JP2011117729A - Signal specifying device and method, and signal specifying program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a signal specifying device and a method useful in supporting specifying an unwanted electromagnetic field or the like; and to provide a signal specifying program. <P>SOLUTION: Correlation source data and measurement conditions input from an input part 11 are stored in a data storage part 15 under the control of a control part 12. The control part 12 passes data obtained by controlling a probe scanning part 13 based on the stored measurement conditions to an amplitude probability distribution measurement part 14. The amplitude probability distribution measurement part 14 measures amplitude probability distribution, and stores it in the data storage part 15. A calculation analysis part 16 calculates a parameter by fitting a mathematical model for the amplitude probability distribution data stored in the data storage part 15 under the control of the control part 12. The degree of correlation between the correlation source data and the amplitude probability distribution data is calculated on the basis of the parameter obtained about the amplitude probability distribution data and the correlation source data. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a signal specifying device and method and a signal specifying control program, and more particularly to a signal specifying device and method and a signal specifying control program useful for specifying a location where noise or the like occurs.

Since unnecessary electromagnetic waves (electromagnetic noise) are generated in electronic devices, there has been a demand for measures for reducing unnecessary electromagnetic waves in the technical field of EMI (Electromagnetic Interference), and technical development thereof has been advanced.
This is required because unnecessary electromagnetic waves cause wireless communication failures and malfunctions of electronic devices.
In order to reduce unnecessary electromagnetic waves, it is necessary to identify the circuit that causes the generation.

  Patent Document 1 discloses a related technique for measuring an electromagnetic field distribution of an electronic device and specifying an electromagnetic field generation source. In Patent Document 1, the electromagnetic field distribution is measured with an amplitude probability distribution (APD: Amplitude Probability Distribution) representing a time probability that the noise intensity exceeds a predetermined level as a relational expression between time and probability. The measured amplitude probability distribution represents an intensity distribution at a specified probability, and as a result, Patent Document 1 obtains an electromagnetic field distribution that takes into account temporal variations.

  Further, Patent Document 2 discloses a related technique for obtaining information on the influence of unnecessary radiation noise on the transmission / reception function of an electronic device by correlating the antenna electromagnetic field distribution with the near-substrate electromagnetic field distribution which is unnecessary radiation noise. ing.

International Publication No. W02006 / 0775584 JP 2007-192744 A

As described above, Patent Document 1 provides related technology that is useful for specifying the degree to which an unnecessary electromagnetic wave is given to an object to be measured by measuring an electromagnetic field distribution that takes time fluctuations into account with an amplitude probability distribution down. I can say that.
However, it is difficult to specify the cause of the unnecessary electromagnetic field only by the intensity distribution indicated by the electromagnetic field distribution. Even if noise information in which time fluctuation is taken into account in the electromagnetic field intensity is obtained, an unnecessary electromagnetic field is not necessarily radiated from the electromagnetic field generating part where the electromagnetic field intensity indicated by the electromagnetic field distribution is large. This is not always the case.
Therefore, even if a strong electromagnetic field can be found on the electromagnetic field distribution, it cannot be identified as the cause, and there may be a cause in another part.
For these reasons, the actual situation is that the causal site has been identified by experience while referring to the intensity distribution.

  In addition, the related art disclosed in Patent Document 2 merely correlates the antenna electromagnetic field distribution and the near-substrate electromagnetic field distribution that is unwanted radiation noise, and is a correlation only for the intensity of the electromagnetic field, There is a technical problem similar to the related art disclosed in Patent Document 1.

  The present invention has been made in view of the above-described circumstances, and is a signal identification device that can be useful for identifying a noise source or the like based on correlation target data and correlation source data that has a predetermined correspondence with the correlation target data. And a method and a signal specific control program.

  In order to solve the above-described problem, a first configuration of the present invention relates to a signal specifying device, a measuring unit that outputs a signal measured for each signal generation position in a measurement target device, and a signal output from the measuring unit. First data generation means for deriving correlation target data based on a signal to be output, and second source for outputting correlation source data having a predetermined correspondence with the correlation target data output from the first information output means From the data generation means, the fitting means for fitting a mathematical model to the correlation target data and the correlation source data, the parameter derivation means for deriving a plurality of parameters obtained from the fitting by the fitting means, and the parameter derivation means An operation for calculating the degree of correlation between the correlation target data and the correlation source data based on the plurality of output parameters. It is characterized in that it comprises a means.

  A second configuration of the present invention relates to a signal specifying method, outputs a signal measured for each signal generation position in a measurement target device, generates correlation target data based on the output signal, and is derived. Correlation source data having a predetermined correspondence with the correlation target data is generated, a mathematical model is fitted to the correlation target data and the correlation source data, and a plurality of parameters obtained from the fitting are extracted and extracted. The degree of correlation between the correlation target data and the correlation source data is calculated based on the plurality of parameters.

  According to the configuration of the present invention, the correlation target data is generated based on the signal measured for each signal generation position in the signal measuring device, and the correlation target data and the correlation corresponding to the correlation target data have a predetermined correspondence relationship. Fitting a mathematical model to the original data, extracting a plurality of parameters from the fitting, and obtaining the correlation between the correlation target data and the correlation source data based on the extracted plurality of parameters to identify the signal Therefore, it is possible to improve the specific accuracy of noise or the like contained in the signal, and to assist the support.

It is a block diagram which shows the electrical constitution of the noise distribution measuring apparatus which is Embodiment 1 of this invention. It is a flowchart which shows the process sequence of the same noise distribution measuring apparatus. It is a figure which shows an example of the time series signal used with the mathematical model used with the same noise distribution measuring apparatus. It is a figure which shows the other example of the time series signal used with the mathematical model used by the process of the noise distribution measuring apparatus of Embodiment 2 of this invention. It is a figure which shows the electrical constitution of the noise distribution measuring apparatus which is Example 1 of this invention. It is a figure which shows the measurement process sequence of the noise distribution measuring apparatus. It is a block diagram of the measuring object apparatus of the same noise distribution measuring apparatus. It is a figure which shows the example which fitted the noise distribution measured about the measuring object apparatus shown in FIG. 7 with the simplest mathematical model. It is a distribution map which shows the output result at the time of calculating | requiring a similarity based on the parameter p. It is a distribution map which shows the output result at the time of calculating | requiring a similarity based on the parameter a. It is a distribution map which shows the output result at the time of calculating | requiring a similarity based on the parameter m.

Embodiment 1

1 is a block diagram showing an electrical configuration of a noise distribution measuring apparatus according to a first embodiment of the present invention, FIG. 2 is a flowchart showing a processing procedure of the noise distribution measuring apparatus, and FIG. 3 shows the noise distribution. It is a figure which shows an example of the time series signal used with the mathematical model used with a measuring apparatus.
A noise distribution measuring apparatus 10A according to this embodiment relates to an apparatus that obtains and displays a correlation value between amplitude probability distribution data on a virtual plane that is a predetermined distance away from the upper surface of a measurement target apparatus and input correlation source data. 1, the input unit 11, the control unit 12, the probe scanning unit 13, the amplitude probability distribution measurement unit 14, the data storage unit 15, the arithmetic analysis unit 16, and the output unit 17 are roughly configured. ing. This configuration is one specific configuration example of the signal specifying device.
The calculation analysis unit 16 includes a fitting unit 16a, a parameter extraction unit 16b, and a correlation calculation unit 16c.

The input unit 11 includes the frequency of the electromagnetic field to be measured (specifically, the same frequency common to each measurement position), measurement position information (measurement conditions), and correlation source data (amplitude probability distribution) to be correlated. Data) is input to the control unit 12. Here, the correlation source data is known amplitude probability distribution data that is measured in advance for a sample or is obtained in advance by simulation, or is theoretically calculated in advance and input from the input unit 11.
The control unit 12 controls the probe scanning unit 13, the amplitude probability distribution measurement unit 14, the data storage unit 15, the operation analysis unit 16, and the output unit 17.
The probe scanning unit 13 includes a probe that measures an electromagnetic field and a stage that scans the probe. The probe includes a loop antenna for measuring an electromagnetic field. The stage is a three-axis stage that can move the loop antenna to a position set by the control unit 12.

The amplitude probability distribution measuring unit 14 is connected to the probe of the probe scanning unit 13 and is a means for measuring an electromagnetic field received by the probe and calculating an amplitude probability distribution, and has, for example, an amplitude probability distribution display function. It consists of a spectrum analyzer.
The data storage unit 15 stores amplitude probability distribution data (also referred to as amplitude probability distribution data) (one example of correlation target data) measured by the amplitude probability distribution measurement unit 14, and also includes a parameter extraction unit 16b described later. The parameter data of the extracted parameter group and the correlation data calculated by the correlation calculation unit 16c are stored.

  The fitting unit 16a of the arithmetic analysis unit 16 fits the amplitude probability distribution data stored in the data storage unit 15 with a mathematical model. The parameter extraction unit 16b extracts a parameter group that characterizes the amplitude probability distribution obtained by the fitting in the fitting unit 16a. The correlation degree calculation unit 16c calculates the degree of correlation between the correlation source data input by the input unit 11 and the amplitude probability distribution data obtained by measurement using the parameter group obtained by fitting.

なお、式（１）の関数Ｑは、式（２）で定義される誤差関数である。

この式（２）により振幅確率分布をフィッティングしたときの、振幅確率分布についてのパターン情報は、パラメータ群ａ、ｐ、ｍ、σで表すことができる。
出力部１７は、測定対象装置の上面から所定距離離れた仮想平面上の相関値の分布を表示する手段である。その表示は、相関値で色分けした図や等高線等の平面上で大小を視認し得る情報を出力して行う。 In the mathematical model used in the fitting unit 16a, the amplitude information of the time series signal is examined in the calculation of the amplitude probability distribution. Therefore, the process is expressed numerically, and the final amplitude probability distribution is expressed by the mathematical expression. .
For the sake of convenience of explanation, the example will be described taking the simple time series signal (one amplitude model) shown in FIG. 3 as an example. This time-series signal has one type of intensity amplitude, its size is a, its entire period is divided into N sections, and the rate at which the signal level is on (high level) is p = n / N. Here, n represents the total time of the signal level being on, and assuming that Gaussian noise having an average of m and a variance of σ is added to the time series signal, the signal for the total time series signal is When the ratio in which the amplitude of A is greater than or equal to A is calculated, Equation (1) is obtained.

In addition, the function Q of Formula (1) is an error function defined by Formula (2).

The pattern information about the amplitude probability distribution when the amplitude probability distribution is fitted by this equation (2) can be represented by parameter groups a, p, m, and σ.
The output unit 17 is a means for displaying the distribution of correlation values on a virtual plane that is a predetermined distance away from the upper surface of the measurement target device. The display is performed by outputting information that can be visually recognized on a plane such as a figure or a contour line color-coded by correlation values.

  The input unit 11, control unit 12, probe scanning unit 13, amplitude probability distribution measurement unit 14, data storage unit 15, operation analysis unit 16, and output unit 17 described above are configured under program control. Is stored in a memory or a storage device (not shown), and the stored program is read and executed by a CPU (Central Processing Unit) or processor (not shown) in the memory of the CPU or processor. By this execution, the operation of each unit described above is sequentially performed.

Next, the operation of this embodiment will be described with reference to FIGS.
Prior to starting measurement by the noise distribution measuring apparatus 10 </ b> A, measurement position information and correlation source data necessary for measurement are input to the control unit 12 from the input unit 11.
When measurement is started after this input, the control unit 12 moves the probe to the measurement position with the input measurement position information (for example, coordinate information and step distance of the start point and end point) downward (FIG. 2). Step S21). Then, the control unit 12 sends the amplitude probability distribution measurement start signal and the input measurement conditions (for example, frequency and measurement time) to the amplitude probability distribution measurement unit 14.

The amplitude probability distribution measurement unit 14 measures the amplitude probability distribution according to the measurement conditions at the measurement position described above (step S22 in FIG. 2). The measured amplitude probability distribution data is stored in the data storage unit 15 (step S23 in FIG. 2).
Following this storage, if there is measurement position information that has not been measured in the input measurement position information (No in step S24 in FIG. 2), the control unit 12 controls the probe scanning unit 13 to measure the probe. The amplitude probability distribution is measured in the same manner as described above by moving to an incomplete measurement position.

If the amplitude probability distribution has been measured for all the measurement positions (Yes in step S4 in FIG. 2), the fitting unit 16a fits the amplitude probability distribution data stored in the data storage unit 15 with a mathematical model, The parameter extraction unit 16b extracts a parameter group from the fitting data and stores it in the data storage unit 15 (step S25 in FIG. 2).
The correlation degree calculation unit 16c calculates the degree of correlation between the amplitude probability distribution data on the virtual plane and the correlation source data input from the input unit 11 using the parameter group obtained by the fitting. The obtained correlation degree is stored in the data storage unit 15 under the control of the control unit 12 (step S26 in FIG. 2). One example of the calculation of the correlation degree is as follows. For example, the degree of correlation is obtained by comprehensive correlation calculation between a plurality of parameters obtained for the correlation source data and a plurality of parameters obtained for the amplitude probability distribution data being measured. For example, the degree of correlation described above is calculated based on the distance between different parameters in the multidimensional space represented by each parameter.

  For each parameter used in the calculation, the importance of the parameter may be different for each parameter. Therefore, in that case, the importance for each parameter can be emphasized by putting an anisotropic metric in the space or adding anisotropy to the distance function.

The correlation distribution on the virtual plane stored in the data storage unit 15 is output and displayed on the output unit 17 (step S27 in FIG. 2). The display is performed using a distribution map that is color-coded according to the degree of correlation, or a distribution map in which the size on a plane such as a contour line can be visually recognized.
From the display using the correlation degree of the amplitude probability distribution described above, it can be determined whether or not the noise is caused by the same noise generation. This judgment can be obtained because the electromagnetic field measured from the noise caused by the same noise generation exhibits a very close amplitude probability distribution even if the intensity is different.
Also, instead of comparing the shape of the measured amplitude probability distribution data with the correlation source data, the amplitude probability distribution is mathematically modeled, and the degree of correlation is calculated using the parameters of the mathematical model. It has the advantage of being less susceptible to artifacts that have nothing to do with essence. Further, since several types of parameters are extracted from the image data, there is an advantage that the amount of data is compressed.

  As described above, according to the configuration of this embodiment, the degree of correlation between the amplitude probability distribution data on the virtual plane that is a predetermined distance away from the measurement target device and the input correlation source data is obtained from the parameters extracted for both data, and Since the correlation data is displayed, even if unnecessary electromagnetic waves are generated, which makes it difficult to identify the cause of noise generation by means of using only the electromagnetic field intensity distribution, a powerful support means for identifying such electromagnetic fields is provided. Can be provided.

Embodiment 2

FIG. 4 is a diagram showing one example of a signal time series used in the mathematical model used in the processing of the noise distribution measuring apparatus according to the second embodiment of the present invention.
The electrical configuration of the noise distribution measuring apparatus according to the second embodiment of the present invention is substantially the same as that of the first embodiment. However, as a mathematical model used in the fitting unit constituting the arithmetic analysis unit in this embodiment, the equation (3 ) Is different from the first embodiment. This configuration is one specific configuration example of the signal specifying device.

This mathematical model (3) is a mathematical model in the case of a time-series signal having two types of amplitudes as shown in FIG. When the mathematical model (3) is used, a larger number of parameters can be obtained than when the mathematical model used in the first embodiment is used, and the degree of correlation by comprehensive correlation calculation between these parameters, for example, each parameter is expressed. The degree of correlation described above can also be expressed by the distance between different parameters in a multidimensional space.
Even in this case, since the importance of each parameter may be different, in that case, by adding an anisotropic metric on the space or adding anisotropy to the distance function, The importance for each parameter can be further emphasized.

  According to the configuration of this embodiment, the degree of correlation can be further emphasized, and a support means that can more strongly identify the unnecessary electromagnetic field can be provided.

Embodiment 3

The electrical configuration of the noise distribution measuring apparatus according to the third embodiment of the present invention is substantially the same as that of the first embodiment. However, the calculation by the correlation calculation unit 16c constituting the calculation analysis unit 16 in the first embodiment is performed. Any one of parameters a, p, m, and σ obtained by fitting the measured amplitude probability distribution data with the mathematical model (1), and a parameter obtained by fitting the correlation source data with the mathematical model (1) The difference lies in taking the difference from one of a, p, m, and σ as the similarity. This configuration is one specific configuration example of the signal specifying device.
Other points are the same as in the first embodiment.

  Even in the configuration of this embodiment, the similarity between the amplitude probability distribution data on the virtual plane that is a predetermined distance away from the measurement target device and the input correlation source data is obtained from the parameters extracted for both data, and the similarity data Even if unnecessary electromagnetic waves that make it difficult to identify the cause of noise generation by means that use only the electromagnetic field intensity distribution are provided, the support means necessary to identify such electromagnetic fields is provided. be able to.

  5 is a diagram showing the electrical configuration of the noise distribution measuring apparatus according to the first embodiment of the present invention, FIG. 6 is a diagram showing a measurement processing procedure of the noise distribution measuring apparatus, and FIG. 7 is the noise distribution measuring apparatus. FIG. 8 is a diagram showing an example of fitting the amplitude probability distribution measured for the measurement target device shown in FIG. 7 with the simplest mathematical model, and FIG. 9 is based on the parameter p. 10 is a distribution diagram showing the output result when the similarity is obtained, FIG. 10 is a distribution diagram showing the output result when the similarity is obtained based on the parameter a, and FIG. 11 is a similarity based on the parameter m. It is a distribution map which shows the output result when calculating | requiring.

The noise distribution measuring apparatus 10B of this example is a specific example of the third embodiment. It differs from the noise distribution measuring apparatus 10 in the embodiment shown in FIG. It is in the similarity calculation unit 16d of the calculation analysis unit 18 shown in FIG. The similarity calculation unit 16d calculates the similarity using any one parameter of the parameter groups a, p, m, and σ extracted by the parameter extraction unit 16b (see also FIG. 1).
As shown in FIG. 7, the measurement target apparatus 501 in this embodiment includes a board 502 and sub boards 503 and 504, and five LSIs, that is, LSI 504 to LSI 508, are mounted on the board 502. Yes. The size of the board 502 of the measurement target device 501 is about 255 mm in width and about 270 mm in length.
Other configurations in the configuration of this example are the same as the configurations of the first embodiment, and thus the same reference numerals are given and the explanation thereof is omitted.

Next, the operation of this embodiment will be described with reference to FIGS.
The measurement procedure in the noise distribution measuring apparatus 10B in this example is basically the same as that in the third embodiment, and when measurement is started, the frequency to be measured and the measurement from the input unit 11 (see FIG. 5). Enter the position, measurement time, similarity calculation source data, etc.
In this embodiment, the frequency to be measured is a 20 GHz band centered on 2.412 GHz, the measurement time is 100 ms, the measurement position is a virtual plane 30 mm above the apparatus, and a size range of 20 mm in the front, rear, left, and right directions (270 mm × 255 mm) was defined as step 15 mm (18 × 17 points). As an example of the similar calculation source data, amplitude probability distribution data at a point of the LSI 504 is used. Other similar calculation source data include, for example, far field amplitude probability distribution data.

Under the input measurement conditions, the control unit 12 (FIG. 5) controls the probe scanning unit 13 (FIG. 5). The probe scanning unit 13 is a three-axis stage that can move along three axes, up, down, front, back, left, and right, and a sleeve antenna is connected to the tip of the stage as a probe that measures an electric field.
The probe scanning unit 13 moves the sleeve antenna between measurement positions in response to a signal from the control unit 12. The measurement start position is moved to the left front end of the measurement range (S61 in FIG. 6).

After the movement is completed, the control unit 12 outputs a measurement start signal to the amplitude probability distribution measurement unit 14 (FIG. 5). When receiving the measurement start signal, the amplitude probability distribution measurement unit 14 starts measurement, and measures the amplitude probability distribution during the measurement time input from the input unit 11 (S62 in FIG. 6).
After the measurement, the measured amplitude probability distribution data is stored in the data storage unit 15 (FIG. 5) (S63 in FIG. 6).

Next, if there is a measurement position that has not been measured yet (No in S64 in FIG. 6), the probe is moved to that measurement position (S61 in FIG. 6), and the measurement is repeated (S62 to S63 in FIG. 6).
After the measurement of all measurement positions is completed (Yes in S64 in FIG. 6), the calculation analysis unit 18 calculates the measurement data (amplitude probability distribution data) stored in the data storage unit 15 and the input similar calculation source data. Corresponding parameter differences (features) are extracted, and the similarity is calculated (S65, S66 in FIG. 6).

The fitting in the fitting unit 16a of the calculation analysis unit 18 in this embodiment was performed on the amplitude probability distribution data for one amplitude model. An example of the fitting is shown in FIG. In FIG. 8, the horizontal axis represents amplitude, and the vertical axis represents probability. From FIG. 8, this system can represent amplitude probability distribution data measured sufficiently accurately by a single amplitude model. The parameter extraction unit 16b extracts parameters (a, p, m, σ) for each measurement patch (measurement position) using the fitting data obtained by the fitting in the fitting unit 16a. This parameter is stored in the data storage unit 15 (S65 in FIG. 6).
Then, the similarity calculation unit 16c of the calculation analysis unit 18 compares any one of the parameter groups stored in the data storage unit 15 with the corresponding parameter in the parameter group for the similar calculation source data, for example, The difference is calculated by calculating the difference between the two parameters. The calculated similarity is stored in the data storage unit 15 (S66 in FIG. 6).

After the calculation is completed, an image in which the similarity is color-coded is displayed on the screen of the output unit 17 (S67 in FIG. 6). Display examples of the similarity are shown in FIGS. FIG. 9 shows the degree of similarity with the similar calculation source data for the parameter p that bears the signal information most strongly. As described above, the degree of similarity is expressed by taking the difference between the parameter p for the measured amplitude probability distribution data and the parameter p for the similarity calculation source data. Since the difference is taken, the closer the value is to 0, the higher the degree of similarity (measurement patch). This portion is shown shaded in FIG. Speaking of this in terms of the degree of similarity shown on the right side of FIG. 9, a portion between 0.009200 and -0.001600 is high.
From the result shown in FIG. 9, the similarity with the active component periphery is extracted.

Similarly, display examples of similarity for other parameters are shown in FIGS. FIG. 10 is a display example for the parameter a, and FIG. 11 is a display example for the parameter m.
Speaking from the meaning of the parameters, a represents information on the logic component of the time-series signal, and m and σ represent information on the stochastic component of the time-series signal. Speaking of this display content with reference to FIG. 10 and FIG. 11, in FIG. 10, the area represented by the similarity between the similarity values 23.50 and 21.19 shown on the right side thereof shows a portion where the noise of the logic component is high, In FIG. 11, the region represented by the similarity between the similarity numerical values −73.29 and −73.71 shown on the right side indicates a portion where the noise for the stochastic component is high.
In addition, for these pieces of information, the relationship between the parameter for the measurement data and the parameter for the similar calculation source data can also be seen, but by looking at the above distribution itself, the strong logic information, It is also possible to obtain environmental information of noise on the board where the stochastic component is strong.
The effect of the third embodiment can be obtained by the configuration of this example.

Although the embodiments and examples of the present invention have been described in detail with reference to the drawings, the specific configuration of the present invention is not limited to these embodiments and examples, and the gist of the present invention. Even if there is a design change or the like without departing from the scope of the invention, they are included in the present invention.
For example, although the correlation source data or the similar calculation source data has been described as known amplitude probability distribution data, the amplitude probability on the virtual plane measured for the noise distribution measurement device at an arbitrarily selected position of the measurement target device. The distribution data can also be used as correlation source data or similar calculation source data.
Further, not only one correlation source data or similar calculation source data but also a plurality of correlation source data or similar calculation source data can be used to assist in noise identification support by utilizing the fact that the correlation or similarity is different.

In addition, although it has been explained that the measurement of the amplitude probability distribution is performed at the same frequency common to each measurement position, the amplitude probability distribution data is obtained at different frequencies and the degree of correlation or similarity between the data is obtained to determine the cause of noise generation. May be used to specify which part of the search target is located.
A magnetic field may be used for measuring the amplitude probability distribution.
Further, in addition to specifying the noise source, it may be configured to be used for specifying or determining the generation location or output location of one-dimensional information such as other signals and pattern information or multi-dimensional information. The signal is, for example, one-dimensional information such as a cipher, a signal representing temperature and pressure, and an audio signal. The pattern information is multidimensional information such as graphic information.
Further, the noise distribution measuring apparatus is configured under program control in the above-described example, but the present invention can be implemented even if the essence of the control logic is replaced by all or part of the hardware. Can do.
This specific configuration change is also valid in apparatuses other than the noise distribution measurement described above.

  The signal identification device and method and the signal identification control program disclosed herein can be used for various devices that require identification of signals other than noise and pattern information, such as authentication devices.

10, 10A Noise distribution measuring device (signal identification device)
11 Input unit (part of first data generation means, part of second data generation means)
12 control unit (part of first data generation means, part of second data generation means)
13 Probe scanning unit (measuring means)
14 Amplitude probability distribution measurement unit (part of first data generation means, part of second data generation means)
15 Data storage (remaining part of the first data generating means, remaining part of the second data generating means)
16a Fitting part (fitting means)
16b Parameter extraction unit (parameter extraction means)
16c Correlation degree calculation part (calculation means)
16d Similarity calculation unit (calculation means)
17 Output unit (output means)

Claims (20)

Measuring means for outputting a signal measured for each signal generation position in the measurement target device;
First data generation means for deriving correlation target data based on a signal output from the measurement means;
Second data generation means for outputting correlation source data having a predetermined correspondence with the correlation target data output from the first information output means;
Fitting means for fitting a mathematical model to the correlation target data and the correlation source data;
Parameter extraction means for extracting one or more parameters obtained from the fitting by the fitting means;
A signal specifying device comprising: an operation unit that calculates a degree of correlation between the correlation target data and the correlation source data based on the one or more parameters output from the parameter extraction unit.   The signal specifying device according to claim 1, wherein the signal is a signal on which a noise signal output from a noise source is superimposed.   The signal specifying apparatus according to claim 1, wherein the correlation target data and the correlation source data are amplitude probability distribution data.   4. The signal specifying apparatus according to claim 1, wherein the correlation degree is obtained from a comprehensive correlation calculation between different parameters among the plurality of parameters.   5. The signal specifying apparatus according to claim 1, wherein the comprehensive correlation calculation obtains a distance between different parameters in a multidimensional space represented by the plurality of parameters.   The calculation means is extracted by fitting the mathematical model to the correlation source data and any one of the plurality of parameters extracted by fitting the mathematical model to the correlation target data. 6. The signal specifying device according to claim 1, wherein the signal specifying device calculates a difference between the one of the plurality of parameters and one parameter of the same type as a similarity. 7. .   The signal specifying apparatus according to claim 1, wherein the correlation source data is data measured for an arbitrary signal generation position in a known apparatus or a measurement target apparatus.   The signal specifying device according to claim 1, wherein the correlation target data and the correlation source data are obtained under the same or different conditions.   The signal specifying device according to claim 1, further comprising an output unit that outputs the degree of correlation or the degree of similarity calculated by the calculating unit. Output a signal measured for each signal generation position in the measurement target device,
Generating correlation target data based on the output signal;
Generating correlation source data having a predetermined correspondence with the derived correlation target data;
Fitting a mathematical model to the correlation target data and the correlation source data,
Extracting one or more parameters obtained from the fitting;
A signal specifying method, wherein a correlation degree between the correlation target data and the correlation source data is calculated based on the extracted parameter or parameters.   The signal specifying method according to claim 10, wherein the signal is a signal on which a noise signal output from a noise source is superimposed.   12. The signal specifying method according to claim 10, wherein the correlation target data and the correlation source data are amplitude probability distribution data.   The signal specifying method according to claim 10, wherein the correlation degree is obtained from a comprehensive correlation calculation between the plurality of parameters.   The signal specifying method according to claim 10, wherein the comprehensive correlation calculation obtains a distance between different parameters in a multidimensional space represented by the plurality of parameters.   The calculation is extracted by fitting the mathematical model to the correlation source data with any one of the plurality of parameters extracted by fitting the mathematical model to the correlation target data. The signal specifying method according to any one of claims 10 to 14, wherein a difference from one parameter corresponding to any one of the plurality of parameters is calculated as a similarity.   The signal specifying method according to claim 10, wherein the correlation source data is data measured for any of the signal generation positions in a known device or a measurement target device.   The signal specifying method according to claim 10, wherein the correlation target data and the correlation source data are obtained under the same or different conditions.   The signal specifying method according to any one of claims 10 to 17, wherein the calculated degree of correlation or degree of similarity is output to an output unit and displayed.   A control program for causing a computer to control the signal specifying device according to any one of claims 1 to 9.   A control program for causing a computer to execute the signal specifying method according to any one of claims 10 to 18.

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