System

Abstract

System (100, 200, 300) for determining correction parameters (104, 204, 304) for a measurement of switching parameters (317) for high-performance semiconductors (198-1, 198-2), the system (100, 200, 300) comprising: a computing device (101, 201, 301) which can be coupled to a first measuring probe (112, 312) and a second measuring probe (113, 313), wherein the first measuring probe (112, 312) is designed to detect a first measured variable (102, 202, 302) for the high-performance semiconductors (198-1, 198-2) to be tested, and wherein the second measuring probe (113, 313) is designed to detect a second measured variable (103, 203, 303) for the high-performance semiconductors (198-1, 198-2) to be tested; wherein the computing device (101, 201, 301) is designed: to receive the first measured variable (102, 202, 302) from the first measuring probe (112, 312) and to receive the second measured variable (103, 203, 303) from the second measuring probe (113, 313); and to determine the correction parameters (104, 204, 304) for the first measuring probe (112, 312) and the second measuring probe (113, 313) based on the first measured variable (102, 202, 302) and the second measured variable (103, 203, 303).

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a system for determining correction parameters for a measurement of switching parameters for high-performance semiconductors. TECHNICAL BACKGROUND

[0002] The present disclosure is described below primarily in connection with the measurement of switching parameters of power semiconductors. In particular, the present disclosure is described with wide band gap (WBG) semiconductors, such as silicon carbide semiconductors (SIC) or gallium nitride semiconductors (GaN).

[0003] The measurement of the switching parameters of MOSFETs or IGBTs is typically performed using the double-pulse method. In such a measurement using the double-pulse method, two pulses are applied to the gate of the respective component at different times in an inductive clamp circuit, and the corresponding measured values are recorded.

[0004] The measuring probes used each have specific properties, such as different propagation delays. Such differences can, for example, contribute to different delays in the simultaneous measurement of current and voltage, also known as "skew." Therefore, a "deskewing" or "deembedding" is usually performed before a measurement to determine this time offset and incorporate it into the determination of the switching parameters.

[0005] This process is usually done manually and is time-consuming. SUMMARY

[0006] One object of the disclosure is therefore to simplify the deembedding or deskewing of measurement setups for the measurement of power semiconductors.

[0007] The problem is solved by the subject matter of the independent claims.

[0008] It is revealed: A system for determining correction parameters for a measurement of switching parameters for high-power semiconductors, the system comprising a computing device which can be coupled to a first measuring probe and a second measuring probe, the first measuring probe being designed to detect a first measured variable for the high-power semiconductors to be tested, and the second measuring probe being designed to detect a second measured variable for the high-power semiconductors to be tested, the computing device being designed to receive the first measured variable from the first measuring probe and the second measured variable from the second measuring probe, and to determine the correction parameters for the first measuring probe and the second measuring probe based on the first measured variable and the second measured variable.

[0009] The present disclosure is based on the finding that conventional methods for determining the switching parameters of high-performance semiconductors have limited operating voltages. The operating voltages typically used in such conventional methods are too low to test modern widebandgap semiconductors. Such methods therefore lead to inaccurate results. Furthermore, the determination of the switching parameters of high-performance semiconductors must be performed manually, which increases the effort and the probability of errors.

[0010] The present disclosure therefore provides a system for determining correction parameters for a measurement of switching parameters for high-performance semiconductors, in which the correction parameters are determined automatically. The term "correction parameters" refers to the parameters necessary to compensate for the differences in the measuring probes during a measurement of switching parameters. In general, the determination of the correction parameters can also be referred to as "deembedding" or "deskewing," whereby the determination of the correction parameters can go beyond simply determining a time offset.

[0011] The system according to the present disclosure therefore provides a computing device that determines the correction parameters. For this purpose, the computing device can be coupled to a first measuring probe and a second measuring probe. The measuring probes are the probes that, after determining the correction parameters, are also used to measure the switching parameters. The high-performance semiconductors can be, for example, semiconductors from the group of IGBTs made of SiC, GaN, GaN-HEMT, vertical GaN, and GaN cascode.

[0012] The first measuring probe is designed to detect a first measured variable, and the second measuring probe is designed to detect a second measured variable. The measured variables are each detected on the measurement setup, which is then used to measure the switching parameters, e.g., using the double-pulse method. Test pulses, in particular according to the double-pulse test method, can also be used to detect the measured variables for determining the correction parameters.

[0013] In some embodiments, the computing device can be arranged in a measuring device. In particular, the computing device can be arranged in the measuring device used for measuring the switching parameters, e.g., in an oscilloscope. Thus, the entire measurement setup can be used for determining the correction parameters and subsequently measuring the switching parameters without any modifications having to be made to it.

[0014] The system according to the present disclosure may include not only the computing device, but also any measurement devices required in the respective application. In particular, the computing device may be arranged in a measurement device or a device coupled to a measurement device, e.g., a server, and may be indirectly coupled to the measurement probes.

[0015] In one embodiment, which can be combined with all other embodiments, a measuring device can be coupled to the measuring probes, convert the measured variables into digital values and provide them to the computing device via a network.

[0016] The determined correction parameters can be displayed on a display of the respective measuring device or computer for a user. At the same time, the acquired measured values can be displayed to the user. For this purpose, the DC offset and the vertical scale of the assigned measurement channels can be adjusted to fill the entire screen during a measurement interval.

[0017] The computing device may comprise or be provided as part of a dedicated processing element, for example, a processing unit, microcontroller, field-programmable gate array (FPGA), complex programmable logic device (CPLD), application-specific integrated circuit (ASIC), or the like. A corresponding program or configuration may be provided to implement the required functionality. The computing device may also be provided, at least in part, as a non-transitory computer program product containing computer-readable instructions that can be executed by a processing element. In a further embodiment, the computing device may be integrated as an additional function or method into the firmware or operating system of a processing element already present in the respective application, wherein the corresponding computer-readable instructions are provided.Such computer-readable instructions may be stored in a memory coupled to or integrated with the processing element. The processing element may retrieve and execute the computer-readable instructions from the memory. This also applies to any other elements, units, or functions disclosed herein as part of the system and method.

[0018] Furthermore, it is understood that any necessary supporting or additional hardware, such as a power supply circuit and a clock generator circuit, may be provided.

[0019] In general, any computer program or computer program product disclosed herein shall be deemed to be a non-transitory computer program product.

[0020] A metrology device according to the present disclosure can generally include any device used in a metrology application to acquire an input signal or generate an output signal, or to perform additional or supporting functions in a metrology application. A metrology device can also be implemented as a program or software application that runs as a metrology application on a computer or processor and can communicate with other metrology devices to perform a metrology task. A metrology application, also referred to as a measurement or test setup, can, for example, include at least one or more different metrology devices used for electrical, magnetic, or electromagnetic measurements, in particular on individual devices under test, also called DUTs.A measurement device according to the present disclosure can be configured to perform such electrical, magnetic, or electromagnetic measurements or signal generation on a test object, for example, in a measurement laboratory or in a production facility on the respective production line. An exemplary measurement setup can be used to qualify the individual test objects, i.e., to verify the proper electrical function of the respective test objects.

[0021] For this purpose, measurement devices can comprise at least one signal receiving part for detecting electrical, magnetic, or electromagnetic signals from the device under test and / or at least one signal generating part for generating electrical, magnetic, or electromagnetic signals that can be fed to the device under test. Such a signal receiving part can, for example, but not limited to, contain a front-end stage for detecting, filtering, attenuating, or amplifying electrical signals. The signal generating part can, for example, but not limited to, comprise corresponding signal generators, amplifiers, and filters. In embodiments, the signal is detected via the signal receiving part in a wired or contact-based manner. For this purpose, a corresponding measuring probe (also called a probe) can be connected to the measurement device via a corresponding cable.Likewise, in embodiments, the signal generation and output via the signal generation part takes place in a wired or contact-based manner. For this purpose, a corresponding signal output probe can be connected to the measuring device via a corresponding cable, or the signal is output directly via the cable, e.g., to a device under test. In further embodiments, the signal acquisition can take place contactlessly, e.g., via corresponding antennas, also called OTA or over-the-air. In further embodiments, the signal generation and output can take place contactlessly, e.g., via corresponding antennas, also called OTA or over-the-air. A combination of contact-based signal acquisition, contactless signal acquisition, contact-based signal generation and output, and contactless signal generation and output is also possible.

[0022] Furthermore, measurement devices for signal acquisition may include a signal processing unit that processes the acquired signals. This processing may include converting the acquired signals from analog to digital or vice versa, as well as any other type of digital signal processing, for example, converting time-domain signals to frequency-domain signals.

[0023] The measurement devices may also have a user interface to display the acquired signals to the user and allow the user to control the measurement devices. Of course, a housing enclosing the elements of the measurement device may be provided. It is understood that additional elements such as a power supply circuit and communication interfaces may be provided.

[0024] A measurement device can be a standalone device that can be operated without any additional elements in a measurement application to perform tests on a device under test. Of course, communication capabilities can also be provided to connect the measurement device to other measurement devices.

[0025] A measurement device can, for example, be a signal recording device such as an oscilloscope, in particular a digital oscilloscope, a spectrum analyzer, or a vector network analyzer. A measurement device can also include a signal generation device, e.g., a signal generator, in particular an arbitrary signal generator, also referred to as an arbitrary waveform generator, or a vector signal generator. Other possible measurement devices include devices such as calibration standards or measuring probe tips.

[0026] Of course, at least some of the possible functions, such as signal recording and signal generation, can be combined in a single measuring device.

[0027] In embodiments, the measurement device may comprise pure data acquisition devices capable of acquiring an input signal and transmitting the acquired input signal as a digital input signal to a corresponding data storage or application server. Such pure data acquisition devices do not necessarily have a user interface or a display. Instead, such pure data acquisition devices may be remotely controlled, e.g., via a corresponding data connection such as a network interface or a USB interface. The same applies to pure signal generation devices capable of generating an output signal without having a user interface or configuration input devices. Instead, in one embodiment that can be combined with all other embodiments, such signal generation devices may be remotely controlled via a data connection.

[0028] With the aid of the system according to the present disclosure, an adjustment or correction of different properties of the measuring probes for determining switching parameters can be carried out easily.

[0029] Further embodiments and developments emerge from the dependent claims and from the description with reference to the figures. In particular, all embodiments mentioned herein can be combined with one another in any order or number, unless individual features are mutually exclusive. In particular, the dependent claims of one claim category can also be developed according to another claim category. The features described as device features can be implemented as corresponding functions of a method, and vice versa.

[0030] In an embodiment that can be combined with all embodiments mentioned herein, the first measurement variable and the second measurement variable can each have a different one of the following options: a magnitude of an electrical voltage, a magnitude of an electrical current, a strength of a magnetic field, a strength of an electric field.

[0031] The measured quantities can be those used to determine the switching parameters of the high-performance semiconductors.

[0032] In one embodiment, which can be combined with all other embodiments, the first measured variable can characterize an electrical voltage, e.g. a drain-source voltage of a high-performance semiconductor, and the second measured variable can characterize an electrical current, e.g. a drain current of the high-performance semiconductor, or vice versa.

[0033] In another embodiment, which can be combined with all other embodiments, the first measurement variable can characterize a strength of a magnetic field and the second measurement variable can characterize a strength of an electric field, or vice versa.

[0034] The computing device can be configured to determine the correction parameters independently of the specific nature of the first and second measured variables. This enables flexible use of the system in different scenarios.

[0035] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the correction parameters from the detected first measured variable, a modeled second measured variable, or from the detected second measured variable, a modeled first measured variable, and to determine the correction parameters based on the modeled second measured variable and the detected second measured variable, or based on the modeled first measured variable and the detected first measured variable.

[0036] The modeled measured quantities can each be modeled based on a model of the high-performance semiconductor to be investigated or the measurement setup in which the high-performance semiconductor to be investigated is located.

[0037] For modeling purposes, an equivalent circuit diagram can be used, for example, on the basis of which the behavior of the high-power semiconductor to be investigated or of the measurement setup in which the high-power semiconductor to be investigated is located can be simulated.

[0038] Through modeling, the two measured variables, which are of different nature, can be converted into a comparable form. The determination of the correction parameters can therefore be carried out more easily than would be the case with measured variables of different types.

[0039] In one exemplary embodiment, which can be combined with all other embodiments, the first measured variable can be a drain-source voltage of the power semiconductor. The second measured variable can be a drain current of the power semiconductor.

[0040] A corresponding model can be used to model the modeled first measured variable from the second measured variable or vice versa. Other parameters requested by a user can also be included in the model creation. Corresponding parameters can be, for example, circuit parameters of the measurement setup. The user can, for example, enter the probe types of the measuring probes used. For example, current shunt measuring probes such as current viewing resistor probes, also called CVR probes, as well as isolated current probes such as Hall effect, transformer or Rogowski coil current probes can be used. Depending on the type of probe, the probe resistance and the loop inductances can be taken into account. Other parameters that can be entered directly by a user can be, for example, the probe resistance, the bias voltage, the differential order and the effective inductance of the measuring circuit.If the correction parameters are determined iteratively (see below), the user can also enter a step size for a model inductance, e.g. 1 nH.

[0041] The generation of the modeled first measured variable can, for example, be based on the acquired second measured variable, the measured drain current Id, and the parameters entered by a user. For this purpose, two pulses are transmitted to the test setup according to the double-pulse test method, and the measured variables are identified at least for the turn-on and turn-off processes.

[0042] The first measured quantity, the drain voltage, can be modeled for the switching transients using Kirchhoff's voltage law. To model CVR probes, a corresponding equivalent circuit with a shunt resistor can be used, across which a differential voltage probe measures a shunt voltage. To model current probes, a corresponding measuring impedance can be inserted instead of the shunt resistor.

[0043] In a typical circuit setup, the body diode in one of the high-power semiconductors is active for the turn-on and turn-off processes. For example, if the body diode of the high-side high-power semiconductor is active in a measurement setup, the modeled first measured value, i.e., the modeled drain voltage of the low-side high-power semiconductor, can be calculated as follows: Vds=VDD−Vhsbf−Id*Rprobe−Leff*dId / dt

[0044] Where V dsthe drain voltage, V DD the switching voltage, V hsbf the voltage across the body diode of the high-side high-power semiconductor, I d the current through the high-performance semiconductor, R probe the resistance of the measuring probe used for the second measured quantity, and L eff the effective inductance measured against the inductance of the power loop. V DD and V hsbf can be summarized as the effective bias voltage for the low-side high-power semiconductor, since they are constant during the turn-on process. Therefore, the voltage V ds a function of I d and the d / dt: Vds=Veb−Id*Rprobe−Leff*dld / dt

[0045] If an isolated current probe is used, the equation simplifies to Vds=Veb−Leff*dId / dt

[0046] L eff can be entered by the user in advance. If this is not the case or if L effnot known, L can eff determined by the computing device as described below.

[0047] In a further embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to compare the modeled second measured variable with the acquired second measured variable and to repeat the modeling with at least one changed model parameter if the difference between the modeled second measured variable and the acquired second measured variable exceeds a predetermined threshold. Alternatively, the computing device can further be designed to compare the modeled first measured variable with the acquired first measured variable and to repeat the modeling with at least one changed model parameter if the difference between the modeled first measured variable and the acquired first measured variable exceeds a predetermined threshold.

[0048] The agreement between the respective recorded measured variable and the respective modeled measured variable is a measure of the quality of the modeling. The term "difference" refers to the difference or agreement in the course of the respective variable, whereby a temporal shift or offset is possible.

[0049] The modeling of the respective measured variable can therefore be repeated if the quality of the modeling is not sufficiently good.

[0050] In one embodiment, which can be combined with all other embodiments, the difference can be determined, for example, by comparing several characteristic points, such as maxima or minima. In further embodiments, the offset of characteristic points can be based on the start of the measurement. Another criterion for the difference can be, for example, an offset between the recorded measured variable and the modeled measured variable. The difference can also be determined, for example, via a correlation.

[0051] In yet another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively, in each case with changes to the high-power semiconductor to be examined or the measurement setup in which the high-power semiconductor to be examined is located.

[0052] Iterative determination means that the modeled measurand is calculated multiple times and that the model used to calculate it is adjusted multiple times so that the modeling of the respective measurand is optimized for the various changes. Each iteration can involve a change to the high-performance semiconductor under investigation or the measurement setup in which the high-performance semiconductor under investigation is located. However, multiple iterations can also be performed with each configuration of the high-performance semiconductor under investigation or the measurement setup in which the high-performance semiconductor under investigation is located, as already explained above, in order to optimize the respective model before a further change is made to the model.

[0053] Possible changes that are made to the high-power semiconductor under investigation or to the measurement setup in which the high-power semiconductor under investigation is located are described below.

[0054] In an embodiment which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively in each case with a change in the series resistance of the gate of the high-performance semiconductor to be examined.

[0055] The gate resistance of the high-power semiconductor under test influences its switching behavior. In some embodiments, the gate resistance of the high-power semiconductor under test can be adjusted manually, for example. Each iteration can be initiated by user input.

[0056] In one embodiment, which can be combined with all other embodiments, the system may include an adjustable series resistor coupled to the computing device. Such an adjustable series resistor can be automatically adjusted by the computing device to trigger a further iteration of the modeling process.

[0057] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively in each case with a change in the drain voltage of the high-performance semiconductor to be examined.

[0058] The drain voltage also influences the switching behavior of the high-performance semiconductor under investigation. The drain voltage can be adjusted manually, for example, using a suitable voltage source. Each iteration can be initiated by user input.

[0059] In one embodiment, which can be combined with all other embodiments, the system may include an adjustable voltage source coupled to the computing device. Such an adjustable voltage source can be automatically adjusted by the computing device to trigger a further iteration of the modeling process.

[0060] In yet another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively in each case with a change in the switching voltage of the high-power semiconductor to be examined.

[0061] The switching voltage also influences the switching behavior of the high-performance semiconductor under investigation. The switching voltage can be adjusted manually, for example, using a suitable voltage source. Each iteration can be started by user input.

[0062] In one embodiment, which can be combined with all other embodiments, the system may include an adjustable voltage source coupled to the computing device. Such an adjustable voltage source can be automatically adjusted by the computing device to provide a corresponding switching voltage and trigger a further iteration of the modeling process.

[0063] In an embodiment which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively in each case with a change in the temperature of the high-performance semiconductor to be examined.

[0064] Temperature also influences the switching behavior of the high-performance semiconductor under investigation. The temperature can be adjusted manually, for example, using a suitable heater or cooler. Each iteration can be initiated by user input.

[0065] In one embodiment, which can be combined with all other embodiments, the system may include an adjustable cooling and heating device coupled to the computing device. Such an adjustable cooling and heating device can be automatically adjusted by the computing device to set an appropriate temperature and initiate a further iteration of the modeling process.

[0066] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the model parameters iteratively in each case with a change in the operating point of the high-performance semiconductor to be examined.

[0067] The operating point also influences the switching behavior of the high-performance semiconductor under test. The operating point can be adjusted manually, for example, using appropriate series resistors and voltage sources. Each iteration can be initiated by user input.

[0068] In one embodiment, which can be combined with all other embodiments, the operating point in the system can be set via a single or a combination of the above-mentioned devices in order to initiate a further iteration of the modeling.

[0069] In an embodiment which can be combined with all embodiments mentioned herein, the computing device can further be designed to adapt the model parameters iteratively based on a transformation of the modeled second measured variable and the acquired second measured variable into the time domain, or based on a transformation of the modeled first measured variable and the acquired first measured variable into the time domain.

[0070] When modeling a corresponding measured variable, a corresponding filter order is usually specified for the model. If this filter order is incorrectly estimated or specified, this leads to a fundamental frequency mismatch in the frequency domain between the recorded measured variable and the corresponding modeled measured variable. This can be verified by transforming it to the time domain.

[0071] If the computing device detects such a lack of fundamental frequency agreement, it can, for example, reduce the filter order of the filter used in the model and remodel the corresponding measured variable. This process can be repeated until sufficient agreement in the fundamental frequency is determined.

[0072] The filter order is included in the modeling of the measured quantity in the examples shown above in the term dI d / dt. This can be: dId / dt(i)=[Id(i+h)−Id(i+h)] / 2h

[0073] The filter order of the filter is referred to here as h.

[0074] In yet another embodiment, which can be combined with all embodiments mentioned herein, the computing device can be further configured to determine a characteristic portion of the first measured variable and the second measured variable and to determine the correction parameter based on the determined characteristic portions.

[0075] Characteristic intervals can be, for example, maxima and minima. These can be used to easily determine the time offset, i.e., a horizontal shift, between the modeled second measured variable and the acquired second measured variable, or between the modeled first measured variable and the acquired first measured variable.

[0076] In a further embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the correction parameter based on a cross-correlation.

[0077] In comparison to a simple comparison of maxima or minima, cross-correlation does not require explicit detection of maxima and minima.

[0078] In an embodiment which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the correction parameter based on a trained algorithm.

[0079] The term "trained algorithm" can refer to any type of algorithm that can be trained with appropriate training data to achieve the desired result. Such algorithms are also commonly called AI algorithms, artificial intelligence algorithms, and machine learning algorithms.

[0080] A corresponding algorithm can be trained to determine the correction parameters based on the acquired first measurement variable and the acquired second measurement variable. A corresponding algorithm can be trained with training data collected and processed, for example, by a manufacturer of the system according to the present disclosure.

[0081] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine, in particular iteratively, an effective inductance of a measurement setup in which the high-performance semiconductor to be examined is located, based on a vertical distance between the detected first measurement variable and the modeled first measurement variable or between the detected second measurement variable and the modeled second measurement variable.

[0082] The effective inductance of the measurement setup is incorporated into the modeling of the measured variables, as described above. To determine the effective inductance when it is unknown, an iterative procedure can be used.

[0083] For this purpose, the measured value, e.g., the drain-source voltage, can be compared with the corresponding modeled value. If the measured value is larger than the modeled value, the value for L eff be reduced by the corresponding step size. If the measured value is smaller than the modeled value, the value for L eff increased by the corresponding step size.

[0084] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine, in particular iteratively, an amount of a rise rate of the high-performance semiconductor to be examined based on a vertical distance between the detected first measured variable and the modeled first measured variable or between the detected second measured variable and the modeled second measured variable.

[0085] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the correction parameters as a time offset between the first measured variable and the second measured variable.

[0086] If the correction parameter is determined as a time offset, a simple correction of the measurement of the switching parameters of the high-performance semiconductors can be carried out.

[0087] All that is required is to shift one of the recorded measured values by the corresponding value in the time domain.

[0088] In an embodiment which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the correction parameters additionally as or in the correction parameters additionally a deembedding parameter set.

[0089] For the purposes of this disclosure, a deembedding parameter set is understood to mean a data set that can have S-parameters and, similar to the S-parameters used in deembedding with network analyzers, compensates for undesired effects in the system or in the high-performance semiconductor under investigation or the measurement setup in which the high-performance semiconductor under investigation is located. In contrast to a pure temporal shift, a deembedding parameter set allows for a variety of corrections, in particular the correction of the frequency response.

[0090] Such a deembedding parameter set can, for example, be generated as a deembedding filter or used in a deembedding filter. Furthermore, such a deembedding parameter set can also be used to generate or determine corresponding mapping rules.

[0091] In yet another embodiment, which can be combined with all embodiments mentioned herein, the system can further comprise the first measuring probe and the second measuring probe, and a measuring device coupled to the first measuring probe and the second measuring probe, wherein the measuring device is coupled to the computing device or comprises the computing device, and wherein the measuring device is configured to carry out the measurement of switching parameters on the high-performance semiconductor based on the determined correction parameters.

[0092] The system according to the present disclosure can serve not only to determine the correction parameters. Rather, the system can also be used to measure the switching parameters of the respective high-performance semiconductors. For this purpose, the system can comprise a suitable measuring device, such as an oscilloscope. Such an oscilloscope can comprise the computing device or be coupled to it.

[0093] Furthermore, the system can include the first measuring probe and the second measuring probe, which are connected to the measuring device. If additional measuring probes are present, as described above, these can also be connected to the measuring device.

[0094] The correction parameters can be determined using the measuring device and the computing device and then stored in the measuring device. The subsequent measurement of the switching parameters of the high-performance semiconductors can then be corrected based on the correction parameters.

[0095] The measuring device can further comprise a user interface that allows a user to enter inputs. Such inputs can be, for example, the parameters for modeling explained above. The measuring device can further comprise a display device via which the recorded and modeled measured variables can be displayed to the user. It is understood that other information relevant to the user can also be displayed or can be retrieved by the user for display.

[0096] In an embodiment which can be combined with all embodiments mentioned herein, the measuring device can be designed to adapt at least one input signal for the measurement of the switching parameters.

[0097] The measurement device generates or controls input signals that are fed to the measurement circuit in which the high-performance semiconductor(s) are measured. The measurement device can generate such input signals and output them directly. Alternatively or additionally, the measurement device can control and configure corresponding signal sources.

[0098] For example, an input signal can be the voltage at a gate terminal of one of the high-performance semiconductors. For this purpose, the measurement device can adjust an internal or external voltage source accordingly. Additionally or alternatively, the system can include appropriately configurable gate drivers, which are configured accordingly by the measurement device.

[0099] An input signal can also be the switching voltage, i.e., the voltage that switches the high-performance semiconductors in their load path. For this purpose, a corresponding voltage source can be provided in the measuring device or coupled to it. Other possible input signals can be the gate-source voltage, which can be adjusted, for example, via switchable resistors in the gate drive.

[0100] In another embodiment, which can be combined with all embodiments mentioned herein, the first measuring probe and the second measuring probe can be arranged at spatially different measuring points.

[0101] If the two measuring probes are positioned at the same spatial measurement point, they can be positioned directly on the respective high-performance semiconductor, for example. However, contacting them can be difficult in this case.

[0102] To simplify contacting the high-performance semiconductor, the measuring probes can be arranged at spatially different measuring points. The measuring points can thus be configured, for example, as explicit measuring contacts in the circuit or on the circuit carrier. This can be achieved, in particular, in combination with deembedding, as described above. Deembedding can compensate for distortions in the measurement results that arise due to the different measuring points.

[0103] In yet another embodiment, which can be combined with all embodiments mentioned herein, at least one further measuring probe can be provided in the system, wherein the computing device can further be designed to determine the correction parameters additionally based on a further measured variable detected by the at least one further measuring probe.

[0104] In another embodiment, which can be combined with all embodiments mentioned herein, the measuring probes can each comprise a measuring probe from the group of shunt resistance probes, Hall effect probes, Rogowski coil probes, transformer-based measuring probes, quantum sensor-based measuring probes, and optically isolated probes.

[0105] In an embodiment that can be combined with all embodiments mentioned herein, the computing device can further be configured to determine the correction parameters further based on known probe parameters for the first measuring probe, or known probe parameters for the second measuring probe, or known probe parameters for the first measuring probe and the second measuring probe.

[0106] The probe parameters can, for example, indicate the type of probe and a probe resistance, e.g. a shunt resistance.

[0107] The measuring probes can be pre-measured probes for which the probe parameters are known. Such measuring probes can also be referred to as "proven probes." The measuring probes can have a memory containing the probe parameters. Furthermore, the measuring probes can be designed to transmit the probe parameters to the computing device and the measuring device. A digital data interface can be provided for this purpose, for example.

[0108] Alternatively or additionally, the measuring probes can have or provide a unique code. Using this code, the computer or measuring device can retrieve the probe parameters from a corresponding data source, e.g., a server of the measuring probe manufacturer.

[0109] In another embodiment, which can be combined with all embodiments mentioned herein, the computing device can further be designed to determine the probe parameters for the first measuring probe, or the probe parameters for the second measuring probe, or the probe parameters for the first measuring probe and the second measuring probe from a model for determining the modeled first measured variable or the modeled second measured variable.

[0110] The probe parameters can be determined, for example, by iteratively adjusting the probe parameters in the model and repeatedly comparing the modeled measured variable with the measured variable. For this purpose, a similarity value can be determined and optimized, for example. TABLE OF CONTENTS OF THE DRAWINGS

[0111] The present disclosure is explained in more detail below with reference to the exemplary embodiments shown in the schematic figures of the drawings. Fig. 1 shows a block diagram of an embodiment of a system according to the present disclosure; Fig. 2 shows a block diagram of another embodiment of a system according to the present disclosure; Fig. 3 shows a block diagram of another embodiment of a system according to the present disclosure; Fig. 4 shows a flow diagram of an embodiment of a method according to the present disclosure; Fig. 5 shows a block diagram of an embodiment of an oscilloscope that may serve as a measurement device according to the present disclosure; and Fig. 6 shows a block diagram of an embodiment of another oscilloscope that may serve as a measurement device according to the present disclosure.

[0112] In all figures, functionally identical elements and devices - unless otherwise indicated - have been provided with similar reference symbols which are identical at least in the two least significant digits (units and tens). DETAILED DESCRIPTION OF THE FIGURES

[0113] Fig. 1 shows a block diagram of a system 100. The system 100 is used to determine correction parameters 104 for measuring switching parameters for high-power semiconductors 198-1, 198-2. For this purpose, the system 100 has a computing device 101 that performs the determination of the correction parameters 104. It is understood that the explanations of other embodiments of the system disclosed herein apply analogously to the system 100.

[0114] The computing device 101 can be coupled to a first measuring probe 112 and a second measuring probe 113. It is understood that such a coupling can be direct or indirect. The first measuring probe 112 can be configured to acquire a first measured variable 102 for the high-performance semiconductors 198-1, 198-2 to be tested, and the second measuring probe 113 can be configured to acquire a second measured variable 103 for the high-performance semiconductors 198-1, 198-2 to be tested.

[0115] For better illustration only, Fig. 1 further shows a circuit arrangement 199. The first measuring probe 112 can detect a drain-source voltage of a high-power semiconductor 198-2 of the circuit arrangement 199, and the second measuring probe 113 can detect a drain-source current of the high-power semiconductor 189-2. The high-power semiconductor 189-2 is the low-side switch of the circuit arrangement 199. Alternatively or additionally, measuring probes can also be arranged on the high-side high-power semiconductor 189-1. The non-measured high-power semiconductor, i.e., the high-side high-power semiconductor 189-1 here, can also be replaced or bridged by an inductor.

[0116] The computing device 101 receives the first measured variable 102 from the first measuring probe and the second measured variable 103 from the second measuring probe. Based on the first measured variable 102 and the second measured variable 103, the computing device 101 determines the correction parameters 104. The correction parameters can in particular relate to differences between the first measuring probe and the second measuring probe, which can be compensated for based on the correction parameters 104 during a later measurement of switching parameters.

[0117] The first measured variable 102 and the second measured variable 103 can, for example, have a value of an electrical voltage and a value of an electrical current, or a strength of a magnetic field and a strength of an electrical field.

[0118] The computing device 101 can determine a characteristic portion of the first measured variable 102 and the second measured variable 103 and determine the correction parameters 104 based on the determined characteristic portions. Characteristic portions can be, for example, the turn-on phase or the turn-off phase of the high-power semiconductors 198-1, 198-2.

[0119] The computing device 101 can determine the correction parameter 104, for example, based on a cross-correlation. The computing device 101 can additionally or alternatively determine the correction parameter 104 based on a trained algorithm, also called a K1 algorithm.

[0120] The computing device 101 can determine the correction parameters 104, for example, as a time offset between the first measured variable 102 and the second measured variable 103, which results from the differences in the first measuring probe and the second measuring probe.

[0121] The computing device 101 can further determine the correction parameters 104 as a deembedding parameter set.

[0122] In addition, the computing device 101 can determine, in particular iteratively, a vertical distance between the acquired first measured variable 102 and the modeled first measured variable or between the acquired second measured variable 103 and the modeled second measured variable. Based on this distance, the computing device 101 can determine an effective inductance of a measurement setup in which the high-power semiconductor 198-1, 198-2 under test is located, or a magnitude of a slew rate of the high-power semiconductor 198-1, 198-2 under test, or both.

[0123] Fig. 2 shows a block diagram of a system 200. The system 200 is based on the system 100. Consequently, the system 200 includes a computing device 201 that performs the determination of the correction parameters 204. It is understood that the explanations of other embodiments of the system disclosed herein apply analogously to the system 200.

[0124] In the system 200, the computing device 201 has a modeling function 207. The modeling function 207 calculates a modeled second measured variable 208 from the first measured variable 202. The correction parameters 204 are subsequently determined from a comparison 209 of the acquired second measured variable 203 and the modeled second measured variable 208. Alternatively, the computing device 201 can also calculate a modeled first measured variable from the acquired second measured variable 203 and compare it with the acquired first measured variable 202.

[0125] The computing device 201 can iteratively determine the respective modeled measured variable. To this end, the computing device 201 can compare the modeled second measured variable 208 with the acquired second measured variable 203 and repeat the modeling with at least one modified model parameter if the difference between the modeled second measured variable 208 and the acquired second measured variable 203 exceeds a predetermined threshold. The computing device 201 can also compare the modeled first measured variable with the acquired first measured variable 202 and repeat the modeling with at least one modified model parameter if the difference between the modeled first measured variable and the acquired first measured variable 202 exceeds a predetermined threshold.

[0126] The computing device 201 can further determine the model parameters iteratively, in each case with changes to the high-power semiconductor to be tested or to the measurement setup in which the high-power semiconductor to be tested is located. To this end, the computing device 201 can determine the model parameters iteratively, for example, in each case with a change in the series resistance of the gate of the high-power semiconductor to be tested, or in each case with a change in the drain voltage of the high-power semiconductor to be tested, or in each case with a change in the switching voltage of the high-power semiconductor to be tested, or in each case with a change in the temperature of the high-power semiconductor to be tested, or in each case with a change in the operating point of the high-power semiconductor to be tested. A combination of different changes is also possible.

[0127] The computing device 201 can further adapt the model parameters iteratively based on a transformation of the modeled second measured variable 208 and the acquired second measured variable 203 into the time domain, or based on a transformation of the modeled first measured variable and the acquired first measured variable 202 into the time domain.

[0128] Fig. 3 shows a block diagram of a system 300. The system 300 is based on the system 200. Therefore, the system 300 has a computing device 301, which determines the correction parameters 304. The computing device 301 has a modeling function 307. The modeling function 307 calculates a modeled second measured variable 308 from the first measured variable 302. The correction parameters 304 are then determined from a comparison 309 of the acquired second measured variable 303 and the modeled second measured variable 308. Alternatively, the computing device 301 can also calculate a modeled first measured variable from the acquired second measured variable 303 and compare it with the acquired first measured variable 302. It is understood that the explanations of other embodiments of the system disclosed herein apply analogously to the system 300.

[0129] The system 300 further comprises a measuring device 314, e.g., an oscilloscope. The measuring device 314 comprises the computing device 301. The measuring device 314 further comprises two signal acquisition interfaces 315-1, 315-2. The signal acquisition interface 315-1 is coupled to a first measuring probe 312, and the signal acquisition interface 315-2 is coupled to a second measuring probe 313. The first measuring probe 312 acquires the first measured variable 302, and the second measuring probe acquires the second measured variable 303, transmitting it to the measuring device 314, in which the two acquired measured variables 302, 303 are transferred to the computing device 301.

[0130] The measuring probes 312, 313 can each comprise a measuring probe from the group of shunt resistance probes, Hall effect probes, Rogowski coil probes, transformer-based measuring probes, quantum sensor-based measuring probes, and optically isolated probes. The computing device 301 can further be configured to determine the correction parameters 304 based on known probe parameters for the measuring probes 312, 313.

[0131] The computing device 301 can alternatively determine or derive the probe parameters for the measuring probes 312, 313 from the model for determining the modeled first measured variable or the modeled second measured variable.

[0132] The measuring device 314 further comprises a signal processing unit 316, which receives both the acquired measurement variables 302, 303 and the correction parameters 304. The signal processing unit 316 determines the switching parameters 317 on the high-performance semiconductor based on the determined correction parameters 304.

[0133] The measuring device 314 can adapt at least one input signal for measuring the switching parameters 317. For this purpose, the measuring device 314 can have a corresponding signal or control output.

[0134] In system 300, the first measuring probe 312 and the second measuring probe 313 can be arranged at spatially different measuring points. In further embodiments, at least one additional measuring probe can be provided, and the computing device 301 can additionally determine the correction parameters 304 based on a further measured variable detected by the at least one additional measuring probe.

[0135] Fig. 4 shows a flowchart of a method for determining correction parameters for a measurement of switching parameters for high-performance semiconductors.

[0136] The method comprises receiving S1 a first measurement variable from a first measuring probe, wherein the first measuring probe is designed to detect a first measurement variable for the high-power semiconductors to be tested, receiving S2 a second measurement variable from a second measuring probe, wherein the second measuring probe is designed to detect a second measurement variable for the high-power semiconductors to be tested, and determining S3 the correction parameters for the first measuring probe and the second measuring probe based on the first measurement variable and the second measurement variable.

[0137] The method can be developed according to any of the further embodiments listed below.

[0138] Fig. 5 shows a block diagram of an oscilloscope OSC1 that may be used in an embodiment of a system according to the present disclosure.

[0139] The OSC1 oscilloscope comprises a housing (HO) containing four measurement inputs (MIP1, MIP2, MIP3, and MIP4). These inputs are connected to a signal processor (SIP) for processing the measured signals. The SIP is connected to a display (DISP1) for displaying the measured signals to the user.

[0140] Although not explicitly shown, it should be understood that the OSC1 oscilloscope can also include signal outputs. Such signal outputs can be used, for example, to output calibration signals. Such calibration signals allow the calibration of the measurement setup before performing measurements. The process of calibrating and correcting measurement signals based on the calibration can also be referred to as "deembedding" and can involve applying appropriate algorithms to the generated or measured signals.

[0141] In the oscilloscope OSC1, the signal processor SIP or an additional processing element can execute or implement the function of the computing device according to the present disclosure. Of course, a communication interface for communication with other measurement devices can be provided in the oscilloscope OSC1.

[0142] Fig.Figure 6 shows a block diagram of an oscilloscope (OSC), which may be an implementation of a measurement device according to the present disclosure. The oscilloscope (OSC) is implemented as a digital oscilloscope. However, it should be noted that the present disclosure may also be implemented with any other type of oscilloscope.

[0143] The oscilloscope OSC comprises, for example, five general sections: the vertical system (VS), the triggering section (TS), the horizontal system (HS), the processing section (PS), and the display (DISP). It is understood that the division into five general sections represents a logical division and in no way restricts the placement and implementation of the oscilloscope OSC elements.

[0144] The VS vertical system is primarily used to offset, attenuate, and amplify a signal to be acquired. For example, the signal can be modified to fit within the available display area of the DISP display or to have a user-configured vertical size.

[0145] For this purpose, the vertical system VS includes a signal conditioning section SC with an attenuator ATT and a digital-to-analog converter (DAC), which are connected to an amplifier AMP. The amplifier AMP is connected to a filter FI1, which in the example shown is provided as a low-pass filter. The vertical system VS also includes an analog-to-digital converter (ADC), which receives the output of the filter FI1 and converts the received analog signal into a digital signal.

[0146] The attenuator ATT and the amplifier AMP serve to adapt the amplitude of the signal to be acquired to the operating range of the analog-to-digital converter (ADC). The digital-to-analog converter (DAC) modifies the DC component of the input signal to be acquired so that it fits within the operating range of the analog-to-digital converter (ADC). The filter FI1 serves to filter out unwanted high-frequency components of the signal to be acquired.

[0147] The trigger section TS operates with the signal provided by the amplifier AMP. The trigger section TS includes a filter FI2, which in this version is implemented as a low-pass filter. The filter FI2 is connected to a trigger system TS1.

[0148] The trigger section (TS) is used to capture predefined signal events and allows the horizontal system (HS) to, for example, display a stable view of a repeated waveform or simply display waveform sections containing the respective signal event. It should be understood that the predefined signal event can be configured by a user via a user input on the oscilloscope (OSC).

[0149] Possible predefined signal events may include, but are not limited to, the signal crossing a predefined trigger threshold in a predefined direction, i.e., with a rising or falling edge. Such a trigger condition is also referred to as an edge trigger. Another trigger condition is called "glitch triggering," and triggers when a pulse occurs in the signal being acquired whose width is greater or less than a predefined time.

[0150] To ensure an exact match of the trigger signal with the waveform shown on the DISP display, a common time base can be provided for the analog-to-digital converter ADC and the trigger system TS1.

[0151] It is understood that, although not explicitly shown, the trigger system TS1 may comprise at least one of the following components: configurable voltage comparators for setting the trigger thresholds, fixed voltage sources for setting the required edge, corresponding logic gates such as an XOR gate and flip-flops for generating the trigger signal.

[0152] The trigger section TS is provided as an analog trigger section for example. It should be understood that the oscilloscope OSC can also be equipped with a digital trigger section. Such a digital trigger section operates not with the analog signal provided by the amplifier AMP, but with the digital signal provided by the analog-to-digital converter ADC.

[0153] A digital trigger section may include a processing element, such as a processor, a DSP, a CPLD, an ASIC, or an FPGA, to implement digital algorithms for detecting a valid trigger signal.

[0154] The horizontal system HS is connected to the output of the trigger system TS1 and is mainly used to position and scale the signal to be acquired horizontally on the display DISP.

[0155] The oscilloscope OSC further includes a processing section (PS), which implements digital signal processing and data storage for the oscilloscope. The processing section PS includes an acquisition processing element (ACP), which is connected to the output of the analog-to-digital converter (ADC) and the output of the horizontal system (HS), as well as to a memory (MEM), and a post-processing element (PPE).

[0156] The acquisition processing element (ACP) manages the acquisition of digital data from the analog-to-digital converter (ADC) and the storage of the data in the memory (MEM). The acquisition processing element (ACP) may, for example, comprise a processing element that has a digital interface to the analog-to-digital converter (ADC) and a digital interface to the memory (MEM). The processing element may, for example, comprise a microcontroller, a DSP, a CPLD, an ASIC, or an FPGA with appropriate interfaces. In a microcontroller or DSP, the functionality of the acquisition processing element (ACP) may be implemented as computer-readable instructions executed by a CPU. In a CPLD or FPGA, the functionality of the acquisition processing element (ACP) may be configured in the CPLD or FPGA instead of having software executed by a processor.

[0157] The processing section PS also includes a communication processor CP and a communication interface COM.

[0158] The communication processor CP can be a device that manages data transfer to and from the oscilloscope OSC. The communication interface COM can be designed for any suitable communication standard, such as Ethernet, Wi-Fi, Bluetooth, NFC, an infrared communication standard, and a visible-light-based communication standard.

[0159] The communication processor CP is connected to the memory MEM and can use the memory MEM to store and retrieve data.

[0160] Of course, the communication processor CP can also be connected to any other element of the oscilloscope OSC in order to retrieve device data or to provide device data received, for example, from a management server.

[0161] The post-processing element (PPE) can be controlled by the acquisition processing element (ACP) and can access the memory (MEM) to retrieve data to be displayed on the display (DISP). The post-processing element (PPE) can prepare the data stored in the memory (MEM) so that the display (DISP) can display the data to a user, for example, as a waveform. The post-processing element (PPE) can also implement analysis functions such as cursors, waveform measurements, histograms, or mathematical functions.

[0162] The display DISP controls all aspects of signal representation for a user and, although not explicitly shown, may include any component required to receive data to be displayed and control a display device to display the data as desired.

[0163] It is understood that the oscilloscope OSC, although not shown, may include a user interface through which a user can interact with the oscilloscope OSC. Such a user interface may include dedicated input elements such as buttons and switches. The user interface may also be provided, at least in part, as a touch-sensitive display device.

[0164] In the oscilloscope OSC, one of the processing elements, also called a computing element, in the processing section PS or an additional processing element can perform the function of the computing unit according to the present disclosure.

[0165] It is understood that all elements of the oscilloscope OSC that perform digital data processing can be provided as dedicated elements. Alternatively, at least some of the functions described above can be implemented in a single hardware element, such as a microcontroller, DSP, CPLD, or FPGA. In general, the logical functions described above can be implemented in any suitable hardware element of the oscilloscope OSC and do not necessarily need to be divided into the various sections described above.

[0166] The processes, methods, or algorithms disclosed herein may be transferred to or implemented by a computing device, controller, or computer. These may include any existing programmable electronic control unit or dedicated electronic control unit. Likewise, the processes, methods, or algorithms may be stored as data and instructions that can be executed by a controller or computer in many forms, including, but not limited to, information permanently stored on non-writable storage media such as read-only devices and information modifiably stored on writable storage media such as floppy disks, magnetic tapes, compact discs, random access memory, and other magnetic and optical media. The processes, methods, or algorithms may also be implemented in a software-executable object.Alternatively, the processes, methods, or algorithms may be embedded, in whole or in part, in suitable hardware components such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), state machines, controllers, or other hardware components or devices, or a combination of hardware, software, and firmware components.

[0167] Although exemplary embodiments are described above, it should be understood that these embodiments do not encompass all possible forms of implementation of the present disclosure covered by the claims. The terms used in the specification are for the purpose of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the disclosure. As described above, the features of various embodiments may be combined to form further embodiments of the invention that may not be explicitly described or illustrated.While various embodiments may be described as advantageous or preferred over other embodiments or prior art implementations with respect to one or more desired characteristics, those skilled in the art will recognize that one or more properties or features may be altered in favor of desired overall system characteristics depending on the specific application and implementation. These properties may include, but need not be limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, maintainability, weight, manufacturability, ease of assembly, etc.Thus, to the extent that embodiments have been described as less desirable than other embodiments or prior art implementations with respect to certain features, those embodiments still fall within the scope of the disclosure and may be desirable for certain applications.

[0168] With respect to the processes, systems, methods, heuristics, etc. described herein, it is understood that although the steps of such processes, etc., have been described in a particular order, such processes may be performed in a different order than that described herein. Likewise, it is understood that certain steps may be performed concurrently, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of the processes herein are illustrative of particular embodiments and should in no way be construed to limit the claims.

[0169] In summary, it is understood that the disclosed subject matter may be modified and varied without departing from the scope of the present disclosure.

[0170] All terms used in the claims are intended to be given their broadest reasonable interpretations and their common meanings as understood by those familiar with the technologies described herein, unless expressly stated otherwise. In particular, the use of singular articles such as "a," "a," "the," "said," etc., should be read to include one or more of the specified elements, unless a claim expressly excludes the contrary. OTHER EMBODIMENTS: 1. A method for determining correction parameters for a measurement of switching parameters for high-performance semiconductors, the method comprising: Receiving (S1) a first measured variable from a first measuring probe, wherein the first measuring probe is designed to detect a first measured variable for the high-performance semiconductors to be tested, Receiving (S2) a second measurement variable from a second measurement probe, wherein the second measurement probe is configured to detect a second measurement variable for the high-performance semiconductors to be tested; Determining (S3) the correction parameters for the first measuring probe and the second measuring probe based on the first measured variable and the second measured variable. 2. The method according to embodiment 1, wherein the first measurement variable and the second measurement variable each have a different one of the following options: a quantity of electrical voltage; a quantity of an electric current; a strength of a magnetic field; and a strength of an electric field. 3. Method according to one of the preceding embodiments, further comprising: Determining a modeled second measured variable from the acquired first measured variable, or determining a modeled first measured variable from the acquired second measured variable; and Determining the correction parameters based on the modeled second measured variable and the acquired second measured variable, or based on the modeled first measured variable and the acquired first measured variable. 4. The method according to embodiment 3, further comprising comparing the modeled second measured variable with the acquired second measured variable and repeating the modeling with at least one changed model parameter if the Difference between the modelled second measured variable and the recorded second measured variable exceeds a specified threshold; or Comparing the modeled first measured variable with the acquired first measured variable and repeating the modeling with at least one changed model parameter if the difference between the modeled first measured variable and the acquired first measured variable exceeds a predetermined threshold. 5. The method according to embodiment 4, further comprising determining the model parameters iteratively with changes to the high-power semiconductor to be examined or the measurement setup in which the high-power semiconductor to be examined is located. 6. The method according to embodiment 5, further comprising determining the model parameters iteratively in each case with a change in the series resistance of the gate of the high-performance semiconductor to be examined. 7. The method according to any one of embodiments 5 and 6, further comprising determining the model parameters iteratively in each case with a change in the drain voltage of the high-performance semiconductor to be examined. 8. The method according to any one of embodiments 5 to 7, further comprising determining the model parameters iteratively in each case with a change in the switching voltage of the high-performance semiconductor to be examined. 9. The method according to any one of embodiments 5 to 8, further comprising determining the model parameters iteratively in each case with a change in the temperature of the high-performance semiconductor to be examined. 10. Method according to one of embodiments 5 to 9, further comprising determining the model parameters iteratively in each case with a change in the operating point of the high-performance semiconductor to be examined. 11. The method according to any one of embodiments 5 to 10, further comprising adapting the model parameters iteratively based on a transformation of the modeled second measured variable and the acquired second measured variable into the time domain, or based on a transformation of the modeled first measured variable and the acquired first measured variable into the time domain. 12. The method according to any one of embodiments 3 to 10, further comprising determining a characteristic portion of the first measured variable and the second measured variable and determining the correction parameter based on the determined characteristic portions. 13. The method of any one of embodiments 3 to 12, further comprising determining the correction parameter based on a cross-correlation. 14. The method of any preceding embodiment, further comprising determining the correction parameter based on a trained algorithm. 15. The method according to any one of embodiments 3 to 14, further comprising determining, based on a vertical distance between the detected first measured variable and the modeled first measured variable or between the detected second measured variable and the modeled second measured variable, in particular iteratively, one of the following variables: an effective inductance of a measurement setup in which the high-performance semiconductor to be tested is located; and an amount of a rise rate of the high-performance semiconductor to be examined. 16. Method according to one of the preceding embodiments, further comprising determining the correction parameters as a time offset between the first measured variable and the second measured variable. 17. The method according to any one of the preceding embodiments, further comprising determining the correction parameters as a deembedding parameter set. 18. Method according to one of the preceding embodiments, further comprising: Detecting the first measured variable with the first measuring probe and the second measured variable with the second measuring probe on a measuring device which is coupled to the first measuring probe and the second measuring probe; wherein the measuring device performs the measurement of switching parameters on the high-power semiconductor based on the determined correction parameters. 19. The method according to embodiment 18, further comprising adapting at least one input signal for the measurement of the switching parameters by the measuring device. 20. Method according to one of the preceding embodiments 18 and 19, wherein the first measuring probe and the second measuring probe are arranged at spatially different measuring points. 21. Method according to one of the preceding embodiments 18 to 20, wherein at least one further measuring probe is provided; and wherein the method comprises determining the correction parameters additionally based on a further measured variable detected by the at least one further measuring probe. 22. Method according to one of the preceding embodiments, wherein the measuring probes each comprise a measuring probe from the group of: shunt resistance probes; Hall effect probes; Rogowski coil probes; Transformer-based measuring probes; quantum sensor-based measuring probes; and optically isolated probes. 23. The method according to any one of the preceding embodiments, further comprising determining the correction parameters based on known probe parameters for the first measuring probe, or known probe parameters for the second measuring probe, or known probe parameters for the first measuring probe and the second measuring probe. This also applies to the additional measuring probes, if present. 24. The method according to embodiments 3 and 23, further comprising determining the probe parameters for the first measuring probe, or the probe parameters for the second measuring probe, or the probe parameters for the first measuring probe and the second measuring probe from a model for determining the modeled first measurand or the modeled second measurand. LIST OF REFERENCE SYMBOLS 100, 200, 300 systems 101, 201, 301 computing device 102, 202, 302 first measurement 103, 203, 303 second measurement 104, 204, 304 correction parameters 207, 307 Modeling 208, 308 modeled second measure 209, 309 Comparison 112, 312 first measuring probe 113, 313 second measuring probe 314 measuring device 315-1, 315-2 signal acquisition interface 316 Signal processing unit 317 switching parameters 199 Circuit arrangement 198-1, 198-2 High-performance semiconductors OSC1 oscilloscope HO housing MIP1, MIP2, MIP3, MIP4 measurement input SIP signal processor DISP1 Display OSC Oscilloscope VS vertical system SC signal conditioning section ATT attenuator DAC digital-to-analog converter AMP amplifier FI1 Filter ADC analog-to-digital converter TS trigger section AMP2 amplifier FI2 filter TS1 trigger system HS horizontal system PS processing section ACP capture processing element MEM memory PPE post-processing element DISP Display

Claims

[1] System (100, 200, 300) for determining correction parameters (104, 204, 304) for a measurement of switching parameters (317) for high-performance semiconductors (198-1, 198-2), the system (100, 200, 300) comprising: a computing device (101, 201, 301) which can be coupled to a first measuring probe (112, 312) and a second measuring probe (113, 313), wherein the first measuring probe (112, 312) is designed to detect a first measured variable (102, 202, 302) for the high-performance semiconductors (198-1, 198-2) to be tested, and wherein the second measuring probe (113, 313) is designed to detect a second measured variable (103, 203, 303) for the high-performance semiconductors (198-1, 198-2) to be tested; wherein the computing device (101, 201, 301) is designed: to receive the first measured variable (102, 202, 302) from the first measuring probe (112, 312) and to receive the second measured variable (103, 203, 303) from the second measuring probe (113, 313); and to determine the correction parameters (104, 204, 304) for the first measuring probe (112, 312) and the second measuring probe (113, 313) based on the first measured variable (102, 202, 302) and the second measured variable (103, 203, 303). [2] The system (100, 200, 300) of claim 1, wherein the first measurement variable (102, 202, 302) and the second measurement variable (103, 203, 303) each comprise a different one of the following options: a quantity of electrical voltage; a quantity of an electric current; a strength of a magnetic field; and a strength of an electric field. [3] System (100, 200, 300) according to one of the preceding claims, wherein the computing device (101, 201, 301) is further designed: to determine the correction parameters (104, 204, 304) from the recorded first measured variable (102, 202, 302) a modeled second measured variable (208, 308), or from the recorded second measured variable (103, 203, 303) a modeled first measured variable; and to determine the correction parameters (104, 204, 304) based on the modeled second measured variable (208, 308) and the acquired second measured variable (103, 203, 303), or based on the modeled first measured variable and the acquired first measured variable (102, 202, 302). [4] System (100, 200, 300) according to claim 3, wherein the computing device (101, 201, 301) is further configured: to compare the modeled second measured variable (208, 308) with the acquired second measured variable (103, 203, 303) and to repeat the modeling with at least one changed model parameter if the difference between the modeled second measured variable (208, 308) and the acquired second measured variable (103, 203, 303) exceeds a predetermined threshold value; or to compare the modeled first measured variable with the acquired first measured variable (102, 202, 302) and to repeat the modeling with at least one changed model parameter if the difference between the modeled first measured variable and the acquired first measured variable (102, 202, 302) exceeds a predetermined threshold value. [5] System (100, 200, 300) according to claim 4, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively, in each case with changes to the high-power semiconductor (198-1, 198-2) to be examined or to the measurement setup in which the high-power semiconductor (198-1, 198-2) to be examined is located. [6] System (100, 200, 300) according to claim 5, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively in each case with a change in the series resistance of the gate of the high-performance semiconductor (198-1, 198-2) to be examined. [7] System (100, 200, 300) according to one of claims 5 and 6, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively in each case with a change in the drain voltage of the high-performance semiconductor (198-1, 198-2) to be examined. [8] System (100, 200, 300) according to one of claims 5 to 7, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively in each case with a change in the switching voltage of the high-power semiconductor (198-1, 198-2) to be examined. [9] System (100, 200, 300) according to one of claims 5 to 8, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively in each case with a change in the temperature of the high-performance semiconductor (198-1, 198-2) to be examined. [10] System (100, 200, 300) according to one of claims 5 to 9, wherein the computing device (101, 201, 301) is further configured to determine the model parameters iteratively in each case with a change in the operating point of the high-performance semiconductor (198-1, 198-2) to be examined. [11] System (100, 200, 300) according to one of claims 5 to 10, wherein the computing device (101, 201, 301) is further configured to adapt the model parameters iteratively based on a transformation of the modeled second measured variable (208, 308) and the acquired second measured variable (103, 203, 303) into the time domain, or based on a transformation of the modeled first measured variable and the acquired first measured variable (102, 202, 302) into the time domain. [12] System (100, 200, 300) according to one of claims 3 to 10, wherein the computing device (101, 201, 301) is further configured to determine a characteristic portion of the first measured variable (102, 202, 302) and the second measured variable (103, 203, 303) and to determine the correction parameter (104, 204, 304) based on the determined characteristic portions. [13] System (100, 200, 300) according to one of claims 3 to 12, wherein the computing device (101, 201, 301) is further configured to determine the correction parameter (104, 204, 304) based on a cross-correlation. [14] System (100, 200, 300) according to one of the preceding claims, wherein the computing device (101, 201, 301) is further configured to determine the correction parameter (104, 204, 304) based on a trained algorithm. [15] System (100, 200, 300) according to one of claims 3 to 14, wherein the computing device (101, 201, 301) is further configured to determine, in particular iteratively, one of the following variables based on a vertical distance between the detected first measured variable (102, 202, 302) and the modeled first measured variable or between the detected second measured variable (103, 203, 303) and the modeled second measured variable: an effective inductance of a measurement setup in which the high-performance semiconductor (198-1, 198-2) to be tested is located; and a value of a rise rate of the high-performance semiconductor under investigation (198-1, 198-2). [16] System (100, 200, 300) according to one of the preceding claims, wherein the computing device (101, 201, 301) is further configured to determine the correction parameters (104, 204, 304) as a time offset between the first measured variable (102, 202, 302) and the second measured variable (103, 203, 303). [17] System (100, 200, 300) according to one of the preceding claims, wherein the computing device (101, 201, 301) is further configured to determine the correction parameters (104, 204, 304) as a deembedding parameter set. [18] System (100, 200, 300) according to one of the preceding claims, further comprising: the first measuring probe (112, 312) and the second measuring probe (113, 313); a measuring device (314) coupled to the first measuring probe (112, 312) and the second measuring probe (113, 313); wherein the measuring device (314) is coupled to the computing device (101, 201, 301) or comprises the computing device (101, 201, 301); and wherein the measuring device (314) is designed to carry out the measurement of switching parameters (317) on the high-performance semiconductor (198-1, 198-2) based on the determined correction parameters (104, 204, 304). [19] System (100, 200, 300) according to claim 18, wherein the measuring device (314) is designed to adapt at least one input signal for the measurement of the switching parameters (317). [20] System (100, 200, 300) according to one of the preceding claims 18 and 19, wherein the first measuring probe (112, 312) and the second measuring probe (113, 313) are arranged at spatially different measuring points. [21] System (100, 200, 300) according to one of the preceding claims 18 to 20, wherein at least one further measuring probe is provided; and wherein the computing device (101, 201, 301) is further configured to determine the correction parameters (104, 204, 304) additionally based on a further measured variable detected by the at least one further measuring probe. [22] System (100, 200, 300) according to one of the preceding claims, wherein the measuring probes (312, 313) each comprise a measuring probe from the group of: shunt resistance probes; Hall effect probes; Rogowski coil probes; Transformer-based measuring probes; quantum sensor-based measuring probes; and optically isolated probes. [23] System (100, 200, 300) according to one of the preceding claims, wherein the computing device (101, 201, 301) is further configured to determine the correction parameters (104, 204, 304) further based on known probe parameters for the first measuring probe (112, 312), or known probe parameters for the second measuring probe (113, 313), or known probe parameters for the first measuring probe (112, 312) and the second measuring probe (113, 313). [24] System (100, 200, 300) according to claims 3 and 23, wherein the computing device (101, 201, 301) is further configured to determine the probe parameters for the first measuring probe (112, 312), or the probe parameters for the second measuring probe (113, 313), or the probe parameters for the first measuring probe (112, 312) and the second measuring probe (113, 313) from a model for determining the modeled first measured variable or the modeled second measured variable.

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