WO2022122715A1 - Device and method for continuous process optimization in the production of semiconductor components - Google Patents

Device and method for continuous process optimization in the production of semiconductor components Download PDF

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Publication number
WO2022122715A1
WO2022122715A1 PCT/EP2021/084542 EP2021084542W WO2022122715A1 WO 2022122715 A1 WO2022122715 A1 WO 2022122715A1 EP 2021084542 W EP2021084542 W EP 2021084542W WO 2022122715 A1 WO2022122715 A1 WO 2022122715A1
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data
information
simulation
relational database
simulation environment
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PCT/EP2021/084542
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German (de)
French (fr)
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Sarah FISCHBACH
Peter Ebersbach
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Robert Bosch Gmbh
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Publication of WO2022122715A1 publication Critical patent/WO2022122715A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32385—What is simulated, manufacturing process and compare results with real process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/45—Nc applications
    • G05B2219/45031—Manufacturing semiconductor wafers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/18—Manufacturability analysis or optimisation for manufacturability
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to a device and a method for continuous process optimization when manufacturing semiconductor components.
  • simulations are carried out that take into account the device geometry and production processes with regard to manufacturing costs, specifications of the electrical performance of the devices and reliability.
  • the simulation methods support technology and process development in semiconductor production, so that the necessary technical product specifications are ensured.
  • the respective component models are often viewed as individual objects in the simulations.
  • the simulations are therefore limited to simple circuits in which only a certain number of interconnected components can be simulated due to the available computing capacities.
  • Simulation environments additionally consider estimates of process settings to enable the product development cycle. Due to the limited access to such data, the simulation results have limited accuracy.
  • the assumptions of the process behavior and data from error analyzes are often the only input data. Process adjustments can be simulated in advance so that their influence on the components to be manufactured can be estimated. These can be taken into account before the start of production.
  • the existing modules and process chains must be precisely calibrated in order to provide suitable simulation results. Process assumptions and design rules are applied. When using experimental results, the model calibration is successively adjusted.
  • the disadvantage here is that only process properties such as etching rate, diffusion profiles, material properties and selectivity are taken into account in the simulations and simulation environments.
  • the object of the invention is to overcome these disadvantages.
  • the device for continuous process optimization in the manufacture of semiconductor devices comprises a non-relational database, a data preparation device, a simulation environment device and a simulation analysis device.
  • the non-relational database includes manufacturing data and virtual measurement data.
  • the data conditioner provides information about process data assumptions and physical semiconductor device data.
  • the simulation environment device includes manufacturing plant information, semiconductor device information, semiconductor device model structures, and process models.
  • the simulation environment device generates a variety of output information depending on the manufacturing equipment information, the semiconductor device information and the manufacturing data and the virtual measurement data of the non-relational database, the simulation environment device outputs the variety of output information to the simulation analysis device.
  • the manufacturing line has a digital twin, so various parameters during the manufacturing process are at the Process optimization can be taken into account.
  • This means current production data are constantly compared with the simulation predictions.
  • the advantage here is that the connection of the big data infrastructure with the simulation environment leads to an improvement in process modeling, taking into account real production data, the exchange of process assumptions and an increase in data quality.
  • the multitude of pieces of initial information include semiconductor device simulation results, process optimization data, design experiment verifications, process window estimates and semiconductor device predictions.
  • the data preparation device comprises transformed factory data, with the data preparation device being set up to calibrate process models of the simulation environment device.
  • the advantage here is that new parameters can be created directly from the production process or indirectly with the help of algorithms and used with sensitivity to product properties.
  • the etching rate can be displayed with recipe parameters and sensory data of the process and thus deeper information can be used in the simulation.
  • the simulation environment device includes a large number of simulation modules.
  • the advantage here is that a large number of different data are taken into account in the process optimization.
  • the simulation environment device uses machine learning techniques and generates simulated synthetic data for machine learning.
  • the advantage here is that the data provided by the non-relational database provides correct relevance and knowledge of parameters for the simulation of the required device parameters.
  • the method according to the invention for continuous process optimization in the manufacture of semiconductor components includes storing a large amount of information using a non-relational database, capturing first information in the non-relational database using the data preparation device and capturing second information in the non-relational database using the simulation environment device.
  • the method further includes generating a plurality of pieces of output information using the simulation environment device and evaluating the plurality of pieces of output information using the simulation analysis device.
  • FIG. 1 shows a device for continuous process optimization in the production of semiconductor components
  • FIG. 2 shows a method for continuous process optimization in the production of semiconductor components.
  • FIG. 1 shows a device 100 for continuous process optimization in the production of semiconductor components.
  • the device 100 comprises a non-relational database 103, a data preparation device 104, a simulation environment device 105 and a simulation analysis device 106.
  • the non-relational database 103 comprises a big data database.
  • the non-relational database 103 stores measurement data from a large number of measurement instruments 101, as well as production data and context information.
  • the measurement data is, for example, production data, process control data, sensor measurement data and yield data.
  • virtual measurement data generated by a device 102 are stored in the non-relational database 103 .
  • the non-relational database 103 is connected to both the data preparation device 104 and the simulation environment device 105 .
  • the data preparation device 104 includes, for example, transformed fabrication data, assumptions of process data, and physical device data.
  • the simulation environment facility 105 performs, for example, device simulation, DOE simulation, product design kit validation, process window estimation, and synthetic training data generation for machine learning.
  • the simulation environment device 105 acquires data from the data preparation device 104, manufacturing plant information from a first memory 107, and information about the semiconductor components to be produced from a second memory 108.
  • the simulation environment device 105 generates a large amount of output information, which is acquired by the simulation analysis device 106. This data is evaluated by the simulation analysis device 106 and output to the simulation environment device 105 as an input parameter. Furthermore, the simulation analysis device outputs data to the non-relational database 103 .
  • the device 100 takes into account simulation environments and manufacturing data and acts as a digital twin for product manufacture.
  • the device 100 is calibrated with methods using the big data concept. All of the information from the non-relational database and the data processing device, which serves as model parameters for the simulation environment device, and the result data from the simulation environment device and the information from the multivariate simulation results serve as training data for the machine learning algorithms.
  • the production of the product is carried out in the company by the Production process flow where real process windows and simulated process windows are validated, process development and process optimization are supported, monitored.
  • the combination of the Big Data database, which includes current production data, among other things, with the simulation environment makes it possible to accelerate the optimization cycle in technology development and the process definition of new products.
  • the device 100 finds application in industrial plants, in particular in the manufacture of semiconductor components.
  • FIG. 2 shows the method 200 for continuous process optimization in the production of semiconductor components.
  • the method 200 starts with step 210, in which a large amount of information is stored in a non-relational database.
  • first information is recorded from the non-relational database.
  • second information is acquired using the simulation environment device.
  • a variety of output information is generated using the simulation environment device.
  • the multitude of pieces of initial information is evaluated with the aid of the simulation analysis device.
  • Method 200 is then ended or starts again with step 210.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Manufacturing & Machinery (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Hardware Design (AREA)
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Abstract

The invention relates to a device (100) for continuous process optimization in the production of semiconductor components, comprising a non-relational database (103) which has manufacturing data and virtual measuring data, a data preparation device (104) which provides information about process data assumptions and physical semiconductor component data, a simulation environment device (105) which comprises manufacturing facility information, semiconductor component information, semiconductor component model structures and process models, and a simulation analysis device (106), wherein the simulation environment device (105) generates a plurality of output information as a function of the manufacturing facility information, the semiconductor component information, the manufacturing data and virtual measuring data of the non-relational database (103) and outputs it to the simulation analysis device (106).

Description

Beschreibung description
Vorrichtung und Verfahren zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen Device and method for continuous process optimization in the manufacture of semiconductor components
Stand der Technik State of the art
Die Erfindung betrifft eine Vorrichtung und ein Verfahren zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen. The invention relates to a device and a method for continuous process optimization when manufacturing semiconductor components.
Bei der Entwicklung von Halbleiterprodukten und Halbleiterbauelementen werden Simulationen durchgeführt, die die Bauelementegeometrie und Produktionsprozesse im Hinblick auf Herstellungskosten, Spezifikationen der elektrischen Leistungsfähigkeit der Bauelemente und Ausfallsicherheit berücksichtigen. Die Simulationsmethoden unterstützen dabei Technologie- und Prozessentwicklung bei der Halbleiterherstellung, sodass die notwendigen technischen Produktspezifikationen sichergestellt werden. In the development of semiconductor products and semiconductor devices, simulations are carried out that take into account the device geometry and production processes with regard to manufacturing costs, specifications of the electrical performance of the devices and reliability. The simulation methods support technology and process development in semiconductor production, so that the necessary technical product specifications are ensured.
Die jeweiligen Bauelementmodelle werden in den Simulationen häufig als Einzelobjekte betrachtet. Somit sind die Simulationen auf einfache Schaltkreise begrenzt, bei der lediglich eine bestimmte Anzahl miteinander verbundener Bauelemente aufgrund von vorhandenen Rechenkapazitäten simuliert werden kann. The respective component models are often viewed as individual objects in the simulations. The simulations are therefore limited to simple circuits in which only a certain number of interconnected components can be simulated due to the available computing capacities.
Simulationsumgebungen berücksichtigen zusätzlich Schätzungen von Prozesseinstellungen, um den Produktentwicklungszyklus zu ermöglichen. Aufgrund des limitierten Zugangs zu derartigen Daten, weisen die Simulationsergebnisse eine begrenzte Genauigkeit auf. Die Annahmen des Prozessverhaltens und Daten von Fehleranalysen sind dabei häufig die einzigen Eingangsdaten. Prozessanpassungen können dabei vorab simuliert werden, sodass deren Einflüsse auf die herzustellenden Bauelemente abgeschätzt werden können. Diese können vor dem Produktionsstart berücksichtigt werden. Die vorhandenen Module und Prozessketten müssen exakt kalibriert werden, um geeignete Simulationsergebnisse bereitzustellen. Dabei werden Prozessannahmen und Designregeln angewandt. Bei der Verwendung von experimentellen Ergebnissen wird die Modellkalibrierung sukzessive angepasst. Simulation environments additionally consider estimates of process settings to enable the product development cycle. Due to the limited access to such data, the simulation results have limited accuracy. The assumptions of the process behavior and data from error analyzes are often the only input data. Process adjustments can be simulated in advance so that their influence on the components to be manufactured can be estimated. These can be taken into account before the start of production. The existing modules and process chains must be precisely calibrated in order to provide suitable simulation results. Process assumptions and design rules are applied. When using experimental results, the model calibration is successively adjusted.
Nachteilig ist hierbei, dass in den Simulationen und Simulationsumgebungen lediglich Prozesseigenschaften wie Ätzrate, Diffusionsprofile, Materialeigenschaften und Selektivität berücksichtigt werden. The disadvantage here is that only process properties such as etching rate, diffusion profiles, material properties and selectivity are taken into account in the simulations and simulation environments.
Die Aufgabe der Erfindung ist es, diese Nachteile zu überwinden. The object of the invention is to overcome these disadvantages.
Offenbarung der Erfindung Disclosure of Invention
Die Vorrichtung zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen umfasst eine nicht-relationale Datenbank, eine Datenaufbereitungseinrichtung, eine Simulationsumgebungsvorrichtung und eine Simulationsanalysevorrichtung. Die nicht-relationale Datenbank umfasst Fertigungsdaten und virtuelle Messdaten. Die Datenaufbereitungseinrichtung stellt Informationen über Prozessdatenannahmen und physikalische Halbleiterbauelementdaten bereit. Die Simulationsumgebungsvorrichtung umfasst Fertigungsanlageinformationen, Halbleiterbauelementinformationen, Halbleiterbauelementmodellstrukturen und Prozessmodelle. The device for continuous process optimization in the manufacture of semiconductor devices comprises a non-relational database, a data preparation device, a simulation environment device and a simulation analysis device. The non-relational database includes manufacturing data and virtual measurement data. The data conditioner provides information about process data assumptions and physical semiconductor device data. The simulation environment device includes manufacturing plant information, semiconductor device information, semiconductor device model structures, and process models.
Halbleiterbauelementmodellstrukturen werden durch zwei- oder dreidimensionale, geometrische Objekte und deren verwendeter Materialien beschrieben. Erfindungsgemäß erzeugt die Simulationsumgebungsvorrichtung eine Vielzahl von Ausgangsinformationen in Abhängigkeit der Fertigungsanlageninformationen, der Halbleiterbauelementinformationen und der Fertigungsdaten und der virtuellen Messdaten der nicht-relationalen Datenbank, wobei die Simulationsumgebungsvorrichtung die Vielzahl von Ausgangsinformationen an die Simulationsanalysevorrichtung ausgibt. Mit anderen Worten die Herstellungslinie weist einen digitalen Zwilling auf, sodass verschiedene Parameter während des Herstellungsprozesses bei der Prozessoptimierung berücksichtigt werden können. Das bedeutet aktuelle Fabrikationsdaten werden ständig mit den Simulationsvorhersagen abgeglichen. Der Vorteil ist hierbei, dass die Verbindung der Big- Data- Infrastruktur mit der Simulationsumgebung zur Verbesserung der Prozessmodellierung unter Berücksichtigung realer Fabrikationsdaten, dem Austausch von Prozessannnahmen und der Erhöhung der Datenqualität führt. Semiconductor component model structures are described by two- or three-dimensional geometric objects and their materials used. According to the invention, the simulation environment device generates a variety of output information depending on the manufacturing equipment information, the semiconductor device information and the manufacturing data and the virtual measurement data of the non-relational database, the simulation environment device outputs the variety of output information to the simulation analysis device. In other words, the manufacturing line has a digital twin, so various parameters during the manufacturing process are at the Process optimization can be taken into account. This means current production data are constantly compared with the simulation predictions. The advantage here is that the connection of the big data infrastructure with the simulation environment leads to an improvement in process modeling, taking into account real production data, the exchange of process assumptions and an increase in data quality.
In einer Weiterbildung umfassen die Vielzahl von Ausgangsinformationen Halbleiterbauelementsimulationsergebnisse, Prozessoptimierungsdaten, Designexperimentverifikationen, Prozessfensterschätzungen und Halbleiterbauelementvorhersagen. In a further development, the multitude of pieces of initial information include semiconductor device simulation results, process optimization data, design experiment verifications, process window estimates and semiconductor device predictions.
Vorteilhaft ist hierbei, dass die Prozessoptimierung sehr genau ist. The advantage here is that the process optimization is very precise.
In einer weiteren Ausgestaltung umfasst die Datenaufbereitungseinrichtung transformierte Fabrikdaten, wobei die Datenaufbereitungseinrichtung dazu eingerichtet ist, Prozessmodelle der Simulationsumgebungsvorrichtung zu kalibrieren. In a further refinement, the data preparation device comprises transformed factory data, with the data preparation device being set up to calibrate process models of the simulation environment device.
Der Vorteil ist hierbei, dass neue Parameter, direkt aus dem Produktionsprozess oder indirekt mit Hilfe von Algorithmen erstellt und mit Sensitivität zu Produkteigenschaften herangezogen werden können. So kann beispielhaft die Ätzrate mit Rezeptparametern und sensorischen Daten des Prozesses dargestellt werden und damit tieferliegende Informationen in der Simulation Anwendung finden. The advantage here is that new parameters can be created directly from the production process or indirectly with the help of algorithms and used with sensitivity to product properties. For example, the etching rate can be displayed with recipe parameters and sensory data of the process and thus deeper information can be used in the simulation.
In einer Weiterbildung umfasst die Simulationsumgebungsvorrichtung eine Vielzahl von Simulationsmodulen. In one development, the simulation environment device includes a large number of simulation modules.
Vorteilhaft ist hierbei, dass eine Vielzahl unterschiedlicher Daten bei der Prozessoptimierung berücksichtigt werden. The advantage here is that a large number of different data are taken into account in the process optimization.
In einer weiteren Ausgestaltung verwendet die Simulationsumgebungsvorrichtung maschinelle Lernverfahren und erzeugt simulierte synthetische Daten für das maschinelle Lernen. Der Vorteil ist hierbei, dass die von der nicht-relationalen Datenbank bereitgestellten Daten eine korrekte Relevanz und Wissen über Parameter für die Simulation der benötigten Bauelementparameter bereitstellen. In another embodiment, the simulation environment device uses machine learning techniques and generates simulated synthetic data for machine learning. The advantage here is that the data provided by the non-relational database provides correct relevance and knowledge of parameters for the simulation of the required device parameters.
Das erfindungsgemäße Verfahren zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen umfasst das Speichern einer Vielzahl von Informationen mit Hilfe einer nicht-relationalen Datenbank, das Erfassen erster Informationen der nicht-relationalen Datenbank mit Hilfe der Datenaufbereitungsvorrichtung und das Erfassen zweiter Informationen der nichtrelationalen Datenbank mit Hilfe der Simulationsumgebungsvorrichtung. Das Verfahren umfasst des Weiteren das Erzeugen einer Vielzahl von Ausgangsinformionen mit Hilfe der Simulationsumgebungsvorrichtung und das Auswerten der Vielzahl der Ausgangsinformationen mit Hilfe der Simulationsanalysevorrichtung. The method according to the invention for continuous process optimization in the manufacture of semiconductor components includes storing a large amount of information using a non-relational database, capturing first information in the non-relational database using the data preparation device and capturing second information in the non-relational database using the simulation environment device. The method further includes generating a plurality of pieces of output information using the simulation environment device and evaluating the plurality of pieces of output information using the simulation analysis device.
Weitere Vorteile ergeben sich aus der nachfolgenden Beschreibung von Ausführungsbeispielen bzw. den abhängigen Patentansprüchen. Further advantages result from the following description of exemplary embodiments and the dependent patent claims.
Kurze Beschreibung der Zeichnungen Brief description of the drawings
Die vorliegende Erfindung wird nachfolgend anhand bevorzugter Ausführungsformen und beigefügter Zeichnungen erläutert. Es zeigen: The present invention is explained below with reference to preferred embodiments and attached drawings. Show it:
Figur 1 eine Vorrichtung zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen, und FIG. 1 shows a device for continuous process optimization in the production of semiconductor components, and
Figur 2 ein Verfahren zur kontinuierlichen Prozessoptimierung bei der Herstellung von Halbleiterbauelementen. FIG. 2 shows a method for continuous process optimization in the production of semiconductor components.
Figur 1 zeigt eine Vorrichtung 100 zur kontinuierlichen Prozessoptimierung bei der Herstellung von Halbleiterbauelementen. Die Vorrichtung 100 umfasst eine nicht-relationale Datenbank 103, eine Datenaufbereitungsvorrichtung 104, eine Simulationsumgebungsvorrichtung 105 und eine Simulationsanalysevorrichtung 106. Die nicht-relationale Datenbank 103 umfasst eine Big-Data-Datenbank. In der nicht-relationalen Datenbank 103 sind Messdaten von einer Vielzahl an Messinstrumenten 101, sowie Herstellungsdaten und Kontextinformationen gespeichert. Bei den Messdaten handelt es sich beispielsweise um Fertigungsdaten, Prozesssteuerungsdaten, Sensormessdaten und Ertragsdaten. Zusätzlich sind in der nicht-relationalen Datenbank 103 virtuelle Messdaten gespeichert, die von einer Vorrichtung 102 erzeugt werden. Die nicht-relationale Datenbank 103 ist sowohl mit der Datenaufbereitungsvorrichtung 104 als auch mit der Simulationsumgebungsvorrichtung 105 verbunden. Die Datenaufbereitungsvorrichtung 104 umfasst beispielsweise transformierte Fabrikationsdaten, Annahmen von Prozessdaten und physikalische Bauelementdaten. Die Simulationsumgebungsvorrichtung 105 führt beispielsweise Bauelementsimulationen, Versuchsplanungssimulationen, Produkt-Design-Kit-Validierungen, Prozessfensterschätzungen und Erzeugung synthetischer Trainingsdaten für das Maschinelle Lernen durch. Die Simulationsumgebungsvorrichtung 105 erfasst dazu Daten aus der Datenaufbereitungsvorrichtung 104, Fertigungsanlageninformationen aus einem ersten Speicher 107, sowie Informationen über die zu erzeugenden Halbleiterbauelemente aus einem zweiten Speicher 108. Die Simulationsumgebungsvorrichtung 105 erzeugt eine Vielzahl von Ausgangsinformationen, die von der Simulationsanalysevorrichtung 106 erfasst werden. Diese Daten werden von der Simulationsanalysevorrichtung 106 ausgewertet und an die Simulationsumgebungsvorrichtung 105 als ein Eingangsparameter ausgegeben. Des Weiteren gibt die Simulationsanalysevorrichtung Daten an die nicht-relationale Datenbank 103 aus. FIG. 1 shows a device 100 for continuous process optimization in the production of semiconductor components. The device 100 comprises a non-relational database 103, a data preparation device 104, a simulation environment device 105 and a simulation analysis device 106. The non-relational database 103 comprises a big data database. In The non-relational database 103 stores measurement data from a large number of measurement instruments 101, as well as production data and context information. The measurement data is, for example, production data, process control data, sensor measurement data and yield data. In addition, virtual measurement data generated by a device 102 are stored in the non-relational database 103 . The non-relational database 103 is connected to both the data preparation device 104 and the simulation environment device 105 . The data preparation device 104 includes, for example, transformed fabrication data, assumptions of process data, and physical device data. The simulation environment facility 105 performs, for example, device simulation, DOE simulation, product design kit validation, process window estimation, and synthetic training data generation for machine learning. For this purpose, the simulation environment device 105 acquires data from the data preparation device 104, manufacturing plant information from a first memory 107, and information about the semiconductor components to be produced from a second memory 108. The simulation environment device 105 generates a large amount of output information, which is acquired by the simulation analysis device 106. This data is evaluated by the simulation analysis device 106 and output to the simulation environment device 105 as an input parameter. Furthermore, the simulation analysis device outputs data to the non-relational database 103 .
Die Vorrichtung 100 berücksichtigt dabei Simulationsumgebungen und Fabrikationsdaten und fungiert als digitaler Zwilling für die Produktherstellung. Die Vorrichtung 100 wird mit Methoden kalibriert, die das Big- Data- Konzept verwenden. Dabei dienen die Gesamtheit der Informationen aus der nichtrelationalen Datenbank und der Datenaufbereitungsvorrichtung, die der Simulationsumgebungsvorrichtung als Modellparameter dienen, sowie die Ergebnisdaten aus der Simulationsumgebungsvorrichtung und der Informationen der multivariaten Simulationsergebnisse als Trainingsdaten für die Algorithmen des Maschinellen Lernens. Die Produktherstellung wird im Betrieb durch den Produktionsprozessfluss, bei dem reale Prozessfenster und simulierte Prozessfenster validiert werden, Prozessentwicklung und Prozessoptimierung unterstützt werden, überwacht. Die Kombination der Big-Data-Datenbank, die unter anderem aktuelle Fabrikationsdaten umfasst, mit der Simulationsumgebung ermöglicht es somit den Optimierungszyklus in der Technologieentwicklung und die Prozessdefinition neuer Produkte zu beschleunigen. The device 100 takes into account simulation environments and manufacturing data and acts as a digital twin for product manufacture. The device 100 is calibrated with methods using the big data concept. All of the information from the non-relational database and the data processing device, which serves as model parameters for the simulation environment device, and the result data from the simulation environment device and the information from the multivariate simulation results serve as training data for the machine learning algorithms. The production of the product is carried out in the company by the Production process flow where real process windows and simulated process windows are validated, process development and process optimization are supported, monitored. The combination of the Big Data database, which includes current production data, among other things, with the simulation environment makes it possible to accelerate the optimization cycle in technology development and the process definition of new products.
Die Vorrichtung 100 findet Anwendung in Industrieanlagen, insbesondere bei der Herstellung von Halbleiterbauelementen. The device 100 finds application in industrial plants, in particular in the manufacture of semiconductor components.
Figur 2 zeigt das Verfahren 200 zur kontinuierlichen Prozessoptimierung bei der Herstellung von Halbleiterbauelementen. Das Verfahren 200 startet mit dem Schritt 210, in dem eine Vielzahl von Informationen in einer nicht-relationalen Datenbank gespeichert werden. In einem folgenden Schritt 220 werden erste Informationen aus der nicht-relationalen Datenbank erfasst. In einem folgenden Schritt 230 werden zweite Informationen mit Hilfe der Simulationsumgebungsvorrichtung erfasst. In einem folgenden Schritt 240 werden eine Vielzahl von Ausgangsinformationen mit Hilfe der Simulationsumgebungsvorrichtung erzeugt. In einem folgenden Schritt 250 wird die Vielzahl der Ausgangsinformationen mit Hilfe der Simulationsanalysevorrichtung ausgewertet. Anschließend wird das Verfahren 200 beendet oder startet erneut mit dem Schritt 210. FIG. 2 shows the method 200 for continuous process optimization in the production of semiconductor components. The method 200 starts with step 210, in which a large amount of information is stored in a non-relational database. In a following step 220, first information is recorded from the non-relational database. In a following step 230, second information is acquired using the simulation environment device. In a following step 240, a variety of output information is generated using the simulation environment device. In a subsequent step 250, the multitude of pieces of initial information is evaluated with the aid of the simulation analysis device. Method 200 is then ended or starts again with step 210.

Claims

- 7 - Ansprüche - 7 - Claims
1. Vorrichtung (100) zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen mit 1. Device (100) for continuous process optimization in the manufacture of semiconductor components
• einer nicht-relationalen Datenbank (103), die Fertigungsdaten und virtuelle Messdaten umfasst, • a non-relational database (103) that includes production data and virtual measurement data,
• einer Datenaufbereitungseinrichtung (104), die Informationen über Prozessdatenannahmen und physikalische Halbleiterbauelementdaten bereitstellt, • a data processing device (104) that provides information about process data assumptions and physical semiconductor device data,
• einer Simulationsumgebungsvorrichtung (105), die Fertigungsanlageninformationen, Halbleiterbauelementinformationen, Halbleiterbauelementmodellstrukturen und Prozessmodelle umfasst, und• a simulation environment device (105) comprising manufacturing tool information, semiconductor device information, semiconductor device model structures and process models, and
• einer Simulationsanalysevorrichtung (106), dadurch gekennzeichnet, dass die Simulationsumgebungsvorrichtung (105) eine Vielzahl von Ausgangsinformationen in Abhängigkeit der Fertigungsanlageninformationen, der Halbleiterbauelementinformationen und der Fertigungsdaten und virtuellen Messdaten der nicht-relationalen Datenbank (103) erzeugt und an die Simulationsanalysevorrichtung (106) ausgibt. • a simulation analysis device (106), characterized in that the simulation environment device (105) generates a variety of output information depending on the manufacturing plant information, the semiconductor device information and the manufacturing data and virtual measurement data of the non-relational database (103) and outputs it to the simulation analysis device (106). .
2. Vorrichtung (100) nach Anspruch 1, dadurch gekennzeichnet, dass die Vielzahl von Ausgangsinformationen Halbleiterbauelementsimulationsergebnisse, Prozessoptimierungsdaten, Designexperimentverifikationen, Prozessfensterschätzungen und Halbleiterbauelementvorhersagen umfassen. The apparatus (100) of claim 1, characterized in that the plurality of output information includes semiconductor device simulation results, process optimization data, design experiment verifications, process window estimates, and semiconductor device predictions.
3. Vorrichtung (100) nach einem der Ansprüche 1 oder 2, dadurch gekennzeichnet, dass die Datenaufbereitungseinrichtung (104) transformierte Fabrikdaten umfasst, wobei die Datenaufbereitungseinrichtung (104) dazu eingerichtet ist, Prozessmodelle der Simulationsumgebungsvorrichtung (105) zu kalibrieren. - 8 - 3. Device (100) according to one of claims 1 or 2, characterized in that the data preparation device (104) comprises transformed factory data, wherein the data preparation device (104) is set up to calibrate process models of the simulation environment device (105). - 8th -
4. Vorrichtung (100) nach einem der vorhergehenden Ansprüche, dadurch gekennzeichnet, dass die Simulationsumgebungsvorrichtung (105) eine Vielzahl von Simulationsmodulen umfasst. 4. Device (100) according to any one of the preceding claims, characterized in that the simulation environment device (105) comprises a plurality of simulation modules.
5. Vorrichtung (100) nach einem der vorhergehenden Ansprüche, dadurch gekennzeichnet, dass die Simulationsumgebungsvorrichtung (105) maschinelle Lernverfahren verwendet und simulierte synthetische Daten für maschinelles Lernen erzeugt. 5. Device (100) according to any one of the preceding claims, characterized in that the simulation environment device (105) uses machine learning methods and generates simulated synthetic data for machine learning.
6. Verfahren (200) zur kontinuierlichen Prozessoptimierung bei einer Herstellung von Halbleiterbauelementen mit den Schritten: 6. Method (200) for continuous process optimization in the manufacture of semiconductor components, having the steps:
• Speichern (210) einer Vielzahl von Informationen mit Hilfe einer nichtrelationalen Datenbank, • storing (210) a variety of information using a non-relational database,
• Erfassen (220) erster Informationen der nicht-relationalen Datenbank mit Hilfe der Datenaufbereitungsvorrichtung, • acquiring (220) first information from the non-relational database using the data preparation device,
• Erfassen (230) zweiter Informationen der nicht-relationalen Datenbank mit Hilfe der Simulationsumgebungsvorrichtung, • acquiring (230) second information of the non-relational database using the simulation environment device,
• Erzeugen (240) einer Vielzahl von Ausgangsinformationen mit Hilfe der Simulationsumgebungsvorrichtung, und • generating (240) a plurality of output information using the simulation environment device, and
• Auswerten (250) der Vielzahl der Ausgangsinformationen mit Hilfe der Simulationsanalysvorrichtung. • Evaluate (250) the plurality of pieces of initial information using the simulation analyzer.
PCT/EP2021/084542 2020-12-11 2021-12-07 Device and method for continuous process optimization in the production of semiconductor components WO2022122715A1 (en)

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