Test and measurement system and method

Abstract

To enable the use of test and measurement equipment that uses a proprietary language in other environments. [Solution] The test measurement system includes a test measurement device 28 having a port for connecting to a device under test (DUT) 29 and a user interface. The processor of the computing device 11 or the test measurement device 28 holds a generative artificial intelligence (AI) model 14 as software and provides an application programming interface (API) for the generative AI model. The processor receives protocol specifications and provides them to the generative AI model. It also receives bus settings that operate according to the protocol via one of the user interfaces and provides them to the generative AI model. Furthermore, it receives a decoder file from the generative AI model, deploys the decoder file to the test measurement device 28, and tests the DUT 29 using the decoder file on the test measurement device 28.

Description

Technical Field

[0001] The present disclosure relates to a test measurement system and method, and more particularly to a test measurement system and method using an AI-based decoder that accelerates the extraction, generation, and deployment of a protocol decoder for electronic signals.

Background Art

[0002] Serial communication is the most widely used approach for transferring data between all electronic devices, whether on a computer or mobile. A protocol provides a secure and reliable form of communication in which the transmitter and receiver adhere to a set of rules for addressing.

[0003] Today, due to the need for improved mobility, faster data exchange, secure data exchange, reduced power consumption in data exchange, and improved ease of use of data connectors, many protocols are available on the market.

[0004] To meet these market needs and provide customers with debugging solutions, tools have been developed to enable developers to develop and release protocols as quickly as possible. The test measurement devices of Tektronix, Inc. support the use of a declarative decoder language (DDL) for developing a protocol decoder to solve the above problems.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Non-Patent Documents

[0006]

Non-Patent Document 1

[0007] However, the above DDL is Tektronix's proprietary language, and it has a unique grammar and rules that engineers must adhere to when incorporating new protocols into test and measurement equipment.

[0008] Specifically, existing solutions have several limitations. First, they cannot support proprietary protocols used by customers. The market for decoder support is growing rapidly. Therefore, application engineers need to learn this grammar and develop protocol decoders to stay competitive. However, learning and developing decoders presents challenges. There is a growing demand for custom decoders. However, deploying and maintaining these custom solutions is cumbersome. [Means for solving the problem]

[0009] The embodiments disclosed herein provide users with the ability to build systems that can easily test devices that transmit and receive signals by decoding electrical layer signals. As disclosed herein, the use of AI-based decoders (AIDs) can reduce the user's workflow time by 80%. The embodiments are also useful for users to customize protocol packets to meet customer needs, such as sensitive packets used in military and government applications. These embodiments enable users to quickly build protocol decoders using low-code and no-code models, and to gain insights into bus environments more rapidly.

[0010] In general, embodiments of the present invention include variations of a four-step process for obtaining a complete decoder for use. The user uploads specifications to a generating AI model. The user also provides the AI ​​model with bus configuration boundaries and definitions, typically through a user interface provided by an Application Programming Interface (API). This allows the generating AI model to generate internal prompts related to the development of the decoder protocol. The generating AI model then converts the specifications and bus configuration and boundaries into a declarative decoder language (DDL) file for deployment. The user can then immediately deploy the DDL file to a test measurement device such as an oscilloscope.

[0011] Tektronix's Declarative Decoder Language (DDL) is a proprietary high-level scripting language designed to define custom protocol decoders for use within Tektronix test and measurement equipment such as oscilloscopes and logic analyzers. It allows for the declarative specification of how waveform data is translated into protocol-level information by mapping signal transitions to symbols, defining protocol layers, and identifying trigger conditions based on specific data patterns or errors (i.e., you only need to provide information about what you want to do, without having to provide specific methods for achieving it). DDL supports multi-layer decoding, error detection, and the integration of visualization tools spanning waveform, character, and protocol representations to associate the decoded data. This language facilitates real-time analysis, decoding, and triggering of complex serial protocols, enhancing the flexibility and diagnostic capabilities of Tektronix equipment. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 shows an embodiment of the test measurement system. [Figure 2] Figure 2 shows an embodiment of the process for automatically generating decoder files. [Figure 3] Figure 3 shows a flowchart of one embodiment of the custom decoder operation. [Figure 4] Figure 4 shows an embodiment of the message flow in the test measurement system. [Figure 5] Figure 5 shows an embodiment of the user interface. [Modes for carrying out the invention]

[0013] Figure 1 shows an embodiment of the test measurement system. User 10 uploads the protocol specification of a new protocol via the API 12 of the decoder assistant generating AI model 14. In this embodiment, the generating AI model is referred to as the decoder assistant 14. Please note that this is different from an AI assistant with which the user interacts, so please do not confuse them. The decoder assistant 14 receives configuration knowledge 16, DDL grammar knowledge 18, DDL encryption knowledge 20, and DDL template knowledge 22 through AI learning (training). Next, the model obtains the bus protocol and bus configuration specification and generates a DDL file 24. The computing device in the test measurement system in Figure 1 receives the DDL file from the decoder assistant 14 and executes a script 26 to generate a plugin. As the capabilities of test measurement devices continue to increase, in addition to the computing device 11, the test measurement device 28 can also be considered a type of computing device in the test measurement system. For example, test and measurement equipment such as oscilloscopes from Tektronix have processors, and many of these products use the same operating system as Microsoft, which is used in computing devices such as personal computers. In this case, the same software as that used on personal computers can be used. Computing devices and test and measurement equipment may include one or more processors, which execute programs such as generative AI models that run on the test and measurement system.

[0014] Next, the test measurement system uses the DDL file to generate the necessary “plugins.” The term “plugins” as used herein refers to files automatically generated from the DDL file for use in the test measurement device, thereby enabling the test measurement device 28 to use the DDL file and display the decoded results. In one embodiment, these plugins include XML (extended markup language) files and QML (Qt Modeling Language) files. Of course, other types of plugins may be generated. Next, a computing device (e.g., 11 or 28) receives the DDL file from the decoder assistant 14 and generates the necessary plugins. In one embodiment, the plugins are generated by a script executed by the computing device. Alternatively, in some embodiments, the decoder assistant may generate the plugins. Once these files are generated, they are deployed to the test measurement device 28, which can then use these files to debug the bus operating under these protocols as the device under test. The device under test (DUT) 29 may be connected to a test measurement device via a test fixture, in which case the DUT 29 connects to the test fixture and sends packets to the test measurement device according to the bus protocol, and these packets are then decoded using a DDL file or plugin file for this new protocol.

[0015] The embodiments described above use specific examples of computing devices that generate plugin files from the generated DDL files, in addition to a decoder assistant that generates DDL files. However, this DDL is a proprietary Tektronix language, and the use of plugins as a new software abstraction layer may be specific to implementations in Tektronix products that support custom decoders. Embodiments of the disclosed technology are not limited to the generation or use of DDL files, nor are they limited to the generation or use of plugin files generated from such DDL files. Embodiments of the disclosed technology may be used in conjunction with the proprietary protocol decoding languages ​​and decoding engine implementations of other manufacturers of test measurement equipment. In this disclosure, the term “decoder file” may be used in a general sense and as a synonym to refer to “generated DDL files” available in Tektronix test measurement equipment, and may also be used to refer to similar files available in test measurement equipment from other manufacturers.

[0016] Figure 2 shows an embodiment of the process for automatically generating a decoder file. In step 30, the user assigns a protocol specification to the decoder assistant for which a custom decoder needs to be created. The user provides bus settings such as voltage thresholds defining the signals, inputs to the bus, and baud rates if the bus uses multiple rates. The decoder assistant extracts the protocol requirements from the specification and passes them internally to the decoder assistant using system prompts. These internally generated prompts also pass knowledge of DDL grammar and DDL file templates to the decoder assistant, which then creates the DDL file in step 32. The user may edit the DDL file as shown in step 36, as described below. If the user does not edit the DDL file, the decoder assistant (i.e., AI) uses the DDL file generated in step 32. Next, in step 34, the system generates a plug-in file and an encrypted DDL file (EDDL), and the plug-in file is provided to the test measurement device 28. Using EDDL files allows for secure management of functions on Tektronix test and measurement devices, preventing tampering with DDL files and allowing the use of unauthorized functions. When the test and measurement device 28 is restarted, or when an application running on the test and measurement device 28 is restarted, the EDDL files and plugins become recognizable and can be imported. If the user decides to edit the DDL file in step 36, the Decoder Assistant (AI) generates a new DDL file and its associated EDDL file in step 38, and the process returns to step 34.

[0017] Figure 3 shows a flowchart of an embodiment for generating a custom decoder. In step 40, the decoder assistant receives the specifications and bus configuration from the user. Next, in step 42, the decoder assistant "reads" the specifications, generates requirements for generating a DDL file, and generates the DDL file in step 44. Next, in step 46, the decoder assistant provides the user with an opportunity to review and edit the generated DLL file. If the user only reviews the file, the system leaves the generated DDL file as is and proceeds to step 50, where an encrypted DDL (EDDL) file is generated. If the user edits the DDL file in step 48, a new DDL file is generated before the EDDL file is generated in step 50. The plugin file is generated in step 52, and the EDDL file and the plugin file are placed in the specified path of the test measurement device and deployed to the test measurement device. Thereafter, the test measurement device is restarted in step 56, which may include restarting the application that manages the test measurement device, rebooting the test measurement device, and other updates, so that the test measurement device can access the new DDL file and the plugin file.

[0018] Figure 4 shows an embodiment of the message flow in a test measurement system. The user provides setup settings, a prompt for uploading specifications, and an instruction to generate a DDL file. Next, the decoder assistant uploads setup knowledge, specification knowledge, DDL grammar knowledge, and encryption knowledge. The generation AI model uses a parser to generate a DDL file in the specified path and then generates an EDDL file. Next, the decoder assistant interacts with the user to enable the user to review the file and, if there are changes, generate a new DDL file and a new EDDL file. Next, the user instructs to save the EDDL file to a specific path.

[0019] Figure 5 shows an embodiment of the user editing interface 60 for the decoder assistant. With fields 62 and 64, the user can input the protocol name (in this example, I2C) and the specification "I2C_2_11.pdf". The user can, if necessary, use the reference button 68 to reference the path (folder path name) to the folder where the protocol specification file is saved. When the DDL file is generated, the decoder assistant displays the DDL file in the editing window 70. When done, the user can select the [DDL generation] button 80. When the status window 74 indicates that the DDL file has been generated, the user selects the [EDDL generation] button 82. Then, the [Plugin generation] button 84 is selected, and the [Deployment] button 86 is used to deploy to the test measurement device.

[0020] As described above, the user requests the decoder assistant to create a DDL file. The decoder assistant generates an internal prompt, an example of which is shown below. prompt = f"Here is specification for {protocol_name} Protocol give as {spec} which has information the the " \ f"protocol layer decoding with respect to all events its packet framing. With " \ f"the syntax and info for a ddl language: {grammar}. Write me a ddl code similar to {ddl} for " \ f"{protocol_name} protocol."

[0021] In this way, users can update test measurement devices with new communication protocols as needed in a much simpler manner than previous approaches. Using the decoder assistant as shown in the embodiments above, users can generate decoders more quickly by communicating in natural language. This additional software generates the necessary plugin files and deploys the plugin files and EDDL files, making development and market traction easier for engineers. By using AI in part of the process, the amount of tool and grammar learning required from the user is minimized.

[0022] Furthermore, existing solutions for Tektronix® test and measurement equipment are Tektronix's proprietary tools (TLX), which can certainly be used to develop serial bus protocols more quickly, but as mentioned above, TLX does not support user-defined protocols. However, since users have access to the tools of this application, they can use their own protocol decoding solutions even when debugging systems using Tektronix® test and measurement equipment.

[0023] Embodiments of the disclosed technology can operate on a specially programmed general-purpose computer, including specially created hardware, firmware, digital signal processors, or processors that operate according to programmed instructions. The terms “controller” or “processor” in this application mean microprocessors, microcomputers, ASICs, and dedicated hardware controllers, etc. Embodiments of the disclosed technology can be implemented by one or more computers (including monitoring modules) or other devices, using computer-readable data such as program modules and computer-executable instructions. Generally, program modules include routines, programs, objects, components, data structures, etc., which, when executed by a processor in a computer or other device, perform specific tasks or implement specific abstract data type expressions. Computer-executable instructions may be stored on computer-readable storage media such as hard disks, optical disks, removable storage media, solid-state memory, and RAM. As will be understood by those skilled in the art, the functions of the program modules may be combined or distributed as needed in various embodiments. Furthermore, these functions can be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits or field-programmable gate arrays (FPGAs). One or more aspects of the disclosed technology can be more effectively implemented using specific data structures, such data structures are considered to be within the scope of computer-executable instructions and computer-usable data described herein.

[0024] The disclosed embodiments may, in some cases, be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored in one or more computer-readable media that can be read and executed by one or more processors. Such instructions may be referred to as computer program products. The computer-readable media described herein means any medium accessible by a computing device. For example, but not limited to, computer-readable media may include computer storage media and communication media.

[0025] Computer storage media means any medium that can be used to store computer-readable information. Examples of computer storage media include, but are not limited to, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other memory technologies, compact disc read-only memory (CD-ROM), DVD (Digital Video Disc) and other optical disc storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices and other magnetic storage devices, and any other volatile or non-volatile removable or non-removable media implemented by any technology. Computer storage media exclude signals themselves and temporary forms of signal transmission.

[0026] A communication medium means any medium that can be used to transmit computer-readable information. Examples of communication mediums, though not limited to them, include coaxial cables, fiber optic cables, air, or any other medium suitable for transmitting electrical, optical, radio frequency (RF), infrared, sound, or other types of signals. Examples

[0027] The following examples are provided that are useful for understanding the technology disclosed herein. These embodiments may include one or more of the examples described below, or any combination thereof.

[0028] Embodiment 1 is a test measurement system comprising one or more test measurement devices, each having one or more ports for connecting to a device under test (DUT); one or more user interfaces; a generative artificial intelligence (AI) model connected to the one or more test measurement devices; and one or more processors, wherein the one or more processors are configured to execute a program that causes the one or more processors to perform the following: providing an application programming interface (API) for the generative AI model; receiving a protocol specification and providing the protocol specification to the generative AI model via the API; receiving configuration settings for a bus that operates according to the protocol via one or more user interfaces; providing the configuration settings to the generative AI model; receiving a decoder file from the generative AI model; deploying the decoder file to the test measurement device; and testing the DUT, including a bus that operates according to the protocol, using the decoder file on the test measurement device.

[0029] Example 2 is the test measurement system of Example 1, further comprising a computing device, wherein one or more user interfaces and one or more processors are located on the computing device.

[0030] Embodiment 3 is a test measurement system of Embodiment 1 or Embodiment 2, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the following: providing an editing user interface as one of the one or more user interfaces, and rendering the decoder file on the editing user interface.

[0031] Embodiment 4 is the test measurement system of Embodiment 3, further configured such that one or more processors execute a program that causes the one or more processors to perform the processes of receiving editing information for the decoder file and generating a new decoder file using the editing information.

[0032] Example 5 is a test and measurement system according to any of Examples 1 to 4, further configured such that one or more processors execute a program that causes one or more processors to perform the process of executing a script that generates a plug-in for the test and measurement device.

[0033] Example 6 is the test measurement system of Example 5, wherein a program that causes one or more processors to perform the process of unpacking the decoder file into the test measurement device includes a program that causes one or more processors to perform the process of loading the decoder file and the plugin into the test measurement device.

[0034] Example 7 is a test measurement system according to any of Examples 1 to 6, wherein one or more processors are further configured to execute a program that causes one or more processors to perform a process of training the generating AI model using information about the decoder file.

[0035] Example 8 is the test measurement system of Example 7, wherein the information regarding the decoder file includes one or more of the following: configuration knowledge, decoder file grammar knowledge, decoder file encryption knowledge, and decoder file template knowledge.

[0036] Example 9 is a test measurement system according to any of Examples 1 to 8, further configured to execute a program that causes one or more processors to perform a process to generate an encrypted decoder file from the decoder file received from the generated AI model.

[0037] Example 10 is a method comprising: providing an application programming interface (API) to a generated AI model; receiving a protocol specification and providing the protocol specification to the generated AI model via the API; receiving configuration settings for a bus that operates according to the protocol via a user interface; providing the configuration settings to the generated AI model; receiving a decoder file from the generated AI model; deploying the decoder file to a test measurement device; and testing a device under test (DUT) including the bus that operates according to the protocol using the decoder file on the test measurement device.

[0038] Example 11 is the method of Example 10, further comprising the process of providing an editing user interface and the process of rendering the decoder file on the editing user interface.

[0039] Example 12 is the method of Example 11, further comprising a process for receiving editing information relating to a decoder file and a process for generating a new decoder file using the editing information.

[0040] Example 13 is a method of any of Examples 10 to 12, further comprising the process of executing a script that generates a plugin for the above-mentioned test and measurement device.

[0041] Example 14 is the method of Example 13, wherein the process of deploying the decoder file to the test and measurement device includes the process of loading the decoder file and the plugin to the test and measurement device.

[0042] Example 15 is a method of any of Examples 10 to 14, further comprising the process of training the generated AI model using the information of the decoder file.

[0043] Example 16 is the method of Example 15, wherein the information regarding the decoder file includes one or more of the following: configuration knowledge, decoder file grammar knowledge, decoder file encryption knowledge, and decoder file template knowledge.

[0044] Example 17 is a method of any of Examples 10 to 16, further comprising the process of generating an encrypted decoder file from a decoder file received from the generated AI model.

[0045] All functions disclosed in the specification, claims, abstract and drawings, and all steps in any method or process disclosed, may be combined in any combination, except where at least some of such functions or steps are mutually exclusive. Each of the functions disclosed in the specification, abstract, claims and drawings may be replaced by an alternative function that serves the same, equivalent or similar purpose, unless otherwise specified.

[0046] In addition, the description of this application refers to certain features. It should be understood that the disclosures herein include all possible combinations of these particular features. Where a particular feature is disclosed in relation to a particular aspect or example, that feature may, to the extent possible, also be used in relation to other aspects and examples.

[0047] Furthermore, when this application refers to a method having two or more defined steps or processes, these defined steps or processes may be performed in any order or simultaneously, as long as the circumstances do not rule out such possibilities.

[0048] For the sake of explanation, specific embodiments of the present invention have been illustrated and described, but it should be understood that various modifications are possible without deviating from the gist and scope of the present invention. Therefore, the present invention should not be limited to anything other than the appended claims. [Explanation of Symbols]

[0049] 10 users 11 Computing Devices 12 API 14. Decoder Assistant (Generative AI Model) 16 Environment setting knowledge 18 DDL grammar knowledge 20 DDL encryption knowledge 22 DDL Template Knowledge 24 DDL files 28 Test and measurement equipment 29. Device under test (DUT)

Claims

1. A test and measurement system, One or more test measurement devices, including at least one test measurement device having one or more ports for connecting to a device under test (DUT), One or more user interfaces, and one or more generative artificial intelligence (AI) models connected to the above test measurement devices, One or more processors and Equipped with, The one or more processors are The process of providing an application programming interface (API) for the generated AI model described above, The process involves receiving the protocol specifications of the protocol and providing those specifications to the generated AI model via the API, The process involves receiving the configuration of a bus that operates according to the protocol described above, via one or more of the above user interfaces. The process of providing the above settings to the generated AI model, The process of receiving a decoder file from the above-generated AI model, The process of deploying the above decoder file to the above test measurement device, The process involves testing the DUT, which includes a bus operating according to the protocol, using the decoder file on the above test measurement device. A test and measurement system configured to execute a program that causes one or more of the above-mentioned processors to perform the above task.

2. The test and measurement system according to claim 1, further comprising a computing device, wherein the one or more user interfaces and the one or more processors are located on the computing device.

3. One or more of the above processors The process of providing an editing user interface as one or more of the above user interfaces, The process of rendering the above decoder file onto the above editing user interface and The test measurement system according to claim 1, further configured to execute a program that causes one or more of the above-mentioned processors to perform the above.

4. The test measurement system according to claim 3, further configured such that one or more processors execute a program that causes one or more processors to perform the process of receiving editing information for the decoder file and the process of generating a new decoder file using the editing information.

5. The test and measurement system according to claim 1, further configured such that one or more of the above-mentioned processors execute a program that causes the one or more processors to perform the process of executing a script that generates a plugin for the above-mentioned test and measurement device.

6. The test measurement system according to claim 5, wherein the program that causes one or more processors to perform the process of deploying the above decoder file to the above test measurement device includes a program that causes one or more processors to perform the process of loading the above decoder file and the above plugin to the above test measurement device.

7. The test measurement system according to claim 1, wherein one or more of the above-mentioned processors are further configured to execute a program that causes one or more of the above-mentioned processors to perform a process of training the generated AI model using information about the above-mentioned decoder file.

8. The test measurement system according to claim 7, wherein the information relating to the decoder file includes one or more of the following: configuration knowledge, decoder file grammar knowledge, decoder file encryption knowledge, and decoder file template knowledge.

9. The test measurement system according to claim 1, further configured such that one or more of the above-mentioned processors execute a program that causes one or more of the above-mentioned processors to perform a process of generating an encrypted decoder file from the decoder file received from the generated AI model.

10. The process of providing an application programming interface (API) to the generated AI model, The process involves receiving the protocol specifications of the protocol and providing those specifications to the generated AI model via the API, The process involves receiving the settings for a bus that operates according to the above protocol via the user interface, The process of providing the above settings to the generated AI model, The process of receiving a decoder file from the above-generated AI model, The process involves deploying the above decoder file to the test measurement device, The process involves testing a device under test (DUT) that includes the bus operating according to the protocol, using the decoder file on a test measurement device. A test and measurement method comprising the following.

11. The method according to claim 10, further comprising running a script that generates a plugin for a test measurement device.

12. The method according to claim 10, further comprising the process of generating an encrypted decoder file from a decoder file received from the above-mentioned generated AI model.

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