to digital twin entity . 13
7 Conclusion . 14
Bibliography .15
iii
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
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The procedures used to develop this document and those intended for its further maintenance are described
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This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
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iv
Introduction
The semiconductor ingot growing process is an important step in the production of semiconductor wafers,
which are used to manufacture electronic components such as integrated circuits and solar cells.
Despite advancements in automated ingot growth equipment, the conventional ingot growth process
continues to face challenges due to the degree of human intervention required, it often relies on operators'
experience and know-how. This dependence can lead to issues such as inaccurate quality, inconsistent
machine setup, unstable temperature maintenance, and inconsistent melting.
A digital twin for the ingot growth process can effectively address these issues by simulating and optimizing
the entire process in a virtual environment and reducing reliance on manual intervention.
Using a digital twin for monitoring and controlling the ingot growth process offers several advantages as
follows:
— Real-time monitoring: a digital twin allows continuous and real-time monitoring of the ingot growth
process. It provides detailed insights into key parameters such as temperature, growth rate, crystal
quality, and dopant concentration. This enables operators to detect deviations or anomalies early on and
take necessary corrective actions promptly.
— Process optimization: by analysing the data collected for the digital twin, it becomes possible to optimize the
ingot growth process. Patterns and trends can be identified, allowing for adjustments in various parameters
to improve yield, reduce defects and enhance the overall quality of the ingots and resulting wafers.
— Predictive maintenance: a digital twin can help predict maintenance requirements for the equipment
used in the ingot growth process. By monitoring equipment performance and analysing historical
data, the digital twin can help identify potential issues or deterioration in advance, enabling proactive
maintenance and minimizing unplanned downtime.
— Training and simulation: a digital twin can be used for training operators and engineers. It provides a
virtual environment where individuals can practice and simulate different scenarios without the need
for actual physical equipment. This helps in enhancing operational skills, testing new strategies, and
improving decision-making capabilities.
By leveraging the advantages of a digital twin, semiconductor manufacturers can gain deeper insights into
the ingot growth process, optimize production parameters, enhance quality control, and improve overall
productivity and efficiency.
v
Technical Report ISO/TR 23247-100:2025(en)
Automation systems and integration — Digital twin
framework for manufacturing —
Part 100:
Use case on management of semiconductor ingot growth process
1 Scope
This document describes a digital twin for monitoring and controlling the semiconductor ingot growth
process. The use case is analysed and designed using the ISO 23247 series. The result is a systematic view of
the use case implementation and a high-level design of the digital twins, which can be directly implemented
using the readily available tools and languages, including those supported by the relevant standards.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO 23247-1, Automation systems and integration — Digital twin framework for manufacturing — Part 1:
Overview and general principles
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 23247-1 and the following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
ingot
large, single crystal of semiconductor material, typically silicon, that serves as the starting material for the
production of semiconductor wafers
Note 1 to entry: The ingot is grown through a process called ingot growth, where high-purity silicon is melted and
then slowly solidified to form a large cylindrical crystal. This crystal is typically several inches in diameter and can be
several feet long, depending on the intended use and requirements.
Note 2 to entry: The ingot is the primary raw material from which individual semiconductor wafers are cut. These
wafers undergo further processing steps to fabricate electronic components such as integrated circuits (ICs) or solar
cells. The quality and characteristics of the ingot, including its crystalline structure and impurity levels, play a crucial
role in determining the performance and reliability of the resulting semiconductor devices.
3.2
ingot growth process
crystal growth process
step in semiconductor manufacturing where high-purity silicon is melted to grow a crystal
Note 1 to entry: In this process, a small single crystal called a “seed crystal” is carefully placed on molten silicon, and
as the seed crystal is slowly withdrawn, silicon atoms from the molten phase arrange themselves in an ordered lattice
structure, forming a larger crystal.
Note 2 to entry: The growth is controlled by parameters such as temperature and pulling rate, resulting in the uniform
and high-quality growth of the silicon crystal. The grown crystal is then used to produce semiconductor wafers.
4 Overview
The semiconductor ingot growth process is an important step in semiconductor wafer production. Figure 1
presents the conventional procedures of the ingot growth process.
Key
A melting of polysilicon, doping
B introduction of the seed crystal
C beginning of the crystal growth
D crystal pulling
E formed crystal with a residue of melted silicon
Figure 1 — Ingot growth process
The conventional procedures of the ingot growth process are as follows:
Despite advancements in automated ingot growth equipment, the conventional ingot growth process
continues to face challenges due to the degree of human intervention required, the process often relies
on operators' experience and know-how. This dependence can lead to issues such as inaccurate material
quality, inconsistent machine setup, unstable temperature maintenance and inconsistent melting.
A digital twin for the ingot growth process can effectively address these issues by simulating and optimizing
the entire process in a virtual environment. Using a digital twin enables predictive modeling and real-time
adjustments that enhance quality control and process stability.
Furthermore, by reducing reliance on manual intervention, digital twins facilitate a more consistent
application of best practices and compliance with industry standards, ultimately leading to improved
efficiency and reduced operational costs.
Table 1 summarizes the drawbacks and advantages of the conventional ingot growth process and the
solutions offered by a digital twin.
Table 1 — Comparison of conventional ingot growth process and digital twin solutions
Stages of ingot growth Drawbacks of the conven- Advantages of the digital
Solutions by digital twin
process tional method twin solution
Material selection Dependence on human judg- Automated material quality Increased accuracy in ma-
ment for material quality check using sensors and AI terial quality and reduced
algorithms reliance on human judgment
Preparation of equipment Manual cleaning and prepa- Real-time monitoring and Consistent equipment setup
ration can miss inconsist- feedback for equipment leading to standardized
encies preparation outcomes
Melting Fluctuating temperature Real-time temperature Stable and consistent melt-
and inconsistent melting monitoring and AI-based ing process
adjustments
Dopant addition Inaccurate dopant con- Precision-controlled dopant Precise and consistent dop-
centrations due to manual addition using feedback ing levels
operations loops
Seeding the crystal Potential misalignment of Automated alignment with Perfect seed alignment, lead-
the seed crystal feedback systems ing to better crystal quality
Crystal growth Variability in growth rate AI-controlled growth rate Consistent growth rate,
and potential defects based on real-time feedback reduced defects
Ingot pulling Reliance on operator skill for Automated pulling with Uniformity and quality of
pulling rate and parameters consistent parameters ingots
Testing and inspection Manual inspection can miss Automated inspection using Higher defect detection rate
subtle defects machine vision and AI and consistency
Integrating a digital twin into the ingot growth process can bring significant advantages and resolve
challenges associated with conventional methodologies. It offers a more data-driven, predictive, and
optimized approach, leading to better outcomes in terms of quality, efficiency, and safety.
Table 2 describes the use case for the management of the semiconductor ingot growth process using a use
case template given by ISO 23247-4:2021, Annex B.
Table 2 — Summary of the example for the management of the semiconductor ingot growth process
ID Case number 100
Use case name Management of the semiconductor ingot growth process
Application field Smart manufacturing
Life cycle stage(s)/
Production
phase(s) coverage
Status In-operation
Automated manufacturing process adjustment and tracking based on variable condi-
Scope
tions of the ingot growth equipment (i.e. furnace)
The ingot pulling process, the rate of pulling, temperature control and other parameters
Initial (problem) Situation are carefully controlled to ensure uniformity and quality. This can be done by operator
experience, process monitoring, feedback control system and closed-loop control system.
Objective(s) Production of ingot ensuring uniformity and quality
Developing a digital twin of the ingot growth process can provide the following:
TTabablele 2 2 ((ccoonnttiinnueuedd))
1. Real-time monitoring: a digital twin allows continuous and real-time monitoring of
the ingot growing process. It provides detailed insights into key parameters such as
temperature, growth rate, crystal quality and dopant concentration. This enables
operators to detect deviations or anomalies early on and take necessary corrective
actions promptly.
2. Process optimization: by analysing the data collected for a digital twin, it becomes
possible to optimize the ingot growing process. Patterns and trends can be identified,
allowing for adjustments in various parameters to improve yield, reduce defects and
Short description
enhance the overall quality of the ingots.
(not more than 150
words)
3. Predictive maintenance: by monitoring
...
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