Patent Application
20210357811
This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2020-84147, filed on May 12, 2020, the entire contents of which are incorporated herein by reference.
The embodiment discussed herein is related to a machine learning estimation method and an information processing device.
In recent years, estimation of an electromagnetic wave radiation situation (EMI), which is radiated from a circuit, is performed using a machine learning technology such as deep learning (DL). The electromagnetic wave radiation situation indicates a situation of a far electromagnetic field, and is also referred to as a far field. For example, in an estimation method by using the DL, the electromagnetic wave radiation situation is estimated based on an electronic circuit to be analyzed, by using a learning model generated from learning data in which a learning electronic circuit and a simulation result of electromagnetic wave analysis for the electronic circuit are paired.
Related techniques are disclosed in, for example, Japanese National Publication of International Patent Application No. 2009-534854.
According to an aspect of the embodiment, a non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including identifying, for an electronic circuit to be analyzed, a resonance frequency of a current and a spatial distribution of the current that flows through the electronic circuit to be analyzed at the resonance frequency, generating a machine learning model using a training data set in which arrangements of circuit elements in respective electronic circuits differ from each other, the training data set being a set of training data in each of which a specific value of a resonance frequency for a specific electronic circuit and information of a spatial distribution of a current that flows through the specific electronic circuit at the resonance frequency of the specific value are used as input data and an electromagnetic wave radiation situation of the specific electronic circuit is set as a label, inputting, as input data, a frequency value of the identified resonance frequency and information of the identified spatial distribution to the generated machine learning model, and estimating an electromagnetic wave radiation situation of the electronic circuit to be analyzed, based on an output from the machine learning model according to the inputting.
The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention.
However, in the related art described above, a situation of a near electromagnetic field (near field) generated by various values of elements including an inductor (L), a capacitor (C), and a resistor (R) may not be accurately approximated, and thus there is a problem in that it is difficult to accurately estimate a far field in some cases.
For example, in a case where the values of L, C, and R are sufficiently large, reflection occurs due to the L, C, and R elements (hereinafter, referred to as an LCR element). Therefore, in a case where the circuit is regarded as two lines obtained by being cut at the LCR element, the near field may be approximated based on a total length of the respective lines. However, a case where the values of L, C, and R are small and the reflection is small is exactly the same as a case where the LCR element is not present, so that it is difficult to accurately approximate an approximation field.
Hereinafter, an estimation technique according to an embodiment will be described with reference to the accompanying drawings. In the embodiment, the same reference numeral is attached to a component having the same function, and the description thereof will not be repeated. The estimation program, the model generation program, the estimation method, the model generation method, the estimation device, and the model generation device, which will be described in the embodiment below, are merely examples and do not limit the embodiment. Further, the embodiment below may be appropriately combined in a scope which is not inconsistent.
First, an overview of the present embodiment, in which an electromagnetic wave radiation situation (EMI intensity) in an electronic circuit to be analyzed, will be described. One point of interest in the present embodiment is that a far field of electromagnetic wave radiation in the electronic circuit to be analyzed is determined by an approximation field, and a near field is determined by a current flowing through the circuit.
For example, in the electronic circuit, wiring serves as an antenna to generate an electromagnetic field, and a part of energy of the electromagnetic field is radiated as the EMI. An intensity of the electromagnetic field is determined by the amount of current flowing through the wiring. Therefore, the near field may be approximated by using a spatial distribution (hereinafter, also referred to as a current distribution) of the current flowing through the electronic circuit to be analyzed.
The current flowing through the electronic circuit differs for each frequency. For example, the current distribution has a distribution over the frequency. However, in a case where machine learning is performed on a relationship between the current distributions at all frequencies and the EMI intensity, there is a problem in that the number of pieces of data to be learned becomes unrealistic.
In circuit design, there is an interest in reducing a peak of an EMI spectrum. The peak of the EMI spectrum is obtained as a frequency at which a maximum value of the current distribution is the highest. A peak frequency is referred to as a resonance frequency.
Therefore, another point of interest in the present embodiment is to perform machine learning by focusing on the current distribution at the resonance frequency. For example, in a learning phase, a value of the resonance frequency of the electronic circuit and the current distribution of the electronic circuit at the resonance frequency are set as feature quantities, and the machine learning is performed on a relationship of the EMI intensity with respect to the feature quantities, so that an estimation model is generated. In an estimation phase, the value of the resonance frequency and the current distribution at the resonance frequency are identified for the electronic circuit to be analyzed, and the value of the identified resonance frequency and the current distribution are input to the estimation model, so that the estimated value of the EMI intensity is obtained based on the output from the estimation model.
Next, in the learning phase, based on the circuit information, the current distribution at each frequency in the electronic circuit is calculated by a circuit simulator such as a simulation program with integrated circuit emphasis (SPICE) (S2).
In order to approximate the near field of the electronic circuit, an output in S2 is an image on which coloring processing is performed such that the color becomes lighter as being far away from lines based on intensity information of the current distribution. For example, the output in S2 is information (image information) indicating an intensity of the current distribution that approximates the near field of the electronic circuit. The current distribution over a two-dimensional circuit has a smaller amount of calculation, compared to a three-dimensional electromagnetic field, so that the current distribution may be calculated in a much short time.
Next, in the learning phase, a resonance frequency (f1, f2) at which the maximum value of the calculated current distribution is the highest is identified, and the current distribution for each resonance frequency (f1, f2) is obtained (S3).
Next, in the learning phase, information on the identified frequency (value of the resonance frequency) is input as input data of a channel 1 (Ch. 1), and information on the current distribution at the frequency (image indicating the current distribution) is input as input data of a channel 2 (Ch. 2) to the estimation model such as deep learning (S4). For example, in S4, the resonance frequency (f1) is input to the estimation model as Ch. 1, and the current distribution at the resonance frequency (f1) is input to the estimation model as Ch. 2. Alternatively, the resonance frequency (f2) is input to the estimation model as Ch. 1, and the current distribution at the resonance frequency (f2) is input to the estimation model as Ch. 2.
Next, in the learning phase, parameters of the estimation model are learned (learned by using an error back propagation method) so that the output from the estimation model for the inputs of Ch. 1 and Ch. 2 approaches a correct answer label (EMI intensity) included in the training data set (S5).
In the estimation phase, instead of the circuit information of the electronic circuit to be learned, the circuit information of the electronic circuit to be analyzed is acquired and the processes in S1 to S4 are performed.
For example, in the estimation phase, the circuit information in the electronic circuit to be analyzed is acquired by an input from a user, or the like (S1). Next, in the estimation phase, based on the circuit information, the current distribution at each frequency in the electronic circuit is calculated by the circuit simulator such as SPICE (S2). Next, in the estimation phase, the resonance frequency (f1, f2) at which the maximum value of the calculated current distribution is the highest is identified, and the current distribution for each resonance frequency (f1, f2) is obtained (S3).
Next, in the estimation phase, the information on the identified frequency (the value of the resonance frequency) is input as the input data of the channel 1 (Ch. 1), and the information on the current distribution at the frequency (the image indicating the current distribution) is input as the input data of the channel 2 (Ch. 2) to the estimation model such as the deep learning (S4). Thus, in the estimation phase, the estimated value of the EMI intensity is obtained based on the output from the estimation model (S5).
The communication unit 10 communicates with input devices, such as a keyboard and a mouse, and other information processing devices via a LAN, a communication cable, or the like under the control of the control unit 30. The information processing device 1 receives, for example, an input of a training data set 21 for training the estimation model and an input of circuit information of the electronic circuit to be analyzed via the communication unit 10.
The storage unit 20 is, for example, a semiconductor memory element, such as a random-access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 20 includes the training data set 21, estimation model information 22, and estimation result information 23.
The training data set 21 is a data set of training data including cases to be learned in which the arrangements of the circuit elements in the electronic circuit differ from each other. For example, the training data of the training data set 21 includes the circuit information and the correct answer label (EMI intensity) in each case.
The estimation model information 22 is a parameter or the like for constructing a machine learning model (estimation model) generated by the machine learning.
The estimation result information 23 is an estimation result obtained by using the estimation model of the estimation model information 22 for the circuit information of the electronic circuit to be analyzed. For example, the estimation result information 23 is a value of the EMI intensity for each resonance frequency in the electronic circuit to be analyzed.
The control unit 30 corresponds to the electronic circuit such as a central processing unit (CPU). The control unit 30 includes an internal memory for storing programs defining various processing procedures and control data, and executes various processing by using the programs and the control data.
For example, the control unit 30 includes an input unit 31, an input data creation unit 32, a learning unit 33, and an estimation unit 34. The input data creation unit 32 is an example of an identification unit or an acquisition unit. The learning unit 33 is an example of a model generation unit.
The input unit 31 is a processing unit that receives input of various information such as the training data set 21 and the circuit information of the electronic circuit to be analyzed. For example, the input unit 31 stores the training data set 21 input via the communication unit 10 in the storage unit 20. The input unit 31 outputs the circuit information of the electronic circuit to be analyzed input via the communication unit 10 to the input data creation unit 32.
The input data creation unit 32 is a processing unit that generates the input data to be input to the estimation model. For example, the input data creation unit 32 calculates the current distribution at each frequency in the electronic circuit by the circuit simulator such as SPICE based on the circuit information. Next, the input data creation unit 32 identifies the resonance frequency at which the maximum value of the calculated current distribution is the highest, and identifies the current distribution for each resonance frequency. Next, the input data creation unit 32 generates the input data in which the identified resonance frequency is set to Ch. 1 and the current distribution at the resonance frequency is set to Ch. 2.
The learning unit 33 is a processing unit that learns the parameters of the estimation model by the machine learning (learning by using the error back propagation method). For example, the learning unit 33 learns the parameters of the estimation model such that the output from the estimation model for the input data (Ch. 1: the value of the resonance frequency, Ch. 2: the current distribution at the resonance frequency) generated by the input data creation unit 32 for the case to be learned approaches the correct answer label (EMI intensity) of the case. Next, the learning unit 33 stores the parameters of the estimation model obtained by the learning in the storage unit 20 as the estimation model information 22.
The estimation unit 34 is a processing unit that estimates the electromagnetic wave radiation situation in the electronic circuit to be analyzed by using the estimation model generated by the machine learning of the learning unit 33. For example, the estimation unit 34 inputs the input data (Ch. 1: the value of the resonance frequency, Ch. 2: the current distribution at the resonance frequency) generated by the input data creation unit 32 for the electronic circuit to be analyzed to the estimation model based on the estimation model information 22. Next, the estimation unit 34 obtains the estimated value (estimation result) of the EMI intensity based on the output from the estimation model according to the input. Next, the estimation unit 34 stores the obtained estimation result in the storage unit 20 as the estimation result information 23, together with, for example, the electronic circuit to be analyzed.
Next, based on the circuit information to be learned or estimated, the input data creation unit 32 creates the input data (Ch. 1: the value of the resonance frequency, Ch. 2: the current distribution at the resonance frequency) to be input to the estimation model (S11).
As illustrated in
For example, in the example of
Next, the input data creation unit 32 executes loop processing (S22 to S27) in the current distribution at each frequency.
For example, in a case where the loop processing is started, the input data creation unit 32 determines whether or not the maximum value of the current distribution is the highest (resonance frequency) (S23). In a case where the frequency is not the resonance frequency (S23: No), the input data creation unit 32 returns the process to S22, and continues the loop processing related to the current distribution at a subsequent frequency.
In a case where the frequency is the resonance frequency (S23: Yes), the input data creation unit 32 generates a two-dimensional matrix, in which a wiring pattern of the electronic circuit is colored with the current distribution in order to approximate the near field, for example, an image indicating the current distribution (S24).
Next, the input data creation unit 32 generates a matrix filled with the elements using the resonance frequency (S25). For example, the input data creation unit 32 creates a matrix having the same size as the matrix generated in S24, and assigns the value of the resonance frequency to each of the elements of the matrix.
In a case where a plurality of channels (Ch. 1 and Ch. 2) are input to the estimation model, types of the matrices of the respective channels are demanded to be the same. Therefore, in S25, in a case where scalar (the value of the resonance frequency) is converted into the matrix, handling as a standard neural network model (estimation model) is possible.
As illustrated in
Next, the input data creation unit 32 creates input data in which the two matrices in S24 and S25 are stored in Ch. 1 and Ch. 2 (S26). For example, as illustrated in
After the loop processing, the input data creation unit 32 outputs the input data D2 and D3 to be input to the estimation model to the learning unit 33 or the estimation unit 34 (S28).
Returning to
For example, in a case of the learning (learning phase) of the estimation model based on the circuit information of the electronic circuit to be learned, the learning unit 33 learns the parameters such that the output from the estimation model for the input data D2 (Ch. 1: the value of the resonance frequency) and the input data D3 (Ch. 2: the current distribution at the resonance frequency) approaches the correct answer label.
In a case of the estimation (estimation phase) based on the circuit information of the electronic circuit to be analyzed, the estimation unit 34 obtains an estimated value (estimation result) of the EMI intensity by using the output from the estimation model for the input data D2 (Ch. 1: the value of the resonance frequency) and the input data D3 (Ch. 2: the current distribution at the resonance frequency).
Next, the information processing device 1 outputs a result of the estimation or the learning (S15). For example, in a case of the learning phase, the learning unit 33 stores the parameters of the estimation model, which are obtained by the learning, in the storage unit 20 as the estimation model information 22. In a case of the estimation phase, the estimation unit 34 stores the estimation result information 23, which includes the obtained estimation result, in the storage unit 20.
In the learning phase, the information processing device 1 learns the estimation model with the training data set 21 having a simple circuit configuration using LCR elements. In the estimation phase, the information processing device 1 inputs, to the estimation model, a result of division performed on the current distributions for each configuration of the circuit element included in the electronic circuit to be analyzed. Next, the information processing device 1 may estimate the electromagnetic wave radiation situation of the electronic circuit to be analyzed by adding the output from the estimation model.
For example, the learning case P1a is a case of the current distribution and a correct answer label (EMI intensity at the resonance frequency) in a case where the circuit configuration does not include an element. Similarly, the learning case P1b is a case of a circuit configuration of C: 10 pF, the learning case P1c is a case of a circuit configuration of C: 100 pF, and the learning case P1d is a case of a circuit configuration of R: 100Ω.
In an estimation phase P2, the input data creation unit 32 identifies a current distribution D4 at the resonance frequency (S11a). With respect to the identified current distribution D4, the estimation unit 34 performs division on the current distribution for each configuration of the circuit element included in the electronic circuit (S11b). For example, the estimation unit 34 performs division on the current distribution D4 at a point of a circuit branch in the circuit information, and obtains current distributions D4a and D4b.
Next, the estimation unit 34 individually inputs the obtained current distributions D4a and D4b to the estimation model to obtain respective outputs thereof (estimated values of the EMI intensity) (S13a). Next, the estimation unit 34 adds the obtained outputs (S13b) to estimate the electromagnetic wave radiation situation of the electronic circuit to be analyzed.
Accordingly, even in a case where the electronic circuit to be analyzed has a complex circuit configuration, the information processing device 1 may estimate the electromagnetic wave radiation situation by using the estimation model learned with the training data set 21 having the simple circuit configuration.
As described above, the information processing device 1 includes the input data creation unit 32 and the estimation unit 34. The input data creation unit 32 identifies, for the electronic circuit to be analyzed, the resonance frequency of the current and the spatial distribution (current distribution) of the current flowing through the electronic circuit to be analyzed at the resonance frequency. The estimation unit 34 inputs the value of the identified resonance frequency and the current distribution of the identified electronic circuit to be analyzed to the estimation model (machine learning model) of the estimation model information 22 as the input data. Next, the estimation unit 34 estimates the electromagnetic wave radiation situation of the electronic circuit to be analyzed based on the output from the machine learning model. The estimation model information 22 related to the machine learning model is generated by using the training data set in which the arrangements of the circuit elements in the electronic circuit differ from each other, the training data set being a set of the training data in which the value of the resonance frequency for the electronic circuit and the current distribution of the electronic circuit at the resonance frequency are used as the input data and the electromagnetic wave radiation situation of the electronic circuit is set as a label.
Therefore, in the information processing device 1, the spatial distribution (current distribution) of the current generated using various values of the LCR element included in the electronic circuit to be analyzed may be identified, and the near field of the electronic circuit to be analyzed may be accurately approximated. Therefore, in the information processing device 1, the electromagnetic wave radiation situation (EMI) in the electronic circuit to be analyzed may be estimated with high accuracy by using the machine learning model in which machine learning is performed on the electromagnetic wave radiation situation of the electronic circuit using the resonance frequency of the current in the electronic circuit and the current distribution at the resonance frequency as the feature quantities.
A case C1 and a case C2 have the same circuit configuration, but values of the circuit elements thereof are different from each other. For example, in the case C1, C: 100 pF and L: 10 nH, and, in the case C2, C: 100 pF and L: 100 pH. For example, the case C2 is a case where the value of L is smaller and the reflection is smaller.
As illustrated in
The input data creation unit 32 identifies the frequency, at which the maximum value of the current distribution of the electronic circuit to be analyzed is the highest, as the resonance frequency. Therefore, in the information processing device 1, the electromagnetic wave radiation situation (EMI intensity) may be estimated at the resonance frequency at which the maximum value of the current distribution is the highest in the electronic circuit to be analyzed. In circuit design, there is an interest in reducing the peak of the EMI spectrum. Therefore, it is sufficient in a case where the peak EMI intensity (the maximum value of the current distribution is the highest) may be predicted.
The input data creation unit 32 identifies the resonance frequency and the current distribution for the electronic circuit to be analyzed by using the circuit simulator. Thus, in the information processing device 1, for example, the resonance frequency and the current distribution may be identified in the electronic circuit to be analyzed by the circuit simulator such as SPICE.
The estimation unit 34 inputs, to the machine learning model, the value of the identified resonance frequency and the current distribution obtained through division performed on the identified current distribution of the electronic circuit to be analyzed for each configuration of the circuit element included in the electronic circuit as the input data. Next, the estimation unit 34 estimates the electromagnetic wave radiation situation of the electronic circuit to be analyzed by adding the output from the machine learning model according to the input of the current distributions obtained through division for each configuration of the circuit element. Accordingly, even in the case where the electronic circuit to be analyzed has the complex circuit configuration, the information processing device 1 may estimate the electromagnetic wave radiation situation by using the machine learning model in which machine learning is performed with the training data having the simple circuit configuration.
Note that, each of the components of each of the devices illustrated in the drawing is not demanded to be physically configured as illustrated in the drawing. For example, specific forms of the separation and integration of each of the devices are not limited to the illustrated drawing. All or some of the devices may be functionally or physically separated and integrated in an arbitrary unit according to various loads, usage situations, and the like.
The information processing device 1 may include one of a configuration (the input unit 31, the input data creation unit 32, and the learning unit 33) that executes the learning phase and a configuration (the input unit 31, the input data creation unit 32, and the estimation unit 34) that executes the estimation phase. For example, learning of the estimation model and estimation using the learned estimation model may be performed by different information processing devices.
All or some of the various processing functions performed in the information processing device 1 may be executed over the CPU (or a microcomputer such as a microprocessor unit (MPU) or a micro controller unit (MCU)) or a graphics processing unit (GPU). Further, it is apparent that all or some of the various processing functions may be executed over programs, which are analyzed and executed by the CPU (or the microcomputer such as the MPU or the MCU) or the GPU, or over hardware by using a wired logic. The various processing functions performed in the information processing device 1 may be executed in such a way that a plurality of computers cooperate via cloud computing.
The various processing described in the embodiment may be realized in such a way that the computer executes a program prepared in advance. Hereinafter, an example of the computer (hardware) that executes the program having the same function as in the above-described embodiment will be described below.
As illustrated in
The hard disk device 209 stores programs 211 for executing various processing in the input unit 31, the input data creation unit 32, the learning unit 33, the estimation unit 34, and the like which are described in the embodiment. The hard disk device 209 stores various data 212 such as the training data set 21 and the estimation model information 22 to which the programs 211 refers. The input device 202 receives input of operation information from, for example, an operator. The monitor 203 displays various screens operated by, for example, the operator. For example, a printer or the like is coupled to the interface device 206. The communication device 207 is coupled to a communication network such as a local area network (LAN), and exchanges various information with the external device via the communication network.
The CPU 201 or the GPU 201a reads the programs 211 stored in the hard disk device 209 and deploys and executes the programs 211 in the RAM 208, thereby performing various processing related to the input unit 31, the input data creation unit 32, the learning unit 33, the estimation unit 34, and the like. The programs 211 may not be stored in the hard disk device 209. For example, the programs 211 stored in the storage medium, which is readable by the computer 200, may be read and executed. The storage medium which is readable by the computer 200 corresponds to, for example, a portable recording medium, such as a compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), or a Universal Serial Bus (USB) memory, a semiconductor memory such as a flash memory, a hard disk drive, or the like. The programs 211 may be stored in a device coupled to a public network, the internet, a LAN, or the like, and the computer 200 may read and execute the programs 211 from the device.
According to an aspect of the embodiment, accurate estimation of an electromagnetic wave radiation situation may be supported.
All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-084147 | May 2020 | JP | national |
