Data processing device, magnetic recording device, method for manufacturing a magnetic recording device, and data processing method

Abstract

The present invention provides a data processing device, a magnetic recording device, a method for manufacturing a magnetic recording device, and a data processing method that enable improved efficiency. [Solution] The data processing device includes an acquisition unit and a processing unit. The acquisition unit is configured to acquire a plurality of second data corresponding to at least a portion of waveform data including a first data. The processing unit is configured to perform a first operation and a second operation. In the first operation, the processing unit is configured to correct the first operation so that the difference between the first data and a fourth data obtained by interpolating a plurality of third data obtained by processing the plurality of second data by the first operation is reduced. In the second operation, the processing unit is configured to derive a plurality of sixth data by processing a plurality of fifth data by the corrected first operation.

Description

Technical Field

[0001] Embodiments of the present invention relate to a data processing apparatus, a magnetic recording apparatus, a method of manufacturing a magnetic recording apparatus, and a data processing method.

Background Art

[0002] For example, data for controlling a magnetic recording apparatus or the like is processed by a data processing apparatus. In data processing, improvement in efficiency is desired.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Embodiments of the present invention provide a data processing apparatus, a magnetic recording apparatus, a method of manufacturing a magnetic recording apparatus, and a data processing method capable of improving efficiency.

Means for Solving the Problems

[0005] According to an embodiment, a data processing apparatus includes an acquisition unit and a processing unit. The acquisition unit is configured to acquire a plurality of second data corresponding to at least a part of waveform data including first data. The processing unit is configured to perform a first operation and a second operation. In the first operation, the processing unit is configured to correct the first processing so that the difference between fourth data obtained by interpolating a plurality of third data obtained by processing the plurality of second data by the first processing and the first data becomes small. In the second operation, the processing unit is configured to process a plurality of fifth data by the corrected first processing to derive a plurality of sixth data.

Brief Description of the Drawings

[0006] [Figure 1] Figure 1 is a schematic diagram illustrating a data processing device according to the first embodiment. [Figure 2] Figures 2(a) and 2(b) are schematic diagrams illustrating the operation of a data processing device. [Figure 3] Figures 3(a) and 3(b) are schematic diagrams illustrating the operation of a data processing device. [Figure 4] Figure 4 is a graph illustrating the characteristics of a data processing device. [Figure 5] Figure 5 is a schematic diagram illustrating a data processing device according to the first embodiment. [Modes for carrying out the invention]

[0007] Embodiments of the present invention will be described below with reference to the drawings. In this specification and in each drawing, elements similar to those described above in the previously shown drawings are denoted by the same reference numerals, and detailed descriptions are omitted as appropriate.

[0008] (First Embodiment) Figure 1 is a schematic diagram illustrating a data processing device according to the first embodiment. As shown in Figure 1, the data processing device 110 according to this embodiment includes an acquisition unit 77 and a processing unit 70.

[0009] The acquisition unit 77 is configured to acquire multiple data. These multiple data are, for example, at least a portion of waveform data. The waveform data may be, for example, periodic data. The waveform data may include, for example, control data relating to the magnetic recording medium 80. The waveform data may include, for example, information regarding the RRO (Repeatable Run Out) of the magnetic recording medium 80. The following describes an example of when the data processing device 110 processes data relating to the magnetic recording medium 80.

[0010] The waveform data includes, for example, a first data point, Da1. The first data point, Da1, may include information about RRO.

[0011] The acquisition unit 77 is configured to acquire a plurality of second data Da2. The second data Da2 corresponds to at least a portion of the waveform data including the first data Da1. If the first data Da1 relates to RRO, the plurality of second data Da2 may be sampled RROs.

[0012] For example, the first data Da1 may have M samples per track of the magnetic recording medium 80. M can be 2 or greater. Multiple second data Da2 may have N samples per track of the magnetic recording medium 80. N can be 2 or greater and less than or equal to M. In one example, M is 32 and N is 4.

[0013] For example, when trying to obtain RRO data for a single magnetic recording medium 80, acquiring data at every position along the track would take a long time. Therefore, it is conceivable to acquire RRO data using sampling based on "N" which is smaller than "M". This allows for data acquisition in a shorter time, enabling efficient data acquisition.

[0014] In this embodiment, the processing unit 70 is configured to perform the following first operation OP1 and second operation OP2 (see Figure 1). In the first operation OP1, the processing unit 70 processes a plurality of second data Da2 with a first process 71R to obtain a plurality of third data Da3. The plurality of third data Da3 obtained by the first process 71R are interpolated to obtain a fourth data Da4. The first process 71R is corrected so that the difference Δ1 between the obtained fourth data Da4 and the first data Da1 is small.

[0015] The processing unit 70 is configured to process multiple fifth data points Da5 using a corrected first process 71R in the second operation OP2 to derive multiple sixth data points Da6. The data obtained by interpolating the multiple sixth data points Da6 will be closer to the first data point Da1. High-precision data processing can be efficiently performed.

[0016] The above-described first process 71R may include, for example, processing a plurality of second data Da2 using a learning model 71M including learning parameters 71P. The first process 71R may include, for example, processing by a neural network (NN).

[0017] As shown in FIG. 1, for example, the processing unit 70 may include a first processing unit 71. The first processing unit 71 is configured to perform the first process 71R. The first processing unit 71 is configured to process a plurality of second data Da2 using a learning model 71M including learning parameters 71P. Machine learning is performed in the first processing unit 71.

[0018] As shown in FIG. 1, the processing unit 70 may include a second processing unit 72. The second processing unit 72 is configured to correct the learning parameters 71P so that the above-described difference Δ1 (the difference between the fourth data Da4 and the first data Da1) becomes small. A learning model 71M including the learning parameters 71P corrected so that the difference Δ1 becomes small is obtained. Such a learning model 71M corresponds to a learned model. The data processing according to the embodiment corresponds to a process of generating a learned model.

[0019] As shown in FIG. 1, the processing unit 70 may include a third processing unit 73. The third processing unit 73 is configured to interpolate a plurality of third data Da3 to derive fourth data Da4. In the third processing unit 73, for example, linear interpolation may be performed. In the third processing unit 73, for example, interpolation of a second or higher order may be performed. In the third processing unit 73, for example, interpolation by a differentiable function may be performed.

[0020] As shown in FIG. 1, the processing unit 70 may also include a fourth processing unit 74. The fourth processing unit 74 is configured to derive a difference Δ1 between the fourth data Da4 and the first data Da1. At least a part of the learning parameters 71P is corrected so that the difference Δ1 derived by the fourth processing unit 74 becomes small. Thereby, data closer to the first data Da1 is obtained based on a plurality of second data Da2 with a small number of data.

[0021] As already described, the first data Da1 may include periodic data. The plurality of second data Da2 may include periodic data.

[0022] For example, the plurality of second data Da2 may include information regarding the RRO of the magnetic recording medium 80. The plurality of fifth data Da5 may include information regarding the RRO of the magnetic recording medium 80. The plurality of sixth data Da6 obtained by the second operation OP2 may be recorded in the servo area or the like of the magnetic recording medium 80 (see FIG. 1).

[0023] As shown in FIG. 1, the data processing apparatus 110 may include a memory 78. The memory 78 is configured to store the learning parameter 71P. For example, the corrected learning parameter 71P may be stored in the memory 78. The learning parameter 71P stored in the memory 78 may be read out. In the second operation OP2, the first process 71R may be performed based on the read learning parameter 71P.

[0024] FIGS. 2(a), 2(b), 3(a) and 3(b) are schematic diagrams illustrating the operations of the data processing apparatus. The horizontal axis of these figures is the data number DN. When the target data is data regarding the RRO of the magnetic recording medium 80, the data number DN corresponds to the position in the cross-track direction of the magnetic recording medium 80. The vertical axis is the data D0. The data D0 is, for example, the RRO.

[0025] The first data Da1 changes periodically. For example, the period TP corresponds to one track. In the example of FIG. 2(a), in one track, the first data Da1 includes 32 values. For example, “M” is 32. In FIG. 2(a), for ease of viewing, the marks of the 32 data included in one track (one of the plurality of periods TP) are omitted.

[0026] As shown in Figure 2(b), in this example, the number of secondary data Da2 acquired by the acquisition unit 77 is less than the number of values ​​contained in the primary data Da1. In this example, in one track, the secondary data Da2 contains four values. For example, "N" is 4. For example, each of the multiple values ​​(multiple circles) contained in the secondary data Da2 overlaps with the primary data Da1.

[0027] Multiple such second data points Da2 are linearly interpolated to obtain interpolated data Dp2. Interpolated data Dp2 represents the first data point Da1 to some extent. However, the difference between interpolated data Dp2 and the first data point Da1 is not necessarily small. Interpolated data Dp2 corresponds to, for example, the reference example.

[0028] Multiple such second data points Da2 can be processed using the learning model 71M to derive multiple third data points Da3 (see Figure 3(a)).

[0029] The multiple third data points Da3 shown in Figure 3(a) are obtained from the multiple second data points Da2 illustrated in Figure 2(b). Each track contains four values ​​for the multiple third data points Da3. The multiple values ​​(multiple circles) included in the multiple third data points Da3 may be shifted from the curve of the first data point Da1.

[0030] By interpolating multiple such third data points Da3, a fourth data point Da4 (see Figure 3(a)) can be obtained. For example, linear interpolation may be performed. For example, higher-order interpolation of the second order or higher may also be performed.

[0031] As already explained, for example, a difference Δ1 is derived between the fourth data point Da4 obtained by interpolation and the first data point Da1. Then, the learning parameter 71P is modified so that this difference Δ1 is small. For example, the learning parameter 71P is modified so that the sum of the squares of the differences between the fourth data point Da4 and the first data point Da1 is minimized.

[0032] As shown in Figure 3(b), in the second operation OP2, the learning model 71M, which includes the modified learning parameters 71P, processes other data (multiple fifth data points Da5). At least some of the multiple fifth data points Da5 may be shifted from multiple third data points Da3.

[0033] The fifth data point, Da5, is processed using the modified learning parameter 71P to obtain multiple sixth data points, Da6 (see Figure 3(b)). The seventh data point, Da7 (see Figure 3(b)), is derived by interpolating the multiple sixth data points, Da6. The difference between the seventh data point, Da7, and the first data point, Da1, is smaller than, for example, the difference between the interpolated data point Dp2 exemplified in Figure 2(b) and the first data point, Da1 (the difference in the reference example). A highly accurate RRO can be efficiently derived from a small number of multiple fifth data points, Da5.

[0034] Thus, in the first operation OP1, the processing unit 70 corrects the first process 71R. In the second operation OP2, the processing unit 70 uses the corrected first process 71R to process other data (multiple fifth data Da5) to derive multiple sixth data Da6. The processing unit 70 may further interpolate the multiple sixth data Da6 to derive seventh data Da7. The first data Da1, which has a large number of data points, is represented with high accuracy by the seventh data Da7, which has a small number of data points.

[0035] In this embodiment, the number of multiple second data points Da2 (second number) may be the same as the number of multiple third data points Da3 (third number). In the example above, these numbers per track are "N". In this embodiment, processing by the learning model 71M based on the modified learning parameters 71P yields higher accuracy data processing results. A higher accuracy RRO is obtained.

[0036] In the embodiment, the number of multiple second data Da2 (second number) may be smaller than the number of multiple third data Da3 (third number). For example, if the first data Da1 contains 32 values ​​per track, the multiple third data Da3 may contain 4 values ​​per track. In this case, the multiple second data Da2 may contain 3.7 values ​​per track. For example, if the multiple third data Da3 contains N1 values ​​per track, the multiple second data Da2 may contain 0.925 times the number of N1 values ​​per track. In the embodiment, processing by the learning model 71M based on the modified learning parameters 71P allows for obtaining multiple high-accuracy third data Da3 from a small number of multiple second data Da2. For example, the measurement time for RRO can be reduced. Efficient data acquisition becomes possible.

[0037] In this embodiment, the second number of the multiple second data Da2 may be the same as the fifth number of the multiple fifth data Da5.

[0038] In this embodiment, the interpolation function that generates the fourth data Da4 from a plurality of third data Da3 may be a differentiable function. By applying a differentiable function, for example, the error function generated from the difference Δ1 between the fourth data Da4 and the first data Da1 can be backpropagated to the learning model 71M. This allows for appropriate correction of the learning parameters 71P.

[0039] As shown in Figure 1, a third operation OP3 may be performed. In the third operation OP3, a seventh data Da7 is derived by interpolating a plurality of sixth data Da6. In one example, the control device that performs the third operation OP3 may be different from the processing unit 70 described above. The eighth data Da8 is used, for example, to correct tracking in the magnetic recording device 210.

[0040] Figure 4 is a graph illustrating the characteristics of a data processing device. Figure 4 illustrates the characteristics of the difference between the sixth data point Da6 and the value obtained by linearly interpolating multiple second data points Da2. The horizontal axis represents the number Nx of multiple second data points Da2. The number Nx corresponds to the number of values ​​included in one track. In this example, the horizontal axis is normalized by "N1". The vertical axis represents the precision parameter P1. The precision parameter P1 corresponds to the difference between the sixth data point Da6 and the measured value M, and 3σ (3 times the standard deviation). In the example in Figure 4, the precision parameter P1 is based on the value of 3σ when Nx / N1 is 1. When the precision parameter P1 is positive and large, higher precision than the standard is obtained. When the precision parameter P1 is negative, the precision is lower than the standard.

[0041] As shown in Figure 4, when the number N1 is 4, the accuracy parameter P1 has a large positive value. By interpolating using the corrected learning parameter 71P, which reduces the difference Δ1, highly accurate interpolated data can be obtained.

[0042] As shown in Figure 4, when Nx / N1 is greater than 0.925, a precision parameter P1 greater than 0 is obtained. For example, with a number of data points per track that is 0.925 times or more of the number N1, precision equivalent to or better than that obtained from N1 data points per track can be obtained. For example, the number of second data points Da2 to be measured can be reduced from N1 per track to 0.925 times N1 per track. This enables highly efficient data acquisition.

[0043] In the data processing device 110 according to this embodiment, for example, a small-scale neural network (e.g., a first processing unit 71) is provided before linear interpolation. The first processing unit 71 can improve the accuracy of data interpolation. For example, learning by the NN is performed using the difference Δ1 after interpolation as the loss function. This makes it possible to realize an NN that can improve the accuracy after interpolation with small-scale computation. The interpolation can be, for example, linear interpolation.

[0044] For example, high interpolation accuracy can be obtained with small-scale calculations. The data processing device 110 according to the embodiment can be applied to RRO, for example. The data processing device 110 according to the embodiment can be applied to processing data obtained from various memories or sensors, for example.

[0045] Figure 5 is a schematic diagram illustrating a data processing device according to the first embodiment. The data processing device 110 includes an acquisition unit 77. The acquisition unit 77 can acquire various types of data, for example. The acquisition unit 77 includes, for example, an I / O port. The acquisition unit 77 is an interface. The acquisition unit 77 may also have the function of an output unit. The acquisition unit 77 may also have, for example, a communication function.

[0046] In this example, the data processing device 110 includes a memory 78. The memory 78 is capable of storing various types of data. The memory 78 may include at least one of ROM (Read Only Memory) and DAM (Random Access Memory).

[0047] The data processing device 110 may include a display unit 79a and an input unit 79b, etc. The display unit 79a may include various types of displays. The input unit 79b may include, for example, a device having an operating function (e.g., a keyboard, mouse, touch input panel, or voice recognition input device).

[0048] The processing unit 70 may include, for example, a CPU (CentDal Processing Unit). The processing unit 70 may also include, for example, an electronic circuit.

[0049] Multiple elements included in the data processing device 110 can communicate with each other by at least one of wireless and wired methods. The locations where the multiple elements included in the data processing device 110 are provided may be different from each other. A dedicated circuit may be used as at least a part of the data processing device 110 (for example, the processing unit 70). Multiple interconnected circuits may be used as the data processing device 110.

[0050] For example, a general-purpose computer may be used as the data processing device 110. For example, multiple computers connected to each other may be used as the data processing device 110.

[0051] (Second Embodiment) The second embodiment relates to a magnetic recording device. As shown in Figure 1, the magnetic recording device 210 according to the embodiment includes a data processing device 110 according to the first embodiment and a magnetic recording medium 80. The acquisition unit 77 is configured to acquire a plurality of second data Da2 from the magnetic recording medium 80.

[0052] Multiple sixth data Da6, as described in the first embodiment, may be recorded on the magnetic recording medium 80. For example, multiple sixth data Da6 may be recorded in a servo region provided on the magnetic recording medium 80. Multiple sixth data Da6 may correspond, for example, to corrected RRO data.

[0053] (Third embodiment) The third embodiment relates to a method for manufacturing a magnetic recording device 210. This manufacturing method includes obtaining a plurality of second data Da2 corresponding to at least a portion of waveform data including a first data Da1 from a magnetic recording medium 80. This manufacturing method includes performing a first operation OP1 and a second operation OP2.

[0054] The first operation OP1 includes correcting the first process 71R so that the difference Δ1 between the fourth data Da4, obtained by interpolating the multiple third data Da3 obtained by processing multiple second data Da2 with the first process 71R, and the first data Da1 is reduced. The second operation OP2 includes processing multiple fifth data Da5 with the corrected first process 71R to derive multiple sixth data Da6.

[0055] The first process 71R may include processing multiple second data Da2 using a learning model 71M that includes learning parameters 71P. The manufacturing method may include modifying the learning parameters 71P so that the difference Δ1 is small.

[0056] Multiple second data Da2 may include information regarding RRO (Repeatable Run Out) related to the magnetic recording medium 80. The manufacturing method may include recording multiple sixth data Da6 on the magnetic recording medium 80.

[0057] (Fourth Embodiment) The fourth embodiment relates to a data processing method. The data processing method includes acquiring a plurality of second data Da2 corresponding to at least a portion of waveform data including a first data Da1. The data processing method includes performing a first operation OP1 and a second operation OP2.

[0058] The first operation OP1 includes correcting the first process 71R so that the difference Δ1 between the fourth data Da4, obtained by interpolating the multiple third data Da3 obtained by processing multiple second data Da2 with the first process 71R, and the first data Da1 is reduced. The second operation OP2 may include processing multiple fifth data Da5 with the corrected first process 71R to derive multiple sixth data Da6.

[0059] In the data processing method according to the embodiment, the first process 71R may include processing a plurality of second data Da2 using a learning model 71M that includes learning parameters 71P. The learning parameters 71P may be modified so that the difference Δ1 becomes small.

[0060] The embodiments may include the following technical proposals. (Technical proposal 1) An acquisition unit configured to acquire a plurality of second data corresponding to at least a portion of waveform data including the first data, Processing unit and Equipped with, The processing unit is configured to perform the first and second operations. The processing unit is configured to correct the first processing in the first operation such that the difference between the first data and the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing becomes small. The processing unit is configured to process a plurality of fifth data by the corrected first processing in the second operation to derive a plurality of sixth data.

[0061] (Technical proposal 2) The processing unit includes a first processing unit configured to perform the first processing, The data processing device according to Technical Proposal 1, wherein the first processing unit is configured to process the plurality of second data using a learning model that includes learning parameters.

[0062] (Technical proposal 3) The aforementioned processing unit includes a second processing unit, The data processing device according to Technical Proposal 2, wherein the second processing unit is configured to modify the learning parameters so that the discrepancy is reduced.

[0063] (Technical proposal 4) The processing unit includes a third processing unit, The data processing device according to any one of Technical Proposals 1 to 3, wherein the third processing unit is configured to interpolate the plurality of third data to derive the fourth data.

[0064] (Technical proposal 5) The processing unit includes a fourth processing unit, The data processing device according to any one of Technical Proposals 1 to 4, wherein the fourth processing unit is configured to derive the difference between the fourth data and the first data.

[0065] (Technical proposal 6) The aforementioned plurality of second data includes periodic data, as described in any one of Technical Proposals 1 to 5, in the data processing device.

[0066] (Technical proposal 7) The aforementioned multiple second data include information regarding the RRO (Repeatable Run Out) of the magnetic recording medium, The plurality of fifth data corresponds to the RRO information of the magnetic recording medium, and is a data processing device according to any one of Technical Proposals 1 to 5.

[0067] (Technical proposal 8) A third action is then performed. The data processing device according to Technical Proposal 7, wherein in the third operation, the plurality of sixth data are interpolated to derive a seventh data.

[0068] (Technical proposal 9) A data processing device according to any one of Technical Proposals 1 to 8, wherein the second number of the plurality of second data is the same as the third number of the plurality of third data.

[0069] (Technical proposal 10) The data processing device according to Technical Proposal 9, wherein the second number is the same as the fifth number of the plurality of fifth data.

[0070] (Technical proposal 11) A data processing device according to any one of Technical Proposals 1 to 8, wherein the second number of the plurality of second data is smaller than the third number of the plurality of third data.

[0071] (Technical proposal 12) The data processing device according to Technical Proposal 11, wherein the second number is the same as the fifth number of the plurality of fifth data.

[0072] (Technical proposal 13) The interpolation function that interpolates the plurality of third data to generate the fourth data is differentiable, according to the data processing device described in any one of Technical Proposals 1 to 12.

[0073] (Technical proposal 14) A data processing device described in Technical Proposal 7 or 8, The magnetic recording medium and, Equipped with, The acquisition unit is configured to acquire the plurality of second data from the magnetic recording medium, and is a magnetic recording device.

[0074] (Technical proposal 15) A method for manufacturing a magnetic recording device, Multiple second data sets corresponding to at least a portion of the waveform data, including the first data, are obtained from the magnetic recording medium. The first processing is corrected so that the difference between the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing and the first data becomes small. A method for manufacturing a magnetic recording device, comprising processing multiple fifth data by the corrected first processing to derive multiple sixth data.

[0075] (Technical proposal 16) The first process includes processing the plurality of second data using a learning model that includes learning parameters, A method for manufacturing a magnetic recording device according to Technical Proposal 15, wherein the learning parameters are modified so that the recording error is reduced.

[0076] (Technical proposal 17) The aforementioned plurality of second data include information relating to the RRO (Repeatable Run Out) of the magnetic recording medium, A method for manufacturing a magnetic recording apparatus according to technical proposal 15 or 16, wherein the plurality of sixth data are recorded on the magnetic recording medium.

[0077] (Technical proposal 18) Multiple second data sets are obtained that correspond to at least a portion of the waveform data, including the first data set. Perform the first and second actions, In the first operation, the first processing is corrected so that the difference between the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing and the first data is reduced. A data processing method comprising, in the second operation, processing a plurality of fifth data by the first process after correction to derive a plurality of sixth data.

[0078] (Technical proposal 19) The data processing method according to Technical Proposal 18, wherein the first processing includes processing the plurality of second data using a learning model that includes learning parameters.

[0079] (Technical proposal 20) A data processing method according to Technical Proposal 19, which modifies the learning parameters so that the discrepancy is reduced.

[0080] According to the embodiment, a data processing device, a magnetic recording device, a method for manufacturing a magnetic recording device, and a data processing method can be provided that enable improved efficiency.

[0081] Embodiments of the present invention have been described above with reference to examples. However, the present invention is not limited to these examples. For example, with respect to the configuration of a processing unit included in a data processing device, the present invention can be implemented in the same manner and similar effects can be obtained by appropriately selecting components from the scope known to those skilled in the art, as far as this is within the scope of the present invention.

[0082] Combinations of two or more elements from each example, to the extent technically feasible, are also included within the scope of the present invention, insofar as they encompass the gist of the invention.

[0083] All data processing devices, magnetic recording devices, methods for manufacturing magnetic recording devices, and data processing methods that a person skilled in the art can implement by appropriately modifying the design based on the data processing device, magnetic recording device, method for manufacturing a magnetic recording device, and data processing method described above as embodiments of the present invention also fall within the scope of the present invention, insofar as they encompass the gist of the present invention.

[0084] Within the scope of the concept of this invention, a person skilled in the art would be able to conceive of various modifications and alterations, and it is understood that such modifications and alterations also fall within the scope of this invention.

[0085] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0086] 70: Processing unit, 71-74: 1st-4th processing units, 71M: Learning model, 71P: Learning parameters, 71R: 1st processing unit, 77: Acquisition unit, 78: Memory, 79a: Display unit, 79b: Input unit, 80: Magnetic recording medium, 110: Data processing unit, 210: Magnetic recording device, D0: Data, DN: Data number, Da1-Da8: 1st-8th data, Dp2: Interpolation data, OP1-OP3: 1st-3rd operations, P1: Accuracy parameter, TP: Period

Claims

1. An acquisition unit configured to acquire a plurality of second data corresponding to at least a portion of waveform data including first data, Processing unit and Equipped with, The processing unit is configured to perform the first operation and the second operation. The processing unit is configured to correct the first processing in the first operation so that the difference between the first data and the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing becomes small. The processing unit is configured to process a plurality of fifth data using the corrected first processing in the second operation to derive a plurality of sixth data.

2. The processing unit includes a first processing unit configured to perform the first processing, The data processing apparatus according to claim 1, wherein the first processing unit is configured to process the plurality of second data using a learning model that includes learning parameters.

3. The aforementioned processing unit includes a second processing unit, The data processing apparatus according to claim 2, wherein the second processing unit is configured to modify the learning parameters so that the discrepancy is reduced.

4. The data processing apparatus according to any one of claims 1 to 3, wherein the plurality of second data includes periodic data.

5. The aforementioned plurality of second data include information regarding the RRO (Repeatable Run Out) of the magnetic recording medium, The data processing apparatus according to any one of claims 1 to 3, wherein the plurality of fifth data corresponds to the information relating to the RRO of the magnetic recording medium.

6. The data processing apparatus according to any one of claims 1 to 3, wherein the second number of the plurality of second data is smaller than the third number of the plurality of third data.

7. The data processing apparatus according to any one of claims 1 to 3, wherein the interpolation function that interpolates the plurality of third data to generate the fourth data is differentiable.

8. The data processing device according to claim 5, The magnetic recording medium and, Equipped with, The acquisition unit is configured to acquire the plurality of second data from the magnetic recording medium, and is a magnetic recording device.

9. A method for manufacturing a magnetic recording device, Multiple second data sets corresponding to at least a portion of the waveform data, including the first data, are obtained from the magnetic recording medium. The first processing is corrected so that the difference between the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing and the first data becomes small. A method for manufacturing a magnetic recording device, comprising processing multiple fifth data by the first process after correction to derive multiple sixth data.

10. Multiple second data sets are obtained that correspond to at least a portion of the waveform data including the first data, Perform the first and second actions, In the first operation, the first processing is corrected so that the difference between the fourth data obtained by interpolating the plurality of third data obtained by processing the plurality of second data by the first processing and the first data is reduced. A data processing method comprising, in the second operation, processing a plurality of fifth data by the first process after correction to derive a plurality of sixth data.

Images