Electromagnetic noise analysis apparatus and electromagnetic noise analysis method

Abstract

To provide an electromagnetic-noise analysis device that can analyze propagation paths in a short time even with respect to electromagnetic noise in high-frequency bands, and can ensure a definite level of reliability in the analysis results.SOLUTION: An electromagnetic-noise analysis device is provided with: a propagation-characteristics estimation unit that, on the basis of information on casing structure and wiring paths, statistically estimates electromagnetic-field characteristics regarding the inside the casing, and outputs propagation-characteristics probability distribution data; a propagation-quantity computing unit that, on the basis of the frequency characteristics of noise emitted by a noise source and the propagation-characteristics probability distribution data, statistically estimates the quantity of noise propagated from the noise source to a harmed device, and outputs harmed-device noise-probability distribution data; and a malfunction risk determination unit that, on the basis of the harmed-device noise-probability distribution data, determines a malfunction risk for the harmed device.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to an electromagnetic noise analysis device and an electromagnetic noise analysis method.

Background Art

[0002] In a system combining a plurality of electrical devices, there is a possibility that another electrical device malfunctions due to the influence of electromagnetic noise generated from a certain electrical device. Therefore, EMC (Electro-Magnetic Compatibility) design is carried out. Also, as one of the items of EMC design, for wiring inside a housing such as a vehicle body, a route design considering resistance to electromagnetic noise is required. Regarding the analysis technology of electromagnetic noise, for example, Patent Document 1 is known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional wiring route design, for example, as in Patent Document 1, normal electromagnetic field analysis, that is, mainly an analysis is performed in which a three-dimensional model of a housing constituting a vehicle body or the like is divided into a mesh shape, and the current value propagated from a noise source is calculated for each mesh. However, when the frequency of the signal used is high (when the wavelength is short), the amount of meshes needs to be increased, so the analysis time becomes long. In particular, in recent years, since the operating frequency has been increased to the GHz range, the analysis time tends to be even longer. Also, in general electromagnetic field analysis, if there is a deviation between the three-dimensional shape of the modeled housing and the actual shape, the reliability of the analysis result may decrease.

[0005] The object of the present invention is to provide an electromagnetic noise analysis device that can analyze the propagation path of electromagnetic noise in the high-frequency band in a short time and ensure a certain level of reliability in the analysis results. [Means for solving the problem]

[0006] To solve the aforementioned problems, the electromagnetic noise analysis device of the present invention comprises: a propagation characteristic estimation unit that statistically estimates the electromagnetic field characteristics inside the housing and outputs propagation characteristic probability distribution data based on information on the housing structure and the wiring path; a propagation amount calculation unit that statistically estimates the amount of noise propagation from the noise source to the damaged device and outputs noise probability distribution data on the damaged device based on the frequency characteristics of the noise emitted by the noise source and the propagation characteristic probability distribution data; and a malfunction risk determination unit that determines the risk of malfunction of the damaged device based on the noise probability distribution data on the damaged device. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide an electromagnetic noise analysis device that can analyze the propagation path of electromagnetic noise in the high-frequency band in a short time and ensure a certain level of reliability in the analysis results.

[0008] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0009] [Figure 1] A diagram showing the overall configuration of the computer system in Example 1. [Figure 2] A diagram showing the overall flow of electromagnetic noise analysis in Example 1. [Figure 3] This diagram shows the specific calculation flow in the dashed line portion of Figure 2. [Figure 4] This figure shows an example of data for the propagation characteristic probability distribution between a noise source and a damaged device. [Figure 5] Graph of Figure 4 viewed from the z-axis direction. [Figure 6] A diagram showing the overall image of the electromagnetic noise analysis in Example 2. [Figure 7] A flowchart illustrating the processing performed by the malfunction risk determination unit according to Example 2. [Figure 8] A diagram showing an example of the judgment result by the malfunction risk assessment unit. [Figure 9] A diagram showing the overall image of the electromagnetic noise analysis in Example 3. [Figure 10] A diagram showing the overall image of the electromagnetic noise analysis in Example 4. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. The embodiments are illustrative examples for explaining the present invention, and have been omitted and simplified as appropriate for clarity of explanation. The present invention can also be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0011] In embodiments, processing performed by executing a program may be described. Here, the computer executes the program using a processor (e.g., CPU, GPU) and performs processing defined by the program using memory resources (e.g., memory) and interface devices (e.g., communication ports). Therefore, the main entity performing the processing by executing the program may be the processor. Similarly, the main entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The main entity performing the processing by executing the program may be an arithmetic unit, and may include dedicated circuits that perform specific processing. Here, dedicated circuits include, for example, FPGAs (Field Programmable Gate Arrays), ASICs (Application Specific Integrated Circuits), CPLDs (Complex Programmable Logic Devices), etc.

[0012] The program may be installed on the computer from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer. If the program source is a program distribution server, the program distribution server includes a processor and storage resources to store the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. In addition, in the embodiment, two or more programs may be implemented as one program, or one program may be implemented as two or more programs.

[0013] An electromagnetic noise analysis method and apparatus according to an embodiment of the present invention will be described below with reference to Figures 1 to 10. In this embodiment, the example of evaluating the EMC risk of harnesses (wiring) running through vehicles such as electric vehicles and reflecting this in the design of the wiring route will be used for the explanation.

[0014] In electric vehicles, an inverter is used to convert the direct current (DC) output from the battery into alternating current (AC) to drive the tires with a motor. The inverter is connected to the battery and also to the motor via wiring (motor cable). When an electric vehicle accelerates or decelerates, a control signal is transmitted from the ECU (Engine Control Unit) to the inverter via wiring (control cable), and the inverter performs torque control by pulse modulation according to this signal. Noise is generated during this pulse modulation of the inverter, and if this noise partially leaks to the outside, it can be carried on the vehicle's wiring as a noise signal. If this noise signal carried on the wiring overlaps with signals that control electrical devices in the vehicle, such as the ECU or GPS (Global Positioning System) antennas, these devices may malfunction. Therefore, wiring route design that takes noise immunity into consideration is necessary.

[0015] Therefore, in this embodiment, by incorporating statistical evaluation into electromagnetic field analysis, it is possible to realize a wiring path design that can shorten the analysis time while ensuring a certain level of reliability in the analysis results. Specifically, by applying the RCM (Random Coupling Model) theory, the influence of characteristic variations caused by the shape of the housing and the like is reduced.

[0016] The RCM theory is a theory that when the wavelength of high-frequency electromagnetic waves propagating in a housing with a complex shape is sufficiently small with respect to the housing (for example, 1 / 10 or less), multiple reflections occur, and after a certain period of time, it can be regarded as a random state, and the electromagnetic field can be modeled as a statistical intensity distribution. Note that for the vehicle body, since it has a non-simple shape and is sufficiently large with respect to the wavelength of electromagnetic noise, the RCM theory can be applied.

Example

[0017] The electromagnetic noise analysis method according to Example 1 is realized by a computer system. FIG. 1 is a diagram showing the overall configuration of the computer system of Example 1. As shown in FIG. 1, the computer system includes a processor 1, a storage unit 2, an input unit 3, an output unit 4, and a connection line 5 connecting these. The processor 1 is, for example, a CPU as described above. The storage unit 2 is a memory, HDD (Hard Disk Drive), or the like. The input unit 3 is a keyboard, mouse, touch panel, or the like. The output unit 4 is, for example, a display. The connection line 5 is wiring on a circuit board, a connection cord, a network, or the like. These components do not have to be in the same location and may be installed at a remote location and connected via a network or the like.

[0018] The processor 1 executes each function by reading and executing a program stored in the storage unit 2 and the like. In FIG. 1, each function executed by the processor 1 is conceptually shown as a housing structure / wire path extraction unit 101, a propagation characteristic estimation unit 102, a noise characteristic extraction unit 103, a propagation amount calculation unit 104, and a malfunction risk determination unit 105. Details of each unit will be described later.

[0019] The memory unit 2 has an analysis / measurement database 201 and a design database 202. The analysis / measurement database 201 stores damaged device data (D16) and noise source data (D13). Damaged devices are assumed to be ECUs and various antennas, and noise sources are assumed to be inverters and various sensors. Damaged device data (D16) includes, for example, data on vulnerability and data on the importance of the device obtained through testing of the device. Data on vulnerability is data on how much noise at a given frequency causes a malfunction in the damaged device, and includes the receiving sensitivity frequency characteristics and multiple thresholds (voltage thresholds and probability thresholds described later). Data on the importance of the device is data that shows the degree of risk impact for each device within the enclosure, and in the case of a vehicle, for example, the importance of the device that controls the operation of the vehicle is set higher than the importance of the car audio-related device. Noise source data (D13) is data on the noise emitted by the noise source and is acquired in advance through analysis or actual measurement. The noise included in the acquired data may be radiated noise or conducted noise.

[0020] Next, the flow of electromagnetic noise analysis in this embodiment will be explained using Figures 2 to 5. Figure 2 is a diagram showing the overall flow of electromagnetic noise analysis in Embodiment 1, and Figure 3 is a diagram showing the specific calculation flow in the dashed line portion of Figure 2.

[0021] First, as a preprocessing step for the RCM theory application process performed by the propagation characteristics estimation unit 102, the enclosure structure / wire path extraction unit 101 obtains enclosure structure data and wire path data (D10) from the design database 202 and calculates the enclosure characteristics and the propagation characteristics between each port (wire path coupling characteristics). The enclosure characteristics are obtained by the enclosure structure / wire path extraction unit 101 based on the enclosure structure data obtained from the design database 202, and include the volume of the enclosure (vehicle body) and the Q value. On the other hand, the propagation characteristics between each port are obtained by the enclosure structure / wire path extraction unit 101 based on the wire path data obtained from the design database 202, individually calculating the relationship between one port and other ports (for example, the effect of a noise source in one port on a damaged device in another port). In this calculation, only the neighboring region that affects the radiation characteristics of each port needs to be modeled, so the calculation cost can be reduced.

[0022] Next, the propagation characteristics estimation unit 102 performs processing that applies RCM theory. That is, as shown in Figure 3, the propagation characteristics estimation unit 102 generates multiple distribution patterns by random matrix calculation based on the enclosure characteristics calculated by the enclosure structure / wire path extraction unit 101, and represents the state of the electromagnetic field inside the enclosure (vehicle body) as a characteristic model of a probability distribution centered on the mode. Furthermore, the propagation characteristics estimation unit 102 estimates the propagation characteristics probability distribution using this characteristic model based on the enclosure characteristics (far-range characteristic model) and a characteristic model based on the radiation characteristics of each port and the inter-port propagation characteristics calculated by the enclosure structure / wire path extraction unit 101 (near-field characteristic model). Since this propagation characteristics probability distribution can be obtained as an approximate value without performing a precise 3D analysis, it leads to a reduction in analysis time.

[0023] Figure 4 shows an example of data for the propagation characteristic probability distribution between a noise source and a damaged device, with the x-axis representing frequency, the y-axis representing propagation characteristics, and the z-axis representing the probability distribution. Figure 5 is a graph of Figure 4 viewed from the z-axis direction, with the x-axis representing frequency and the y-axis representing propagation characteristics.

[0024] Figures 4 and 5 show that the propagation characteristics, i.e., the ease with which noise is transmitted from the noise source to the affected device, differ for each frequency, and that the probability distribution also differs. Furthermore, in Figure 4, the range of characteristics that occur above a certain probability cannot be ignored, so by defining the upper and lower limits of the propagation characteristics in a manner corresponding to that characteristic range, we can draw the upper and lower limit lines shown in Figure 5. Moreover, by connecting the peaks of the probability distribution for each frequency in Figure 4, we can also draw the mode line in Figure 5.

[0025] Meanwhile, the noise characteristic extraction unit 103 acquires noise source data (D13) from the analysis and measurement database 201 and extracts the frequency characteristics of the noise emitted by the noise source.

[0026] The propagation amount calculation unit 104 statistically estimates the amount of noise propagation from the noise source to the damaged device based on the propagation characteristic probability distribution data (D12) output by the propagation characteristic estimation unit 102 and the noise frequency characteristic data (D14) output by the noise characteristic extraction unit 103.

[0027] The malfunction risk determination unit 105 determines the risk of the damaged device malfunctioning based on the noise probability distribution data (D15) output by the propagation amount calculation unit 104 and the damaged device data (D16) output by the analysis and measurement database 201. For example, the malfunction risk determination unit 105 calculates the probability of a malfunction occurring in the damaged device based on the result of calculations such as multiplication using the receiving sensitivity frequency characteristics (information showing the relationship between how much noise in which frequency band is applied to the damaged device to cause a malfunction) included in the damaged device data (D16) and the noise probability distribution data (D15) on the damaged device. Furthermore, the malfunction risk determination unit 105 may compare the probability of a malfunction occurring with a predetermined threshold included in the damaged device data (D16) and determine that the risk is high if it is above the threshold, and low if it is below the threshold.

[0028] The results of the malfunction risk determination unit 105 are output to the design database 202 as malfunction risk data (D17). The malfunction risk data (D17) is stored in the design database 202 and, if necessary, is fed back into the SIL (Safety Integrity Level) design as a failure rate and used to update the design data. The malfunction risk data (D17) is also displayed via the output unit 4 as "An error occurs with a probability of XX during operation XX." When the risk is shown quantitatively as the probability of malfunction of the affected device in this way, it becomes useful for EMC design. [Examples]

[0029] The electromagnetic noise analysis method according to Example 2 will be explained using Figures 6 to 8. Figure 6 is a diagram showing the overall image of the electromagnetic noise analysis in Example 2. This example differs from Example 1 in the following two points.

[0030] The first difference is that the noise characteristic extraction unit 103 in this embodiment extracts multiple noise frequency characteristics for each operating state of the noise source. For example, if the noise source is an inverter of an electric vehicle, the noise characteristic extraction unit 103 distinguishes and extracts the frequency characteristics of noise generated by the switching of the inverter during acceleration operation and the frequency characteristics of noise generated by the switching of the inverter during regenerative (deceleration) operation. Graph 2A in Figure 6 shows the noise frequency characteristics in the acceleration mode as control 1 and the regenerative mode as control 2. By using different noise frequency characteristic data for each major control mode in this way, the accuracy of the noise probability distribution on the affected device obtained by the propagation amount calculation unit 104 is improved, and as a result, the accuracy of the judgment by the malfunction risk determination unit 105 is also improved. Furthermore, even during the same acceleration operation, the switching interval of the inverter differs between the low-speed and high-speed ranges, so it is desirable to use different noise frequency characteristic data for each speed range.

[0031] The second difference is that the malfunction risk determination unit 105 in this embodiment does not calculate the probability of a malfunction occurring itself, but rather determines the magnitude of the risk by comparing whether excessive noise (induced voltage) is generated and the probability of such occurrence with a voltage threshold and a probability threshold.

[0032] Figure 7 is a flowchart showing the processing performed by the malfunction risk determination unit according to Embodiment 2. As shown in Figure 7, the malfunction risk determination unit 105 in this embodiment first obtains the probability distribution of induced voltage on the damaged device (graph 2C in Figure 6) as noise probability distribution data (D15) on the damaged device from the propagation amount calculation unit 104 (step S201). Next, the malfunction risk determination unit 105 determines whether the probability distribution of induced voltage on the damaged device exceeds a predetermined voltage threshold (step S202).

[0033] In step S202, if the probability distribution of the induced voltage exceeds the voltage threshold, the malfunction risk determination unit 105 determines whether the probability of generating noise from such an induced voltage is greater than a predetermined probability threshold (step S203). If the probability of noise generation is greater than the probability threshold, the malfunction risk determination unit 105 determines that it is a high risk and outputs this to the design database 202 (step S204), and terminates the determination process.

[0034] On the other hand, in step S202, if the probability distribution of the induced voltage is below the voltage threshold, or in step S203, if the probability of noise generation is below the probability threshold, the malfunction risk determination unit 105 terminates the determination process without outputting to the design database 202. In this way, the malfunction risk determination unit 105 of this embodiment determines that even if there is a possibility of strong noise generation (exceeding the voltage threshold), if the probability of its generation is sufficiently low (not exceeding the probability threshold), the risk of the affected device malfunctioning is small. In other words, in this embodiment, the risk is determined by considering not only the intensity of the noise but also its probability of generation, so a highly accurate determination can be made, and excessive EMC design can be avoided.

[0035] The voltage threshold for the magnitude of the noise voltage and the probability threshold for the probability of noise generation are thresholds determined according to the characteristics of the damaged device and are included in the damaged device data (D16) output from the analysis and measurement database 201. Furthermore, it is desirable that the probability thresholds correspond to the failure rate reference values ​​defined in SIL.

[0036] Figure 8 shows an example of the judgment result by the malfunction risk judgment unit. The malfunction risk judgment unit 105 quantifies (visualizes) the malfunction risk, which is the judgment result, as shown in Figure 8, and outputs it to the design database 202. [Examples]

[0037] The electromagnetic noise analysis method according to Example 3 will be explained using Figure 9. Figure 9 is a diagram showing the overall image of the electromagnetic noise analysis in Example 3.

[0038] This embodiment differs from Embodiment 2 in that the noise characteristic extraction unit 103 extracts not only the frequency characteristics of the noise but also the probability of each noise occurring, and the propagation amount calculation unit 104 uses not only the frequency characteristics of the noise but also the probability of each noise occurring to estimate the amount of noise propagation. As in this embodiment, considering the probability of noise occurrence at the noise source improves the accuracy of the noise probability distribution on the affected device obtained by the propagation amount calculation unit 104, and as a result, the accuracy of the judgment by the malfunction risk determination unit 105 also improves.

[0039] Note that Graph 3A in Figure 9 is a two-dimensional graph showing only frequency (x-axis) and noise intensity (y-axis), but in reality, the probability distribution (z-axis) is also included as data with a range. Furthermore, the frequency with which control modes (such as acceleration mode or regenerative mode) are executed may be used instead of the probability of noise occurrence. [Examples]

[0040] The electromagnetic noise analysis method according to Example 4 will be explained using Figure 10. Figure 10 is a diagram showing the overall image of the electromagnetic noise analysis in Example 4.

[0041] This embodiment converts conducted noise, which arises from factors such as the left-right asymmetry of the connector shape and structure, into differential mode or common mode as appropriate during the electromagnetic noise analysis process.

[0042] As a prerequisite, when the propagation characteristic estimation unit 102 applies RCM theory, the target noise is limited to common-mode noise. Therefore, the propagation characteristic probability distribution data (D12) output by the propagation characteristic estimation unit 102 is also common-mode noise, as shown in graph 4B of Figure 10. Consequently, if the noise frequency characteristics included in the noise source data (D13) are in differential mode, as shown in graph 4A of Figure 10, the noise characteristic extraction unit 103 converts the noise frequency characteristics to common mode and outputs it to the propagation amount calculation unit 104. This allows the propagation amount calculation unit 104 to calculate the propagation amount based on the propagation characteristic probability distribution data (D12) of the common-mode noise and the noise frequency characteristic data (D14) converted to common mode.

[0043] However, in order for the malfunction risk determination unit 105 to determine the risk of malfunction in the affected device, it is necessary to convert the noise propagation amount to differential mode and evaluate it. Therefore, the propagation amount calculation unit 104 in this embodiment converts the calculated propagation amount from common mode to differential mode and outputs the converted data to the malfunction risk determination unit 105 as noise probability distribution data (D15) on the affected device.

[0044] The embodiments described above are detailed explanations provided to facilitate understanding of the present invention, and are not necessarily limited to those comprising all the described configurations. Furthermore, it is possible to replace parts of the configuration of one embodiment with those of another embodiment, and to add configurations from other embodiments to a given embodiment. Additionally, it is possible to add, delete, or replace parts of the configuration of each embodiment with those of other embodiments.

[0045] For example, in the embodiments described above, the enclosure structure / wire path extraction unit 101 extracted enclosure characteristic data and radiation characteristic data (D11) between each port, and the noise characteristic extraction unit 103 extracted noise frequency characteristic data (D14). However, if D11 and D14 are stored in advance in the design database 202 and the analysis / measurement database 201, the enclosure structure / wire path extraction unit 101 and the noise characteristic extraction unit 103 can be omitted. Also, in the embodiments described above, the noise source data (D13) and the damaged device data (D16) are stored in a common analysis / measurement database 201, but each type of data may be stored in a separate database. [Explanation of symbols]

[0046] 1…Processor, 2…Memory unit, 3…Input unit, 4…Output unit, 5…Connection lines, 101…Enclosure structure / wire path extraction unit, 102…Propagation characteristic estimation unit, 103…Noise characteristic extraction unit, 104…Propagation amount calculation unit, 105…Malfunction risk determination unit, 201…Analysis / measurement database, 202…Design database

Claims

1. Based on information on the enclosure structure and wiring paths, the system includes a propagation characteristic estimation unit that statistically estimates the electromagnetic field characteristics inside the enclosure and outputs propagation characteristic probability distribution data, and A propagation amount calculation unit statistically estimates the amount of noise propagation from the noise source to the damaged device based on the frequency characteristics of the noise emitted by the noise source and the propagation characteristic probability distribution data, and outputs noise probability distribution data on the damaged device. A malfunction risk determination unit determines the risk of malfunction of the damaged device based on the noise probability distribution data on the damaged device, An electromagnetic noise analysis device equipped with the following features.

2. In claim 1, The propagation amount calculation unit is an electromagnetic noise analysis device that estimates the noise propagation amount using multiple frequency characteristics for each operating state of the noise source.

3. In claim 1, The aforementioned propagation amount calculation unit is an electromagnetic noise analysis device that estimates the noise propagation amount using not only the frequency characteristics of the noise but also the probability of noise generation.

4. In claim 1, The malfunction risk determination unit is an electromagnetic noise analysis device that determines the malfunction risk of the damaged device using data related to the vulnerability of the damaged device.

5. In claim 4, The malfunction risk determination unit is an electromagnetic noise analysis device that outputs the probability of malfunction occurring in the damaged device based on the results of calculations using data on the vulnerability of the damaged device and noise probability distribution data on the damaged device.

6. In claim 4, The data relating to the vulnerability of the affected device includes a voltage threshold relating to the magnitude of the noise voltage and a probability threshold relating to the probability of noise occurrence. The malfunction risk determination unit is an electromagnetic noise analysis device that outputs the magnitude of the risk by comparing the noise probability distribution data on the affected device with the voltage threshold and the probability threshold.

7. In claim 1, An electromagnetic noise analysis device further comprising a noise characteristic extraction unit that converts the frequency characteristics of noise from differential mode to common mode and outputs it to the propagation amount calculation unit.

8. In claim 7, The electromagnetic noise analysis device includes a propagation amount calculation unit that converts the noise propagation amount from common mode to differential mode and outputs it to the malfunction risk determination unit as noise probability distribution data on the affected device.

9. The propagation characteristics estimation unit performs the following steps: statistically estimates the electromagnetic field characteristics inside the enclosure based on information about the enclosure structure and wiring path, and outputs propagation characteristics probability distribution data; The propagation amount calculation unit statistically estimates the amount of noise propagation from the noise source to the damaged device based on the frequency characteristics of the noise generated by the noise source and the propagation characteristic probability distribution data, and outputs noise probability distribution data on the damaged device. The malfunction risk determination unit determines the malfunction risk of the damaged device based on the noise probability distribution data on the damaged device, An electromagnetic noise analysis method equipped with [specific features / features].

10. In claim 9, The propagation amount calculation unit is an electromagnetic noise analysis method that estimates the noise propagation amount using multiple frequency characteristics for each operating state of the noise source.

11. In claim 9, The aforementioned propagation amount calculation unit is an electromagnetic noise analysis method that estimates the noise propagation amount using not only the frequency characteristics of the noise but also the probability of noise generation.

12. In claim 9, The malfunction risk determination unit is an electromagnetic noise analysis method that determines the malfunction risk of the damaged device using data related to the vulnerability of the damaged device.

13. In claim 9, An electromagnetic noise analysis method further comprising a noise characteristic extraction unit that converts the frequency characteristics of the noise from differential mode to common mode.

14. In claim 13, The propagation amount calculation unit is an electromagnetic noise analysis method that converts the noise probability distribution data on the affected device from common mode to differential mode and outputs it.

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