Method for operating and / or updating a cross-semiconductor-manufacturer database

Abstract

The present disclosure relates to a computer-implemented method for operating, in particular creating, preferably filling, and / or updating, a cross-semiconductor-manufacturer database for managing a supply chain of semiconductor-type elements, preferably semiconductor chips, in particular in the automotive industry, comprising: extracting, on the basis of at least one first data carrier which is associated with a semiconductor manufacturer database of a first computing device and is indicative of at least one first semiconductor-type element and one second data carrier which is associated with a semiconductor manufacturer database of a second computing device and is indicative of at least one second semiconductor-type element, first semiconductor data associated with the first semiconductor-type element and / or second semiconductor data associated with the second semiconductor-type element, and generating a first database entry on the basis of the first semiconductor data and / or generating a second database entry on the basis of the second semiconductor data, for the cross-semiconductor-manufacturer database on a third computing device.

Description

[0001] Secor Supply Chain Transparency GmbH

[0002] Method for operating and / or updating a semiconductor manufacturer-independent database

[0003] The present invention relates to a computer-implemented method for operating, in particular creating, preferably filling, and / or updating, a semiconductor manufacturer-independent database for managing a supply chain of semiconductor-like elements, preferably semiconductor chips, particularly in the automotive industry.

[0004] In the semiconductor industry, particularly in the automotive sector, supply chain management and coordination are crucial. The complexity of these supply chains has increased significantly in recent years, primarily due to the growing number of semiconductor components used in modern vehicles. Established supply chain management systems typically rely on manufacturer-specific databases or information management systems. While these approaches provide some overview of semiconductor component availability and / or specifications, they are limited by fragmentation and / or a lack of interoperability between different manufacturers and suppliers.

[0005] Managing a supply chain of semiconductor-like elements, preferably semiconductor chips, e.g. commodity chips, particularly in the automotive industry, presents several challenges.

[0006] Traditional methods for data management and updates in the semiconductor supply chain often rely on manual or semi-automated processes. These processes are not only time-consuming but also prone to errors, which can lead to delays and potential production outages. Furthermore, data in these systems is frequently stored in incompatible formats, complicating information aggregation and analysis. This variety of formats, and the associated need to manually extract and convert data, results in inefficiencies and increases the risk of information loss.

[0007] A particular problem is the lack of standardization in existing semiconductor chip datasheets. For example, there is a lack of standardization in how data about semiconductor components is collected and presented. Each company uses different formats, terms, and / or layouts for the properties associated with a semiconductor chip. These differences in layout, structure, terminology, and / or other attributes between data from different manufacturers significantly complicate the automated processing and analysis of this information. This leads to further fragmentation of the data landscape and hinders the effective use of available information for strategic decision-making and supply chain optimization.

[0008] In addition to the lack of standardization of datasheets, further problems arise.

[0009] One challenge lies in the heterogeneous data sources. Different semiconductor manufacturers store their data in different databases and / or systems, and use different formats and / or structures, which further complicates access to and / or integration of the data.

[0010] Another problem concerns the quality and consistency of the data. Due to its diverse origins, inconsistencies and / or errors can occur, which must be identified and / or corrected to ensure accurate and / or reliable information for a specific semiconductor chip. Furthermore, sourcing current and / or available semiconductor chips is extremely challenging. Current processes often rely on manual data entry, with this information typically only being shared between two participating companies. Other companies may not have access to this data, and even if access is granted, the information is often outdated. This makes it difficult to obtain an accurate overview of available semiconductor chips. There is even a risk that certain semiconductor chips are already out of stock, leading to unreliable inventory management.

[0011] The object of the present invention is to overcome the disadvantages of the known prior art and in particular to provide an improved computer-implemented method for operating, in particular creating, preferably filling, and / or updating, a semiconductor manufacturer-independent database for managing a supply chain of semiconductor-like elements, preferably semiconductor chips, particularly in the automotive industry.

[0012] The problem is solved by the features of the independent claims. The dependent claims describe preferred embodiments. Further aspects, advantages, and features become apparent from the dependent claims, the description, and the accompanying drawings.

[0013] One aspect of the invention relates to a computer-implemented method for operating, in particular creating, preferably populating, and / or updating, a semiconductor manufacturer-independent database for managing a supply chain of semiconductor-like elements, preferably semiconductor chips, particularly in the automotive industry, comprising:

[0014] Extracting, based on at least one first data carrier assigned to a semiconductor manufacturer database of a first computing device and indicative of at least one first semiconductor-like element, and a second data carrier assigned to a semiconductor manufacturer database of a second computing device and indicative of at least one second semiconductor-like element, first semiconductor data assigned to the first semiconductor-like element and / or second semiconductor data assigned to the second semiconductor-like element,

[0015] Generating a first database entry based on the first semiconductor data and / or a second database entry based on the second semiconductor data for the cross-semiconductor database on a third computing device.

[0016] One aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause it to execute the method according to the invention according to one or more of the aspects and / or embodiments described herein.

[0017] One aspect of the invention relates to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to execute the method according to the invention according to one or more of the aspects and / or embodiments described herein.

[0018] The invention relates to a computer-implemented method for operating, in particular creating, preferably populating, and / or updating, a semiconductor-manufacturer-independent database for managing a supply chain of semiconductor-like elements, preferably semiconductor chips, particularly in the automotive industry. A database, e.g., the semiconductor-manufacturer-independent database, can be understood as a collection of information, parameters, and / or data that is organized and / or structured and may be stored, for example, on a computing device, such as a computer or computer system, or on a data carrier, storage medium, or the like. The database enables the efficient management, retrieval, viewing, and / or updating of this information, parameters, and / or data. The database allows for the organization and / or management of large amounts of information.They are managed so that they can be easily retrieved and / or processed later.

[0019] A "cross-semiconductor database" can be understood as a database, or more specifically a central, information source, that is not limited to the products of a single semiconductor manufacturer, but can encompass products from multiple semiconductor manufacturers, e.g., from at least two. In other words, the products, e.g., semiconductors, and / or the associated information, data, and / or parameters from several different manufacturers can be consolidated, entered, combined, and / or managed in the cross-semiconductor database. The cross-semiconductor database can be understood as a unified platform for storing, managing, and / or organizing semiconductor-like elements or semiconductors, especially semiconductor chips, and / or the associated information, data, and / or parameters that may be relevant to their supply chain.The cross-semiconductor database can enable efficient management of the supply chain and / or the procurement of semiconductor components.

[0020] The term "semiconductor-like elements" can refer to i) the actual products themselves, e.g., chips comprising at least one semiconductor material, in particular semiconductor chips, preferably commodity chips, which can be used, for example, in computers, mobile phones, vehicles, industrial equipment, or the like, and / or ii) one or more components of these products, e.g., various components made of semiconductor materials and / or used in electronic circuits, such as transistors, diodes, resistors, capacitors, and / or other active or passive components. A semiconductor-like element can be a semiconductor chip or integrated circuit (IC), preferably a commodity chip, e.g., a standardized semiconductor chip that is frequently mass-produced and / or can be used in a wide variety of applications. Examples of semiconductor chips or ICs include...Commodity chips can be, for example, memory chips, processors, microcontrollers and / or other standard components that can be used in many different devices.

[0021] "Operating, in particular creating" the cross-semiconductor manufacturer database can be understood as building, creating, generating, establishing, setting up and / or compiling a database, for example based on information, data and / or parameters, from at least two, in particular different, semiconductor manufacturer databases.

[0022] The method according to the invention may include:

[0023] Extracting, based on at least one first data carrier assigned to a semiconductor manufacturer database of a first computing device and indicative of at least one first semiconductor-like element, and a second data carrier assigned to a semiconductor manufacturer database of a second computing device and indicative of at least one second semiconductor-like element, first semiconductor data assigned to the first semiconductor-like element and / or second semiconductor data assigned to the second semiconductor-like element.

[0024] "Extracting semiconductor data" can be understood as the retrieval, acquisition, transfer, use, and / or utilization of "semiconductor data," which is the information, data, and / or parameters associated with a semiconductor-like element. Extracting semiconductor data from various data carriers or semiconductor manufacturer databases enables the targeted collection of specific information about semiconductor elements, which is crucial for supply chain management. This can increase supply chain efficiency, enhance transparency, and improve responsiveness to market or production changes. Database creation and updates are essential for addressing specific challenges, such as data inconsistency and accessibility issues, that can arise in the semiconductor product supply chain. Data extraction from this specific source...These sources ensure that the information comes directly from the manufacturers, which increases the accuracy and reliability of the data.

[0025] The "semiconductor data" can include multiple attributes assigned to the corresponding semiconductor-like element and / or its manufacturer. The semiconductor data, or the multiple attributes, can, for example, include product information of the semiconductor-like element and / or parameters, particularly physical parameters, of the semiconductor-like element.

[0026] The product information may include one or more of the following: an identifier, in particular an order number, of a product; a manufacturer of the product; a category such as electronics; a subcategory, such as semiconductors; web addresses, in particular URL(s) for a datasheet and / or technical documentation of the product; designation of a housing and / or packaging of the product; arrangement and / or assignment of connections and / or pins on the product, in particular pinout; indication of whether the product is lead- and / or halogen-free; directives of the European Union that lay down restrictions on the use of certain hazardous substances in electrical and electronic equipment, preferably "Restriction of Hazardous Substances" (RoHS); classification indicating whether the product is suitable for use in the automotive industry (automotive grade) or in industrial applications (industrial grade), in particular automotive and / or industrial grade;Product pricing information, including list price, discounts, quantity discounts; manufacturing locations; product group categories (e.g., material groups); compliance levels with legal requirements; value chain stages, or similar information.

[0027] The physical parameters can be, for example, electrical parameters. These physical or electrical parameters can include one or more of the following: voltage; current; capacitance; resistance; temperature range; power classes; application areas / industry classes; storage conditions; or the like.

[0028] A "data carrier" can be understood as a carrier, container, and / or a medium, particularly physical or virtual, that is configured to maintain, receive, and / or store data, information, and / or parameters, especially in digital form. The data carrier can be provided remotely, e.g., remotely from the third computing device. The data carrier can, for example, be a datasheet, in particular a technical datasheet, that is indicative of at least one semiconductor-like element, for example, semiconductor data associated with the semiconductor-like element. The data carrier can maintain, store, organize, divide, and / or structure the semiconductor data associated with the semiconductor-like element in a specific format or output format. According to the invention, at least one first and one second data carrier can be accessed.The semiconductor data is extracted from a first and second data carrier. The first and / or second data carrier can be a datasheet, particularly a technical datasheet. The format of the first and / or second data carrier can be referred to as the output format, for example, if it is located in the semiconductor manufacturer's database of the first or second computing device. The output format can, for example, refer to a specific document type.

[0029] The first and / or second data carrier could, for example, be a Portable Document Format (PDF)-based datasheet. PDF-based formats are widely used file formats for documents that can include text, graphics, and / or other multimedia elements; they are widely used data formats and are common in business, allowing access to large amounts of data. Extracting this non-machine-readable data is difficult and time-consuming. It requires, for example, extensive regular expressions (e.g., a type of pattern matching) to identify and select the desired data. The first and / or second data carrier could be a Comma-Separated Value (CSV)-based datasheet. CSV is a simple file format that stores tabular data in text form, with values ​​separated by, for example, one or more commas, semicolons, spaces, and / or tabs.CSV files are easy to read and parse because they have a clear table structure. They are well-suited for storing structured data such as semiconductor attributes, as they can be easily imported into databases or spreadsheet programs. The first and / or second data carrier can be an Extensible Markup Language (XML)-based datasheet. XML is an extensible markup language used to represent hierarchical structures and semantic data. XML offers a flexible way to store structured data because it supports user-defined tags and hierarchies. This facilitates the extraction of specific data fields from complex XML documents. The first and / or second data carrier can be a JavaScript Object Notation (JSON)-based datasheet.JSON is a lightweight data exchange format that is machine- and / or human-readable and stores data in the form of key-value pairs. JSON is easy to understand and parse, making it ideal for transmitting and storing structured data. It is often used in web applications and supports complex data structures, making it effective for storing semiconductor data. The first and / or second data carrier can be an HTML (Hypertext Markup Language)-based datasheet stored as a web page. The typically unstructured data is only machine-readable through pattern recognition. Extracting the data is resource-intensive and requires, for example, extensive regular expressions to recognize and select the desired data. The first and / or second data carrier can alternatively or additionally be based on image data.The image data comprises one or more images, in particular one, preferably high-resolution, image or photograph, e.g., of a printed circuit board or a semiconductor. An artificial intelligence (AI) application, AI system, or AI model (e.g., one / s specialized in the detection of components in images or based on image data, in particular an AI application, AI system, or AI model based on a convolutional neural network (CNN, or a region-based or R-CNN) and / or transformer-based AI applications, AI systems / models, or applications / systems / models based on a transformer architecture, whereby hybrid approaches would also be conceivable) can generate output based on the image data or the one or more images, which can be stored in the semiconductor-manufacturer-independent database described herein. For example, individual (e.g.,One or more components or elements on a printed circuit board or a single semiconductor are identified and / or assigned and stored, saved, and / or entered into the semiconductor manufacturer-independent database described herein as an assembly, component, and / or element. This process can be referred to as "Scan to database" and / or "Motherboard Chip Breakdown".

[0030] The first and / or second data carrier can be an interface to another company, particularly a standardized one. For example, data, especially concerning parts, components, elements, assemblies, etc., can be exchanged, particularly using standards and / or rule sets such as Catena-X, Enterprise Resource Planning (ERP), IT programs (e.g., SAP), or similar systems. This exchange can be used, for example, in the event of bottlenecks (Catena-X PURIS...), and / or demand and / or capacity balancing ("BKA," e.g., Catena-X DCM - "Demand Capacity Management") can be carried out, e.g., electronically and / or automatically, preferably "at the push of a button."

[0031] The first and / or second data carrier can be a kind of exchange that gathers offers and / or bids for semiconductors, e.g. through commodity exchange (semiconductors for semiconductors) and / or through prices, such as on a commodity futures exchange.

[0032] The first data carrier can be assigned to a first computing device, such as a computer. For example, the first data carrier can be assigned to a semiconductor manufacturer's database on the first computing device, meaning it can be stored there, for example, in a format specified and / or preferred by the semiconductor manufacturer. The second data carrier can be assigned to a second computing device, such as a computer. For example, the second data carrier can be assigned to a (second) semiconductor manufacturer's database on the second computing device, meaning it can be stored there, for example, in a format specified and / or preferred by the semiconductor manufacturer. The first computing device can differ from the second computing device, for example, in location, users, and / or network infrastructure; for example, they could be computing devices from different companies.The cross-vendor semiconductor database can, in turn, be maintained, created, built, and / or stored on a third computing device. This third computing device can differ from the first and / or second computing devices, for example, in location, user, and / or network infrastructure.

[0033] Extracting the first and / or second semiconductor data (especially the first and / or second data carrier) can include:

[0034] Capturing and / or converting an output format from the first and / or second data carrier into a pattern-searchable, text-based format. Extracting the first and / or second semiconductor data (especially from the first and / or second data carrier) may include:

[0035] Extracting the first semiconductor data from the pattern-searchable text-based format of the first data carrier and / or

[0036] Extracting the second semiconductor data from the pattern-searchable text-based format of the second data carrier.

[0037] Extracting the first and / or second semiconductor data (especially from the first and / or second data carrier) is performed using an AI system or is AI-based. This extraction can be based on image data and / or one or more images that are indicative of at least one component and / or at least one element of a semiconductor-like element (e.g., a first and / or second semiconductor-like element). Non-restrictive, but optional, example formats for image data or one or more images include formats such as PDF, JPEG (Joint Photographic Experts Group), PNG (Portable Network Graphics), RAW (raw data or unprocessed image data, e.g., from camera(s) or image sensor(s)), and / or TI FF (Tagged Image File Format), etc. In this case, for example, the (first / second, etc.) database entry can be extracted using the AI ​​system.The images can be generated using AI-based methods, for example, via an output of the AI ​​system and / or by means of the AI ​​system or AI-based methods, and then categorized, entered, stored, and / or inserted into the cross-manufacturer semiconductor database on the third computing device, whereby, for example, the image data or one or more images can be provided to the AI ​​system as input. The AI ​​system can be pre-trained accordingly, and image preparation or preprocessing measures of the image data can also be carried out.

[0038] For example, a data carrier, such as a PDF or CSV file, can be converted, transformed, translated, and / or converted into a text, particularly a machine-readable one, which then allows searching for specific patterns and / or keywords. This capture and / or conversion can be automated, saving time and / or resources. Using pattern matching, large amounts of data can be efficiently searched and / or relevant information extracted without requiring manual review or data standardization, regardless of its original format. This further facilitates data processing and / or analysis, while also reducing storage requirements. Extracting or converting semiconductor data into a searchable, text-based format simplifies the search for specific information.This allows users to search specifically for relevant data and quickly access the information they need.

[0039] The first set of semiconductor data may have a manufacturer-specific format that differs from the manufacturer-specific format of the second set of semiconductor data. A "manufacturer-specific format" can refer to how the semiconductor data is organized, structured, arranged, presented, and / or named within the data carrier, e.g., within the document (e.g., PDF, CSV, XML, JSON, HTML, etc.). These differences may be due to specific characteristics and / or requirements of the manufacturers, who have their own standards and / or preferences.

[0040] The manufacturer-specific format of the first semiconductor data may differ from the manufacturer-specific format of the second semiconductor data by one or more of the following: a layout, structure, arrangement, position, terminology, measurement, unit, notation, identifier, formatting, and / or a designation of attributes of the first semiconductor-like element and / or its manufacturer, and / or of the second semiconductor-like element and / or its manufacturer. Considering manufacturer-specific formats can enable efficient and / or consistent processing and / or analysis of different semiconductor data.Furthermore, taking into account manufacturer-specific formats can enable better adaptation to the needs of individual manufacturers and / or contribute to improving the quality, accuracy and / or efficiency of semiconductor data processing.

[0041] Extracting the first and second semiconductor data can involve processing the first and second data carriers using pattern recognition and / or regular expressions. This enables the efficient aggregation, harmonization, and management of data from various sources with differing specifications within a single semiconductor vendor database. The challenge arising from data heterogeneity is addressed by implementing algorithms and processing logics capable of recognizing, interpreting, and / or converting or adapting these differences. This ensures that the integrated data, despite its initial disparities, is in a uniform format, enabling consistent, reliable, and / or effective use of the database for supply chain management.By taking into account these diverse differences in data formats and structures, the method helps to improve interoperability between the systems of different semiconductor manufacturers and thus promote a smooth flow of information within the supply chain, which is of particular importance in the automotive industry, where the reliability and timeliness of information on the availability of semiconductor chips can be crucial.

[0042] Extracting semiconductor data by processing data carriers using pattern recognition and / or regular expressions can be an automated process. This involves searching the data carriers for predefined patterns, structures, and / or expressions to identify, capture, recognize, and / or extract relevant information. This process can include several steps, such as: i) preparing the data carriers, for example, as described above, by capturing and / or converting the original data carrier format into a text-based format searchable by pattern recognition; and ii) defining search patterns and / or extracting the relevant data. Specific patterns, structures, and / or expressions can be defined to search the data carriers for. These can be keywords, phrases, formatting, and / or other characteristic features of the semiconductor data to be extracted.This can be achieved, for example, using the programming language PERL ("Practical Extraction and Reporting Language"). PERL is characterized by its flexibility and versatility. It offers a rich collection of built-in functions and libraries that allow developers to accomplish complex tasks with relatively little code. A distinctive feature of PERL is its strong support for regular expressions. These enable developers to perform complex search and replace operations on text, making PERL a powerful language for text manipulation.

[0043] Extracting semiconductor data by processing data carriers using pattern recognition and / or regular expressions enables efficient, precise, and / or scalable processing of semiconductor information, which in turn improves the efficiency and quality of data analysis. By using pattern recognition and regular expressions, efficient and precise extraction of semiconductor data from various formats, such as the input formats or data types described herein (PDF, CSV, XML, JSON, HTML, etc.) and / or various manufacturer-specific formats, such as structures, can be achieved. The use of pattern recognition and regular expressions enables automated semiconductor data extraction, saving time and effort.By using defined search patterns and algorithms, precise extraction of semiconductor data can be achieved, minimizing errors and / or improving the accuracy of the information. The extraction process can be applied to large datasets and / or a variety of storage media, providing a scalable solution for processing semiconductor information. Defining search patterns allows for adaptation to different data formats and structures, further increasing the flexibility and versatility of the extraction process.

[0044] Below is an example code fragment, specifically for parsing an "RDSon" value, e.g. from a PDF:

[0045] The use of regular expressions is a flexible method for text analysis and / or extraction. It not only improves the accuracy of identifying relevant data by reducing errors that can occur with manual or less specific extraction methods, but also increases the efficiency of the entire process. Automating the extraction process using pattern recognition and regular expressions significantly reduces the time required to collect and process data. This leads to faster updates of the cross-vendor semiconductor database and enables timely adaptation of the supply chain to changing market conditions and / or requirements.Overall, these new features bring a significant improvement in data processing efficiency and accuracy, which is crucial for managing complex supply chains in highly dynamic industries such as the automotive industry.

[0046] The method according to the invention may include:

[0047] Access, using the third computing device, to the semiconductor manufacturer database of the first computing device and / or the semiconductor manufacturer database of the second computing device. In particular, the data carriers (or the semiconductor data contained therein, which are assigned to the corresponding semiconductor-like elements) of the respective semiconductor manufacturer databases can be accessed using the third computing device, for example, the first data carrier of the semiconductor manufacturer database of the first computing device and / or the second data carrier of the semiconductor manufacturer database of the second computing device.

[0048] Access can be achieved via network protocols and data transmission technologies, specifically a network connection, preferably an internet connection. This means that the third computing device has the ability to connect to the semiconductor manufacturers' databases, for example, via the internet, and access the necessary data storage media. Alternatively or additionally, access via the network connection, preferably the internet connection, can occur at regular intervals, particularly daily, weekly, and / or monthly. In other words, the third computing device can essentially automatically and / or periodically retrieve the semiconductor manufacturers' databases to stay up-to-date and obtain current information.By defining specific update intervals, a balance is struck between the need to keep the database up-to-date and the effort associated with data collection and processing. This structured and regular update method helps increase the efficiency of the process by minimizing redundant data queries while ensuring that the database provides an accurate overview of the supply chain situation. This also enables efficient and timely access to semiconductor manufacturers' databases, allowing critical information about semiconductor products or components to be retrieved and / or updated. The use of a network connection and automatic updates at regular intervals facilitate the management and maintenance of the databases within the context of supply chain management, particularly in the automotive industry.These mechanisms help ensure that the database remains up-to-date, accurate, and comprehensive, which is crucial for managing the supply chain of semiconductor-like elements.

[0049] The method according to the invention may include:

[0050] Storing the first data carrier and / or the second data carrier on the third computing device. Alternatively or additionally, the semiconductor data contained in the first and / or second data carrier, which are assigned to the corresponding semiconductor-like elements, can also be stored. For example, the procedure can include: storing the first semiconductor data assigned to the first semiconductor-like element and / or the second semiconductor data assigned to the second semiconductor-like element on the third computing device. This provides an efficient and effective method for integrating and managing data from different sources.

[0051] The method according to the invention may include:

[0052] Generating a first database entry based on the first semiconductor data and / or a second database entry based on the second semiconductor data for the cross-semiconductor database on a third computing device.

[0053] A database entry can essentially be considered a data record or a delimited unit of data used to store relevant information about a specific object, such as specific information about a semiconductor element and / or a component thereof, and / or a manufacturer of the semiconductor element or component. This entry can comprise one or more fields describing various attributes, characteristics, and / or properties of the element in question. A database entry can be viewed as a single row in a database table, with each column of the table corresponding to a specific attribute of the entry. A database entry can also comprise multiple rows, containing, for example, information associated with a semiconductor element. The central storage of the data or data entries, e.g.,on the third computing device, facilitates access and / or analysis, enabling effective management of the supply chain of semiconductor-like elements.

[0054] The method according to the invention may include: structuring and / or normalizing the first semiconductor data to generate the first database entry, and / or

[0055] Structuring and / or normalizing the second semiconductor data to generate the second database entry, such that the first database entry and / or the second database entry can be verified, in particular searched, in the semiconductor manufacturer-independent database.

[0056] "Structuring and / or normalizing" the initial semiconductor data can mean bringing the source data, or raw data, such as semiconductor data, into an organized and / or standardized form. This involves, for example, creating a consistent structure suitable for storage in a database and enabling efficient access. "Structuring" can refer to splitting, categorizing, arranging, and / or ordering the raw data, for example, into specific fields and / or attributes. "Normalizing" the data can refer to adapting the data to specific standards, units, and / or conventions to ensure compatibility with other data in the database. These standards may be defined by the cross-vendor semiconductor database. This increases the efficiency of managing and querying the data in the database and improves the quality of the stored information.

[0057] Structuring and / or normalizing semiconductor data to generate database entries, and / or ensuring vendor-independent verifiability, particularly searchability, of these entries in the central, cross-vendor semiconductor database, significantly improves data consistency, interoperability, and usability. Structuring and normalizing the initial and secondary semiconductor data before generating the corresponding database entries ensures that the data is in a uniform format, greatly facilitating the aggregation, analysis, and comparison of data from diverse sources. This is especially important in environments where data may originate from various semiconductor manufacturer databases, potentially using different formats and standards for data storage and transmission.The vendor-independent verifiability and, in particular, the searchability of database entries in the cross-vendor semiconductor database enables users to quickly and efficiently access specific information, regardless of the data's origin. This is crucial for managing the supply chain of semiconductor components, especially in the automotive industry, where the ability to quickly access accurate and up-to-date information on semiconductor chip availability and specifications directly impacts production planning and flexibility. Overall, these new features offer a robust solution to the challenges associated with managing a complex and dynamic supply chain by simplifying and optimizing data integration and utilization across vendor boundaries.

[0058] Structuring the method according to the invention may include:

[0059] Extracting, from the initial semiconductor data, a first plurality of attributes that are assigned to the first semiconductor-like element and / or a manufacturer thereof. Alternatively or additionally, the structuring of the method according to the invention can comprise:

[0060] Extracting, from the second semiconductor data, a second plurality of attributes that are assigned to the second semiconductor-like element and / or a manufacturer thereof. The structuring of the method according to the invention may include:

[0061] Organizing, in particular splitting and / or arranging, the first plurality of attributes in the first database entry and / or the second plurality of attributes in the second database entry.

[0062] These organizational mechanisms enable an efficient and clear structure and / or storage of data in the cross-semiconductor database, which significantly improves query efficiency and / or data management within the database. Targeted data extraction and structuring ensures data consistency within the database, which in turn increases data reliability for end users, particularly in the automotive industry.

[0063] According to the invention, the first database entry and / or the second database entry can be subdivided into at least two data categories. These at least two data categories can include:

[0064] - Header data, which includes product information of the first and / or second semiconductor-like element, and

[0065] - Detailed data including physical parameters of the first and / or second semiconductor-like element.

[0066] This subdivision can enable differentiated and / or efficient data processing and analysis. The introduction of header data as one of the data categories, encompassing product information of the first and / or second semiconductor element, allows for the rapid identification and / or assignment of the corresponding semiconductor elements. This is particularly important in the automotive industry, where specific product information such as type, model, or manufacturing date is crucial. The inclusion of detailed data as another data category, encompassing physical parameters of the first and / or second semiconductor element, enables in-depth technical analysis and evaluation of the semiconductor chips. This can be decisive for selecting specific semiconductor chips for particular applications, such as in the automotive industry.This brings significant advantages for the management of the supply chain of semiconductor-like elements by offering improved data organization, accessibility and analysis, which can lead to optimized decision-making, higher efficiency and ultimately increased competitiveness, e.g. in the automotive industry.

[0067] The method according to the invention may include:

[0068] Splitting the header data and the detail data across multiple, especially different, database tables of the semiconductor manufacturer-independent database on a third computing device.

[0069] This division enables more efficient and structured data storage and processing by clearly separating general information (header data) from specific details (detail data) of the semiconductor components. Distributing the data across multiple database tables improves data integrity, reduces data access time, and increases system scalability. This provides significant advantages for semiconductor component supply chain management by offering a more robust, flexible, and efficient database structure specifically tailored to the needs of the semiconductor industry, and particularly the automotive industry.Improved data structuring and management enables companies to react more quickly to changes in the supply chain, better predict the availability of semiconductor chips, and overall increase the efficiency of their supply chain processes.

[0070] The method according to the invention may include:

[0071] Saving the first database entry and / or the second database entry to the semiconductor manufacturer-independent database.

[0072] Storing database entries in the cross-semiconductor database enables a centralized and / or consolidated view of supply chain information, providing improved traceability, analytical capabilities, and / or forecasting accuracy for the availability of semiconductor-like components across different manufacturers. This leads to optimized supply chain management, reduced production downtime, and improved decision-making, for example, in the automotive industry and beyond. It provides an efficient aggregation and / or management of critical supply chain information from various sources in a single, cross-semiconductor platform or database, enabling comprehensive visibility and control over the availability of semiconductor components, which is crucial in the highly complex and / or interdependent world of semiconductor supply chains.

[0073] One aspect of the invention relates to a computing device, in particular a computer, comprising: a memory on which a semiconductor manufacturer-independent database created according to the method according to one or more embodiments and / or aspects is maintained or stored, and a processor which is configured to execute the method according to one or more embodiments and / or aspects and / or to operate the semiconductor manufacturer-independent database, in particular to create, preferably to populate, and / or to update it.

[0074] The computing device may include a network interface for communicative coupling with at least one other computing device. The network interface may be configured to receive, in particular receive, at least one data carrier that is assigned to a semiconductor manufacturer database of the at least one other computing device and is indicative of at least one semiconductor-like element.

[0075] The computing device on which the cross-semiconductor database is maintained can be communicatively connected to at least one other computing device, e.g. the first computing device on which a first semiconductor manufacturer database is maintained and / or the second computing device on which a second semiconductor manufacturer database is maintained.

[0076] BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Exemplary embodiments of the invention are now described with reference to the accompanying drawings. To ensure a detailed understanding of the features of the present disclosure mentioned above, a more detailed description of the disclosure, which was briefly summarized above, can be obtained by referring to exemplary embodiments. The accompanying drawings relate to embodiments of the disclosure and are described below:

[0078] Fig. 1 shows a cross-semiconductor database that is connected, in particular communicatively, to several semiconductor manufacturer databases and is operated, in particular created and / or populated, and / or updated, according to the method according to the invention.

[0079] Fig. 2a, b shows exemplary semiconductor data of a semiconductor-like element of an exemplary data carrier, for example before processing;

[0080] Fig. 3 shows exemplary database entries of the semiconductor manufacturer-independent database from Fig. 1, for example after processing, in particular after processing the semiconductor data from Fig. 2a, b.

[0081] DESCRIPTION OF PREFERRED EXECUTION FORMS

[0082] The invention will now be explained in more detail with reference to embodiments shown in the drawings, wherein in all drawings essentially functionally identical elements have the same reference numerals.

[0083] The drawings are schematic and not to scale. Some elements in the drawings may have exaggerated dimensions to emphasize aspects of the present disclosure and / or for greater clarity of presentation. For the sake of simplicity, identical reference numerals are used to identify identical elements that are common to all drawings. It is intended that elements and features of one embodiment may be advantageously incorporated into other embodiments without further mention. In general, only the differences between individual embodiments are described.

[0084] Each embodiment serves to illustrate the disclosure and should not be understood as a limitation of the disclosure. Furthermore, features shown or described as part of one embodiment can be used in conjunction with other embodiments to create a further embodiment. It is intended that the description includes such modifications and variations. Fig. 1 shows a semiconductor manufacturer-independent database 100, which is connected, in particular communicatively, to several semiconductor manufacturer databases 102A-D (see network connection 104 or internet connection 104) and is operated, in particular created and / or populated, and / or updated, according to the method of the invention.

[0085] The cross-semiconductor database 100 can be a database, or in particular a central information source or unified platform 100, for storing, managing, and / or organizing semiconductor-like elements or semiconductors, especially semiconductor chips, and / or associated information, data, and / or parameters that may be relevant to their supply chain. The cross-semiconductor database 100 can enable efficient supply chain management and / or the procurement of semiconductor components.

[0086] Figure 1 shows several computing devices 108A-D, each comprising at least one data carrier 106A-D. Figure 1 shows a total of four computing devices 108A-D, although fewer, e.g., at least two, or more, e.g., five, six, seven, or more, can also be used. A total of four data carriers 106A-D are also shown, with each computing device potentially comprising more than one data carrier.

[0087] The data carrier 106A-D can, for example, be a datasheet, in particular a technical datasheet, that is indicative of at least one semiconductor-like element, for example, comprising semiconductor data associated with the semiconductor-like element. The data carrier 106A-D can maintain, store, organize, divide, and / or structure the semiconductor data associated with the semiconductor-like element in a specific format or output format.

[0088] According to the invention, at least one first and one second data carrier 106A-D can be accessed, or the semiconductor data can be extracted from a first and second data carrier 106A-D. The first data carrier and / or the second data carrier 106A-D can be a datasheet, in particular a technical datasheet. The format of the first and / or second data carrier 106A-D can be an output format, for example, if it is located on the semiconductor manufacturer database 102A-D of the first or second computing device 108A-D. The output format can, for example, refer to a specific document type. The first and / or second data carrier 106A-D can, for example, be a Portable Document Format (PDF)-based datasheet.PDF-based formats are widely used file formats for documents that can include text, graphics, and / or other multimedia elements; they are also widely used data formats, frequently employed by businesses, allowing access to large amounts of data. Extracting this non-machine-readable data is difficult and time-consuming. It requires, for example, extensive regular expressions (a type of pattern matching) to identify and select the desired data. The first and / or second disk, 106A-D, may contain a comma-separated value (CSV)-based spreadsheet. CSV is a simple file format that stores tabular data in text form, with values ​​separated by commas, semicolons, spaces, and / or tabs, for example. CSV files are easy to read and parse because of their clear tabular structure.They are well-suited for storing structured data such as semiconductor attributes because they can be easily imported into databases or spreadsheet programs. The first and / or second data carrier 106A-D can be an Extensible Markup Language (XML)-based datasheet. XML is an extensible markup language used to represent hierarchical structures and semantic data. XML offers a flexible way to store structured data because it supports user-defined tags and hierarchies. This facilitates the extraction of specific data fields from complex XML documents. The first and / or second data carrier 106A-D can be a JavaScript Object Notation (JSON)-based datasheet. JSON is a lightweight, human-readable data interchange format that stores data in the form of key-value pairs.JSON is easy to understand and parse, making it ideal for transmitting and storing structured data. It is frequently used in web applications and supports complex data structures, making it effective for storing semiconductor data. The first and / or second data carrier can be an HTML (Hypertext Markup Language)-based datasheet stored as a web page. The typically unstructured data is only machine-readable through pattern recognition. Extracting the data is computationally intensive and typically requires extensive regular expressions to recognize and select the desired data.

[0089] The first data carrier, e.g., 106A, can be assigned to a first computing device, e.g., a computer, e.g., 108A. For example, the first data carrier, 106A, can be assigned to a semiconductor manufacturer's database, 102A, of the first computing device, 108A, meaning it can be stored therein, for example, in a format specified and / or preferred by the semiconductor manufacturer. The second data carrier, e.g., 106B, can be assigned to a second computing device, e.g., a computer, e.g., 108A. For example, the second data carrier, 106B, can be assigned to a (second) semiconductor manufacturer's database, 102B, of the second computing device, 108B, meaning it can be stored therein, for example, in a format specified and / or preferred by the semiconductor manufacturer. The first computing device, 108A, can differ from the second computing device, 108B, e.g.,The data may differ from the first and / or second data processing devices (108A-B, e.g., also 108C-D) due to location, users, and / or network infrastructure; for example, these may be computing devices from different companies. The semiconductor-manufacturer-independent database 100 can, in turn, be maintained, created, built, and / or stored on a third computing device 109. The third computing device 109 may differ from the first and / or the second computing device (108A-B, e.g., also 108C-D) due to location, users, and / or network infrastructure.

[0090] The method according to the invention may include:

[0091] Extracting, based on at least one first data carrier (106A-D) that is assigned to a semiconductor manufacturer database 102A-D of a first computing device (108A-D) and is indicative of at least one first semiconductor-like element, and a second data carrier (106A-D) that is assigned to a semiconductor manufacturer database 102A-D of a second computing device (108A-D) and is indicative of at least one second semiconductor-like element, of first semiconductor data that is assigned to the first semiconductor-like element and / or of second semiconductor data that is assigned to the second semiconductor-like element.

[0092] Extracting semiconductor data from various storage media and semiconductor manufacturer databases enables the targeted collection of specific information about semiconductor components, which is crucial for supply chain management. This can increase supply chain efficiency, enhance transparency, and improve responsiveness to market or production changes. Database creation and updates are essential for addressing specific challenges, such as data inconsistency and accessibility issues, that can arise in the semiconductor supply chain. Extracting data from this specific source(s) ensures that the information originates directly from the manufacturers, thus increasing data accuracy and reliability.

[0093] Figures 2a and 2b show exemplary semiconductor data for a semiconductor-like element, e.g., an AUIRF 1324 WL WIDELEAD HEXFET® Power MOSFET (metal-oxide-semiconductor field-effect transistor, e.g., from Infineon), of an exemplary data carrier, such as a PDF, particularly before processing. The complete exemplary datasheet containing the exemplary semiconductor data of the semiconductor-like element in Figures 2a and 2b can be viewed at https: / / www.infineon.com / dgdl / auirf1324wl.pdf?fileld=5546d462533600a4015355a8c07c1370. "All" may refer to automotive applications. "IRF1324" may correspond to a model number. As shown in Figures 2a and 2b, the semiconductor data may include multiple attributes associated with the corresponding semiconductor-like element and / or its manufacturer. The semiconductor data orThe majority of the attributes may, for example, include product information of the semiconductor-like element and / or parameters, in particular physical parameters, of the semiconductor-like element, as shown in Fig. 2a, b.

[0094] The product information may include one or more of the following: an identifier, in particular an order number, of a product; a manufacturer of the product; a category such as electronics; a subcategory, such as semiconductors; web addresses, in particular URL(s) for a datasheet and / or technical documentation of the product; designation of a housing and / or packaging of the product; arrangement and / or assignment of connections and / or pins on the product, in particular pinout; indication of whether the product is lead- and / or halogen-free; directives of the European Union that lay down restrictions on the use of certain hazardous substances in electrical and electronic equipment, preferably "Restriction of Hazardous Substances" (RoHS); classification indicating whether the product is suitable for use in the automotive industry (automotive grade) or in industrial applications (industrial grade), in particular automotive and / or industrial grade;Pricing information for the product, including list price, discounts, quantity discounts, places of manufacture, or similar details.

[0095] The physical parameters can be, for example, electrical parameters. These physical or electrical parameters can include one or more of the following: voltage; current; capacitance; resistance; temperature limit / range, or the like. Exemplary physical parameters are shown in Fig. 2a, b.

[0096] Extracting the first and / or second semiconductor data (especially the first and / or second data carrier) can include:

[0097] Capturing and / or converting an output format from the first 106A-D data carrier and / or the second 106A-D data carrier into a pattern-searchable, text-based format. Extracting the first and / or second semiconductor data (especially from the first and / or second 106A-D data carrier) may include:

[0098] Extracting the first semiconductor data from the pattern-searchable text-based format of the first data carrier 106A-D and / or

[0099] Extracting the second semiconductor data from the pattern-searchable text-based format of the second data carrier 106A-D.

[0100] For example, a data carrier 106A-D, such as a PDF or CSV file, can be converted, transformed, translated, and / or converted into a text, particularly a machine-readable one, which then allows searching for specific patterns and / or keywords. This capture and / or conversion can be automated, saving time and / or resources. Using pattern matching allows large amounts of data to be efficiently searched and / or relevant information extracted without manual review. This standardizes the data, regardless of its original format. Furthermore, it facilitates further processing and / or analysis of the data, while also reducing storage requirements. Extracting semiconductor data from a searchable, text-based format simplifies the search for specific information.This allows users to search specifically for relevant data and quickly access the information they need.

[0101] The method according to the invention, as illustrated in Fig. 1, may include:

[0102] Access 110, by means of the third computing device 109, to the semiconductor manufacturer database 102A-D of the first computing device 108A-D and / or the semiconductor manufacturer database 102A-D of the second computing device 108A-D. In particular, the data carriers 106A-D (or the semiconductor data contained therein, which are assigned to the corresponding semiconductor-like elements) of the corresponding semiconductor manufacturer databases 102A-D can be accessed by means of the third computing device 109, for example, the first data carrier 106A-D of the semiconductor manufacturer database 102A-D of the first computing device 106A-D and / or the second data carrier 106A-D of the semiconductor manufacturer database 102A-D of the second computing device 108A-D.

[0103] Access 110 can be achieved via network protocols and data transmission techniques or a network connection 104, preferably an internet connection 104, as indicated in Fig. 1. This means that the third computing device 109 has the ability to connect to the databases 102A-D of the semiconductor manufacturers, e.g., via the internet, and access the required data carriers 106A-D 110. Alternatively or additionally, access via the network connection 104, preferably the internet connection 104, can occur at regular intervals, in particular daily, weekly, and / or monthly. In other words, the third computing device 109 can essentially automatically and / or periodically retrieve the databases 102A-D of the semiconductor manufacturers in order to always remain up-to-date and / or obtain current information.By defining specific time intervals for updates, a balance is struck between the need to keep the database up-to-date and the effort associated with data collection and processing. This structured and regular update method helps increase the efficiency of the process by minimizing redundant data queries while ensuring that database 100 provides an accurate overview of the supply chain situation. This also enables efficient and timely access to the semiconductor manufacturers' databases 102A-D, allowing for the retrieval and / or updating of critical information about semiconductor products or components.The use of a network connection (104) and automatic updates at regular intervals facilitate the management and maintenance of the database (100) within the context of supply chain management, particularly in the automotive industry. These mechanisms help ensure that the database (100) remains current, accurate, and comprehensive, which is crucial for managing the supply chain of semiconductor components.

[0104] As can be seen in Fig. 1, the method according to the invention can comprise:

[0105] Store 112 of the semiconductor data contained in the first data carrier 106A-D and / or second data carrier 106A-D, which are assigned to the corresponding semiconductor-like elements, in the semiconductor-manufacturer-independent database 100. For example, the method may include: storing 112 of the first semiconductor data assigned to the first semiconductor-like element and / or the second semiconductor data assigned to the second semiconductor-like element on the third computing device 109, in particular in the semiconductor-manufacturer-independent database 100. This provides an efficient and effective method for integrating and managing data from different sources.

[0106] The method according to the invention may include:

[0107] Generating a first database entry based on the first semiconductor data and / or a second database entry based on the second semiconductor data for the cross-semiconductor database 100 on the third computing device 109. Fig. 3 shows an exemplary database entry 300 of the cross-semiconductor database 100 from Fig. 1, in particular after processing, e.g., after processing the exemplary semiconductor data of the semiconductor-like element from Fig. 2a, b, for example, after processing the data carrier or after extracting the exemplary semiconductor data.

[0108] As shown in Fig. 3, a database entry 300 can contain specific information about a semiconductor-like element and / or a component thereof and / or a manufacturer of the semiconductor-like element or component (e.g., "AUIRF1324WL", "Infineon", "Gen 10.2", "PG-TO262-3", etc.). The database entry 300 can be viewed as a single row in a database table, where each column (e.g., one or more) of the table corresponds to a specific attribute of the entry. A database entry 300 can also contain multiple rows, for example, information associated with a semiconductor-like element (e.g., "AUIRF1324WL"). Central storage of the data or data entries, e.g., on the third computing device 109 or in the semiconductor-manufacturer-spanning database 100 (see Fig. 1), facilitates access and / or analysis, thus enabling effective management of the semiconductor-like element supply chain.

[0109] The method according to the invention may include:

[0110] Structuring and / or normalizing the initial semiconductor data to generate the first database entry, and / or

[0111] Structuring and / or normalizing the second semiconductor data to generate the second database entry, such that the first database entry and / or the second database entry in the semiconductor manufacturer-independent database 100 are verifiable, in particular searchable.

[0112] Structuring and / or normalizing the semiconductor data to generate database entries, and / or ensuring vendor-independent verifiability, particularly searchability, of these entries in the central, cross-vendor semiconductor database 100, brings significant improvements in data consistency, interoperability, and usability. Structuring and normalizing the initial and secondary semiconductor data before generating the corresponding database entries ensures that the data is in a uniform format, which greatly facilitates the aggregation, analysis, and comparison of data from different sources. This is particularly important in an environment where data may originate from various semiconductor manufacturer databases that may use different formats and standards for data storage and transmission.The vendor-independent verifiability and, in particular, the searchability of database entries in the cross-vendor semiconductor database enables users to quickly and efficiently access specific information, regardless of the data's origin. This is crucial for managing the supply chain of semiconductor components, especially in the automotive industry, where the ability to quickly access accurate and up-to-date information on semiconductor chip availability and specifications directly impacts production planning and flexibility. Overall, these new features offer a robust solution to the challenges associated with managing a complex and dynamic supply chain by simplifying and optimizing data integration and utilization across vendor boundaries.

[0113] Structuring the method according to the invention may include:

[0114] Extracting, from the initial semiconductor data, a first plurality of attributes that are assigned to the first semiconductor-like element and / or a manufacturer thereof. Alternatively or additionally, the structuring of the method according to the invention can comprise:

[0115] Extracting, from the second semiconductor data, a second plurality of attributes that are assigned to the second semiconductor-like element and / or a manufacturer thereof. The structuring of the method according to the invention may include:

[0116] Organizing, in particular splitting and / or arranging, the first plurality of attributes in the first database entry and / or the second plurality of attributes in the second database entry.

[0117] These organizational mechanisms enable an efficient and clear structure and / or storage of data in the cross-semiconductor database, which significantly improves query efficiency and / or data management within the database. Targeted data extraction and structuring ensures data consistency within the database, which in turn increases data reliability for end users, particularly in the automotive industry.

[0118] As can be seen in Fig. 3 (see arrows 310-330), the first database entry and / or the second database entry can be subdivided into at least two data categories. The at least two data categories can include: - Header data 302, which includes product information of the first and / or second semiconductor-like element, and

[0119] - Detailed data 304, which include the physical parameters of the first and / or second semiconductor-like element.

[0120] According to Fig. 3, the database entry can be subdivided into header data 302 such as "Infineon" and "PG-TO262-3". The header data 302 can be indexed, for example, by means of an identifier or identification number or "ID", e.g., with a first identifier or ID such as "id 10" and / or "id 5", as shown in Fig. 3. The detail data 304 can comprise physical parameters of the "Infineon" chip and be indexed by means of a second identifier or ID that differs from the first identifier or ID, such as "id 42" in Fig. 3. It should be clear that other subdivisions and / or indexing methods are also conceivable. It is also possible to identify or index the header data with only one ID.

[0121] This subdivision can enable differentiated and / or efficient processing and analysis of the data. The introduction of header data (302) as one of the data categories, encompassing product information of the first and / or second semiconductor element, allows for the rapid identification and / or assignment of the corresponding semiconductor elements. This is particularly important in the automotive industry, where specific product information such as type, model, or manufacturing date is crucial. The inclusion of detailed data (304) as another data category, encompassing physical parameters of the first and / or second semiconductor element, enables in-depth technical analysis and evaluation of the semiconductor chips. This can be crucial for selecting specific semiconductor chips for particular applications, such as in the automotive industry.This brings significant advantages for the management of the supply chain of semiconductor-like elements by offering improved data organization, accessibility and analysis, which can lead to optimized decision-making, higher efficiency and ultimately increased competitiveness, e.g. in the automotive industry.

[0122] The method according to the invention may include:

[0123] The header data 302 and the detail data 304 are distributed across multiple, specifically different, database tables within the cross-manufacturer semiconductor database 100 on the third computing device 109. This distribution enables more efficient and structured data storage and processing by providing a clear separation between general information (header data 302) and specific details (detail data 304) of the semiconductor-like elements. Distributing the data across multiple database tables improves data integrity, reduces data access time, and increases system scalability. This offers significant advantages for managing the supply chain of semiconductor-like elements by providing a more robust, flexible, and efficient database structure specifically tailored to the requirements of the semiconductor industry, and particularly the automotive industry.Improved data structuring and management enables companies to react more quickly to changes in the supply chain, better predict the availability of semiconductor chips, and overall increase the efficiency of their supply chain processes.

[0124] As can be seen with reference to Fig. 1, the method according to the invention can comprise: storing 112 the first database entry and / or the second database entry in the semiconductor manufacturer-independent database 100.

[0125] Storing 112 database entries in the cross-manufacturer database enables a centralized and / or consolidated view of supply chain information, providing improved traceability, analytical capabilities, and / or forecasting accuracy for the availability of semiconductor-like components across different manufacturers. This leads to optimized supply chain control, reduced production downtime, and improved decision-making, for example, in the automotive industry and beyond. It enables the efficient aggregation and / or management of critical supply chain information from various sources on a single, cross-manufacturer platform.Database 100 was provided, enabling a comprehensive overview and control of the availability of semiconductor components, which is crucial in the highly complex and / or interdependent world of semiconductor supply chains.

[0126] Although the foregoing is directed to embodiments of the disclosure, other and further embodiments of the disclosure can be developed without deviation from its basic scope, the scope being defined by the following claims. List of reference numerals

[0127] 100 Semiconductor Manufacturer Cross-Company Database

[0128] 102A-D Semiconductor Manufacturer Database 104 Network Connection

[0129] 106A-D data carrier

[0130] 108A-D calculating machine

[0131] 109 Computing device

[0132] 110 Access 112 Save

[0133] 300 database entries

[0134] 302 Header data

[0135] 304 detailed data

[0136] 310-330 arrows

Claims

Secor Supply Chain Transparency GmbH PATENT CLAIMS 1. Computer-implemented method for operating, in particular creating, preferably populating, and / or updating, a semiconductor manufacturer-independent database for managing a supply chain of semiconductor-like elements, preferably semiconductor chips, particularly in the automotive industry, comprising: Extracting, based on at least one first data carrier assigned to a semiconductor manufacturer database of a first computing device and indicative of at least one first semiconductor-like element, and a second data carrier assigned to a semiconductor manufacturer database of a second computing device and indicative of at least one second semiconductor-like element, first semiconductor data assigned to the first semiconductor-like element and / or second semiconductor data assigned to the second semiconductor-like element, generating a first database entry based on the first semiconductor data and / or a second database entry based on the second semiconductor data for the semiconductor-manufacturer-spanning database on a third computing device.

2. Computer-implemented method according to claim 1, further comprising: structuring and / or normalizing the first semiconductor data to generate the first database entry, and / or Structuring and / or normalizing the second semiconductor data to generate the second database entry, such that the first database entry and / or the second database entry can be verified, in particular searched, in the semiconductor manufacturer-independent database.

3. Computer-implemented method according to claim 2, wherein the structuring comprises: Extracting, from the initial semiconductor data, from an initial plurality of attributes assigned to the first semiconductor-like element and / or its manufacturer, and / or Extract, from the second semiconductor data, a second plurality of attributes that are assigned to the second semiconductor-like element and / or a manufacturer thereof, and Organizing, in particular splitting and / or arranging, the first plurality of attributes in the first database entry and / or the second plurality of attributes in the second database entry.

4. Computer-implemented method according to one of the preceding claims, wherein the first database entry and / or the second database entry is subdivided into at least two data categories, the at least two data categories comprising: - Header data, which includes product information of the first and / or second semiconductor-like element, and - Detailed data including physical parameters of the first and / or second semiconductor-like element.

5. Computer-implemented method according to claim 4, wherein the product information comprises one or more of: an identifier, in particular an order designation, of a product; a manufacturer of the product; a category such as electronics; a subcategory, such as semiconductors; web addresses, in particular URL(s) for a datasheet and / or technical documentation of the product; designation of a housing and / or packaging of the product; arrangement and / or assignment of connections and / or pins on the product, in particular pinout; indication of whether the product is lead- and / or halogen-free; directives of the European Union that impose restrictions on the use of certain hazardous substances in electrical and electronic equipment, preferably "Restriction of Hazardous Substances" (RoHS);Classification indicating whether the product is suitable for use in the automotive industry (automotive grade) or in industrial applications (industrial grade), in particular automotive and / or industrial grade; product price information, including list price, discounts, quantity discounts, places of manufacture, or the like.

6. Computer-implemented method according to one of claims 4-5, wherein the physical parameters, in particular electrical parameters, are one or more of: a voltage; a current; a capacitance; a resistance; a temperature limit range, storage conditions or the like.

7. Computer-implemented method according to one of claims 5-6, further comprising: Splitting the header data and the detail data across multiple, especially different, database tables of the semiconductor manufacturer-independent database on a third computing device.

8. Computer-implemented method according to one of the preceding claims for storing the first database entry and / or the second database entry in the semiconductor manufacturer-independent database.

9. Computer-implemented method according to one of the preceding claims, wherein the first data carrier and / or the second data carrier is a data sheet, in particular a technical data sheet, preferably a Portable Document Format (PDF)-based data sheet, a Comma Separated Value (CSV)-based data sheet, an Extensible Markup Language (XML)-based data sheet, a JavaScript Object Notation (JSON)-based data sheet, or a structured HTML website.

10. Computer-implemented method according to any one of the preceding claims, further comprising: Accessing, using the third computing device, the semiconductor manufacturer database of the first computing device and / or the semiconductor manufacturer database of the second computing device, Storing the first semiconductor data associated with the first semiconductor-like element and / or the second semiconductor data associated with the second semiconductor-like element on the third computing device. 11 Computer-implemented method according to claim 10, wherein access is made via a network connection, preferably an Internet connection and / or at regular intervals, in particular daily, weekly and / or monthly.

12. Computer-implemented method according to any of the preceding claims, comprising extracting the first and / or second semiconductor data: Capturing and / or converting an output format from the first data carrier and / or the second data carrier into a text-based format searchable by pattern matching; Extracting the first semiconductor data from the pattern-searchable text-based format of the first data carrier and / or Extracting the second semiconductor data from the pattern-searchable text-based format of the second data carrier.

13. Computer-implemented method according to one of the preceding claims, wherein the first semiconductor data have a manufacturer-specific format that differs from a manufacturer-specific format of the second semiconductor data.

14. Computer-implemented method according to claim 13, wherein the manufacturer-specific format of the first semiconductor data differs from the manufacturer-specific format of the second semiconductor data by one or more of: a layout, a structure, an arrangement, a position, a terminology, a measurement, a unit, a term, a designation, a formatting, and / or a designation of attributes of the first semiconductor-like element and / or a manufacturer thereof, and / or of the second semiconductor-like element and / or a manufacturer thereof.

15. Computer-implemented method according to one of the preceding claims, wherein the extraction of the first and second semiconductor data comprises processing the first and second data carriers by means of pattern recognition and / or regular expressions.

16. Computer program product comprising instructions which, when the program is executed by a computer, cause it to execute the method according to any one of claims 1-15.

17. Computer-readable storage medium comprising instructions which, when executed by a computer, cause it to execute the method according to any one of claims 1-15.

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