JP2011044212A - Device, method, and program for inspecting optical pickup - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device, method and program for inspecting an optical pickup which calculate an amount of eccentricity of the optical pickup with high accuracy while providing high robustness. <P>SOLUTION: When a mask region corresponding to a non-track range of an optical recording medium (optical disk) is determined, pitch widths (a range from one maximum point to the next maximum point and a range from one minimum point to the next minimum point) contained in a tracking error signal are extracted, and then a threshold is decided based on statistics of these extracted pitch widths. Thereafter, the decided threshold is used to discriminate an eccentric part corresponding to the track range from an aliasing part corresponding to the non-tracking range. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an optical pickup inspection apparatus, an inspection method, and an inspection program capable of determining pass / fail based on the amount of eccentricity of an optical pickup.

  An optical pickup is used as a device for reading information from an optical recording medium such as a compact disk (Compact Disk), a DVD (Digital Versatile Disk), and a Bluray Disc (registered trademark). With the recent increase in information density, the need to improve the reading accuracy of optical pickups is increasing.

  As an inspection method for such an optical pickup, there is a method using a tracking error signal (TES) obtained when the optical pickup is displaced in a direction crossing the track of the optical recording medium (when traversed). Are known. As the tracking error signal, a differential push-pull (DPP) signal is typically used.

  More specifically, the optical pickup is traversed with respect to the optical recording medium, and the amount of eccentricity is calculated based on the amount of change in the amplitude of the tracking error signal measured in that case. This amount of eccentricity is caused by an axial deviation of the spindle motor of the optical pickup, an asymmetry of the optical system (typically, an optical axis deviation of the diffraction grating), or the like. Therefore, the performance of the entire target optical pickup can be evaluated based on the amount of eccentricity.

  For example, in the adjustment method of the optical pickup device disclosed in Japanese Patent Laid-Open No. 2005-044424 (Patent Document 1), the relative position between the optical pickup and the spindle motor shaft is changed based on the envelope of the tracking error signal. Including the steps of: Japanese Patent Laying-Open No. 2002-050064 (Patent Document 2) discloses a method and apparatus for adjusting the position of a turntable using a DPP signal for detecting a tracking error.

  Further, when the optical pickup is reciprocated in a direction intersecting with the track of the optical recording medium, the moving direction (traverse direction) in the outermost and innermost circumferences of the range where the track is formed (traverse range). Is switched. The timing for switching the traverse direction is determined by detecting reflected light from a portion outside the traverse range. Therefore, the tracking error signal includes a detection signal from a portion other than the track of the optical recording medium. A detection signal from such a portion other than the track is also referred to as a “folding portion”, and needs to be excluded from the above-described calculation of the amount of eccentricity. More specifically, a mask for excluding from the calculation of the eccentricity amount is set in the folded portion. Of the tracking error signal measured when the optical pickup is traversed, a portion excluding the folded portion, that is, a detection signal from the track is also referred to as “eccentric portion”.

  As a method for detecting a folded portion included in a tracking error signal, for example, in Japanese Patent Application Laid-Open No. 2004-079079 (Patent Document 3), a laser spot is positioned outside a track in accordance with the peak level of the tracking error signal. The structure which detects that it exists is disclosed. Japanese Patent Laying-Open No. 2002-216377 (Patent Document 4) discloses a configuration in which a portion where the frequency of a tracking error signal is slower than a predetermined threshold is detected as a folded portion of the tracking error signal.

Also, International Publication No. 2004/49321 pamphlet (Patent Document 5) discloses a configuration for measuring the eccentricity of an optical disk.

JP 2005-044424 A JP 2002-050064 A JP 2004-0799079 A JP 2002-216377 A International Publication No. 2004/49321 Pamphlet

  However, in the conventional method as described above, it is difficult to determine the folded portion with high accuracy.

  For example, in the method disclosed in Japanese Patent Application Laid-Open No. 2004-079079 (Patent Document 3), there is a problem that adjustment of the determination condition is difficult and the robustness is low. In the method disclosed in Japanese Patent Application Laid-Open No. 2002-216377 (Patent Document 4), the folded portion is detected by frequency analysis of the tracking error signal. However, when the frequency analysis is used, the folded portion is detected. There is a problem that the detection accuracy cannot be increased for a component having a relatively low frequency corresponding to. In addition, it is difficult to adjust the determination conditions, and there is a problem that the robustness is low.

  In addition, the method disclosed in International Publication No. 2004/49321 (Patent Document 5) measures the eccentricity of an optical disc on the assumption that the parameters of the optical disc are known. The performance cannot be evaluated.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide an optical pickup inspection apparatus and inspection that can calculate the eccentric amount of the optical pickup with high accuracy while having high robustness. To provide a method and inspection program.

  An inspection apparatus for an optical pickup according to an aspect of the present invention includes error signal generation means for generating a tracking error signal based on a detection result detected by a detector of the optical pickup. Here, the optical pickup is configured to irradiate an optical recording medium with a plurality of beams including a main beam obtained by separating light from a light source and at least two sub-beams in a predetermined relative relationship. The apparatus is configured to detect a plurality of reflected lights generated by reflecting the plurality of beams on the optical recording medium, respectively. Further, the inspection apparatus includes an acquisition unit that acquires time series data of a tracking error signal obtained when the irradiation positions of a plurality of beams are reciprocated in a direction intersecting a track of the optical recording medium, and the time series data includes Extraction means for extracting local maximum points and local minimum points, evaluation standard determination means for determining evaluation criteria based on the statistics of the extracted local maximum points and local minimum points, and effective local maximum points among the local maximum points according to the evaluation criteria And selecting means for selecting an effective minimum point among the minimum points, and a mask for determining a mask region corresponding to the non-track range of the optical recording medium in the time series data based on the effective maximum point and the effective minimum point Based on the area determining means and the effective maximum point and effective minimum point included in the area other than the mask area, the irradiation position reciprocates in the direction intersecting the track of the optical recording medium. Cormorant including a variation characteristic generating means for generating a temporal change characteristics of the tracking error signal, based on the temporal change characteristics, and an eccentric amount calculating means for calculating the eccentricity amount of the optical pickup.

Preferably, the evaluation criterion determining means determines the first threshold value based on the average value and standard deviation of the amplitude of the maximum point, and the second threshold value based on the average value and standard deviation of the amplitude of the minimum point. Means for determining a value, wherein the selecting means extracts means having a larger amplitude than the first threshold value among the maximal points as effective maximal points, and the amplitude having the second threshold value among the maximal points. Means for extracting smaller ones as effective local minimum points.

  Preferably, the mask region determining means specifies a portion appearing in the order of the effective maximum point, the effective minimum point, and the effective maximum point, and calculates a distance between the two effective maximum points, an effective minimum point, Means for calculating the effective maximum point, the portion appearing in the order of the effective minimum point, calculating the interval between the two effective minimum points, and the calculated interval between the two effective maximum points and the two effective minimum points Based on the statistics of the interval between the points, the means for determining the third threshold value and the effective maxima, the effective minima, and the effective maxima in the order of the two effective maxima Among the portions where the interval between them is larger than the third threshold value, the means for excluding the effective minimum point included in the portion and the portion appearing in the order of the effective minimum point, the effective maximum point, and the effective minimum point are 2 For the portion where the interval between two effective local minimum points is greater than the third threshold, Means for excluding effective maximum points included in the portion; means for determining at least one of a range in which two effective maximum points exist continuously and a range in which two effective minimum points exist continuously as mask regions; including.

  Preferably, the inspection apparatus further includes a noise removing unit that removes a noise component from the effective maximum point and the effective minimum point.

  More preferably, the noise removing unit extracts a plurality of effective maximum points and a plurality of effective minimum points that appear in a predetermined pattern among the effective maximum points and the effective minimum points selected by the selecting unit. And means for excluding a part of the plurality of effective maximum points appearing in the predetermined pattern and removing a part of the plurality of effective minimum points appearing in the predetermined pattern.

  Preferably, the inspection apparatus further includes an integration unit that integrates the effective maximum points and the effective minimum points that are detected in duplicate corresponding to substantially the same track.

  More preferably, the integration processing includes a means for extracting a plurality of effective maximum points or effective minimum points existing within a predetermined period on the time axis, and a plurality of extracted effective maximum points are substantially converted into a single effective maximum point. And integrating the plurality of extracted effective minimum points into a substantially single effective minimum point.

  Preferably, the change characteristic generating means includes means for generating a series of data by arranging effective maximum points and effective minimum points existing in an area other than the mask area in time series, and filtering the series of data to obtain temporal changes. Generating a change characteristic.

An inspection method for an optical pickup according to another aspect of the present invention includes a step of attaching an inspection device to the optical pickup. Here, the optical pickup has a mechanism for irradiating a plurality of beams including a main beam obtained by separating light from a light source and at least two sub beams to an optical recording medium in a predetermined relative relationship, and the plurality of beams are light beams. And a detector for detecting a plurality of reflected lights respectively generated by being reflected on the recording medium. The inspection apparatus also includes a circuit that generates a tracking error signal based on the detection result detected by the detector. Further, the present inspection method includes a step of acquiring time series data of a tracking error signal obtained when the irradiation positions of a plurality of beams are reciprocated in a direction crossing the track of the optical recording medium, and the time series data includes A step of extracting local maximum points and local minimum points, a step of determining evaluation criteria based on the statistics of the extracted local maximum points and local minimum points, and selecting an effective local maximum point from the local maximum points according to the evaluation criteria, and determining a local minimum Selecting an effective minimum point among the points; determining a mask region corresponding to the non-track range of the optical recording medium in the time series data based on the effective maximum point and the effective minimum point; and other than the mask region Based on the effective maximum and minimum points included in the area, the tracking error caused by the reciprocation of the irradiation position in the direction intersecting the track of the optical recording medium Generating a temporal change characteristics of the signal, based on the temporal change characteristics, and calculating the eccentricity amount of the optical pickup.

  According to still another aspect of the present invention, an inspection program for an optical pickup executed by an inspection apparatus including a processor is provided. The inspection program is based on a detection result detected by a detector of the optical pickup. Thus, it functions as an error signal generation means for generating a tracking error signal. Here, the optical pickup is configured to irradiate the optical recording medium with a plurality of beams including a main beam obtained by separating light from the light source and at least two sub beams in a predetermined relative relationship. The detector is configured to detect a plurality of reflected lights generated by reflecting the plurality of beams on the optical recording medium. Further, the inspection program includes an acquisition means for acquiring time series data of a tracking error signal obtained when the processor reciprocates the irradiation positions of a plurality of beams in a direction intersecting the track of the optical recording medium, and time series data Extraction means for extracting local maximum and local minimum points included in, evaluation standard determination means for determining evaluation criteria based on the statistics of the extracted local maximum and minimum points, and effective among local maximum points according to the evaluation standard Select the local maximum point, select the effective local point from the local minimum point, and determine the mask area corresponding to the non-track range of the optical recording medium in the time series data based on the effective local maximum point and the effective local minimum point Based on the mask area determination means to be used and the effective maximum point and the effective minimum point included in the area other than the mask area, where the irradiation position intersects the track of the optical recording medium A variation characteristic generating means for generating a temporal change characteristics of the tracking error signal caused to travel to and, based on the temporal variation characteristics to function as an eccentric amount calculating means for calculating the eccentricity amount of the optical pickup.

  According to the present invention, the amount of eccentricity of the optical pickup can be calculated with high accuracy while having high robustness.

1 is a schematic configuration diagram of a system according to an embodiment of the present invention. It is a figure which shows the condensing position of the beam on the optical recording medium in the optical pick-up according to embodiment of this invention. It is a block diagram which shows the more detailed structure of the photon detection part and error signal generation part which are shown in FIG. It is a figure which shows an example of the signal waveform of the differential push pull signal DPP obtained by the test | inspection apparatus according to embodiment of this invention. It is a block diagram which shows the more detailed structure of the test | inspection apparatus shown in FIG. It is a block diagram which shows the control structure of the test | inspection apparatus according to embodiment of this invention. It is a flowchart which shows the whole process sequence of the test | inspection method according to embodiment of this invention. It is a flowchart which shows the process sequence of the input data processing subroutine shown to step S12 of FIG. It is a figure which shows an example of the process result of the input data processing subroutine shown to step S12 of FIG. It is a flowchart which shows the process sequence of the noise removal subroutine shown to step S14 of FIG. It is a figure which shows an example of the pattern determined as noise in the noise removal subroutine shown to step S14 of FIG. It is a flowchart which shows the process sequence of the integrated process subroutine shown to step S16 of FIG. It is a figure which shows an example of the maximum point integrated in the integration process subroutine shown to step S16 of FIG. It is a flowchart which shows the process sequence of the mask area | region determination subroutine shown to step S18 of FIG. It is a figure which shows typically the folding | turning part and eccentric part which appear in the tracking error signal according to the embodiment of the present invention. It is a figure which shows an example of the process in the mask area | region determination subroutine shown to step S18 of FIG. It is a flowchart which shows the process sequence of the smoothing process subroutine shown to step S22 of FIG. It is a figure which shows an example of the process in the smoothing process subroutine shown to step S22 of FIG.

  Embodiments of the present invention will be described in detail with reference to the drawings. Note that the same or corresponding parts in the drawings are denoted by the same reference numerals and description thereof will not be repeated.

<Configuration of optical pickup and inspection device>
FIG. 1 is a schematic configuration diagram of a system according to an embodiment of the present invention. FIG. 2 is a diagram showing a beam condensing position on the optical recording medium in the optical pickup according to the embodiment of the present invention.

  Referring to FIG. 1, the system according to the present embodiment rotates optical pickup 101 as an inspection target and optical recording medium (hereinafter also referred to as “optical disk”) 106 from which optical pickup 101 reads information. A spindle motor 112 for driving and an inspection device 11 for inspecting the optical pickup 101 are included.

(1. Optical pickup)
The optical pickup 101 irradiates a light spot on the information recording surface of the optical disk 106 rotated by the spindle motor 112 and tracks the light spot on a target track formed on the optical disk 106. As a tracking control method, a phase difference method, a three-beam method, a push-pull method, a differential push-pull method, and the like are known.

  In a typical differential push-pull method, a diffraction grating is installed on the optical path from the light source to the optical disk 106, and the light (laser light) emitted from the light source includes the main beam 121 and at least two sub beams 122 and 123. Separate into multiple beams. The transmitted light (0th order light) of the diffraction grating becomes the main beam 121, and the ± 1st order diffracted light becomes the sub beams 122 and 123. These three beams (the main beam 121 and the two sub beams 122 and 123) are irradiated onto the optical disc 106 with a predetermined relative relationship.

  Push-pull signals based on three reflected lights generated by reflecting the main beam 121 and the two sub beams 122 and 123 on the optical disk 106 are detected. A differential signal (differential push-pull signal or DPP signal) of these push-pull signals is used as a tracking error signal.

  The optical pickup 101 according to the present embodiment includes a light source 102, a collimator lens 103, a diffraction grating 104, a beam splitter 105, an objective lens 107, an actuator 111, a condenser lens 108, a cylindrical lens 109, an optical And a detection unit 110.

As shown in FIG. 2, in the tracking control, the condensing position (displacement of the objective lens 107) is controlled so that the main beam 121 is irradiated to the center of the read target track 126 on the optical disc. At this time, the sub beams 122 and 123 are irradiated with a shift of 1/2 track to the left and right with respect to the main beam 121, respectively.

  Since the basic configuration and operation of such an optical pickup are known, a more detailed description will not be given.

(2. Inspection device)
The inspection device 11 is connected to the optical pickup 101, gives a control command for inspection to the actuator 111 to perform a predetermined operation, and based on the detection results of the three reflected lights output from the light detection unit 110, The amount of eccentricity of the pickup 101 is calculated. More specifically, first, the inspection apparatus 11 gives a control command to the spindle motor 112 to rotate the optical disk 106 at a predetermined speed, and only the focus servo control is effective with the tracking servo control stopped. As described above, a control command is given to the actuator 111. Subsequently, the inspection apparatus 11 gives a control command to the actuator 111 to displace the condensing positions of the main beam 121 and the sub beams 122 and 123 in the radial direction of the optical disc 106 (direction intersecting the track). At this time, the actuator 111 is given a control command so that the converging positions of the three beams reciprocate between the inner and outer peripheral sides of the range where the track of the optical disk 106 is formed. That is, the inspection apparatus 11 gives a control command for traversing the objective lens 107.

  Finally, the inspection apparatus 11 calculates the amount of eccentricity of the optical pickup 101 based on the time series data of the tracking error signal (differential push-pull signal) obtained by this traverse.

  The inspection apparatus 11 includes an error signal generation unit 221, a computer 201, and a display 207.

  The error signal generation unit 221 is an example of a tracking error signal based on detection results (three push-pull signals detected by detectors 131, 132, and 133 described later) detected by the light detection unit 110 of the optical pickup 101. The differential push-pull signal DPP is generated. The configuration and processing for generating this tracking error signal will be described later.

  The computer 201 governs overall control of the inspection apparatus 11. The computer 201 typically includes a processor for performing arithmetic processing, a memory for storing programs and data, and various peripheral circuits.

  The display 207 displays to the user the amount of eccentricity calculated by the computer 201 and the quality determination result based on the amount of eccentricity.

<Tracking error signal>
FIG. 3 is a block diagram showing a more detailed configuration of the light detection unit 110 and the error signal generation unit 221 shown in FIG.

Referring to FIG. 3, the detector 131 of the light detection unit 110 is a four-divided photodetector for receiving the main beam, and is divided into four light receiving elements (A, B, C) as a light receiving surface. , D). The detector 132 of the light detection unit 110 is a two-divided photodetector for receiving a sub beam, and has a light receiving element (E, F) divided into two as a light receiving surface. Similarly, the detector 133 of the light detection unit 110 is a two-divided light detector for receiving a sub beam, and has a light receiving element (G, H) divided into two as a light receiving surface. These light receiving elements (A to H
) Detects light independently and outputs a signal corresponding to the detected light intensity.

  The error signal generation unit 221 generates a main push-pull signal MPP of the detector 131, a sub push-pull signal SPP1 of the detector 132, and a sub push-pull signal SPP2 of the detector 133. Furthermore, the error signal generation unit 221 generates a differential push-pull signal DPP based on the push-pull signals MPP, SPP1, and SPP2.

  More specifically, the error signal generation unit 221 includes subtracters 141, 142, 143, 146, an adder 144, and a constant multiplier 145. Typically, all or a part of these is constituted by an analog circuit such as an operational amplifier.

  The differential push-pull signal DPP output from the error signal generator 221 is as follows.

DPP = MPP-k × SPP (1)
MPP = (a + d) − (b + c) (2)
SPP = SPP1 + SPP2, SPP1 = ef, SPP2 = g−h (3)
Here, the coefficient k is a constant for correcting the difference between the light intensity of the 0th order light and the light intensity of the + 1st order light and the −1st order light.

  FIG. 4 shows an example of a signal waveform of differential push-pull signal DPP obtained by the inspection apparatus according to the embodiment of the present invention. The differential push-pull signal DPP shown in FIG. 4 is an example of a temporal change characteristic obtained when the actuator 111 (FIG. 1) traverses the range where the track of the optical disk 106 is formed. In FIG. 4, the X axis indicates the time axis, and the Y axis indicates the signal strength of the differential push-pull signal DPP.

  FIG. 4A shows a time waveform when the main beam 121 and the sub beams 122 and 123 are ideally generated without the optical axis shift of the diffraction grating 104 and the like.

  However, as shown in FIG. 4B, the waveform of the differential push-pull signal DPP that is actually measured has a change in the amplitude. The peak portion 81 having a large amplitude, the valley portion 82 having a large amplitude, and the like. Appears periodically.

  Therefore, in the inspection apparatus 11 according to the present embodiment, the ratio between the maximum amplitude Amax and the minimum amplitude Amin of the differential push-pull signal DPP obtained when the objective lens 107 is traversed is used as an eccentric amount (or eccentric ratio). The quality of the optical pickup is determined based on the calculated amount of eccentricity.

<Hardware configuration of inspection device>
FIG. 5 is a block diagram showing a more detailed configuration of the inspection apparatus 11 shown in FIG.

  Referring to FIG. 5, the computer 201 of the inspection apparatus 11 includes an A / D (Analog to Digital) conversion unit 202, a storage device 203, a processor 204, an output unit 205, an input unit 206, and a communication interface 208A. , 208B.

  The A / D converter 202 converts the differential push-pull signal DPP (analog voltage signal) generated by the error signal generator 221 into digital time-series data.

The storage device 203 stores a program executed by the processor, data given from the outside of the computer 201, data generated inside the computer 201, and the like. Data given from the outside of the computer 201 includes, for example, time-series data of the differential push-pull signal DPP output from the A / D converter 202, and parameter values used in the inspection method according to the present embodiment described later. Etc. The data generated inside the computer 201 includes, for example, data in the middle of processing of the inspection method according to the present embodiment output from the processor 204, processing results, and results of determining whether the optical pickup 101 is good or bad. Typically, the storage device 203 includes a volatile storage device such as a DRAM (Dynamic Random Access Memory) and a nonvolatile storage device such as a FLASH memory or a hard disk.

  The processor 204 executes the inspection method according to the present embodiment in accordance with a program stored in the storage device 203 or the like. More specifically, the processor 204 gives a control command to the actuator 111 and the spindle motor 112 through the communication interfaces 208A and 208B, and performs the time series data of the differential push-pull signal DPP stored in the storage device 203. Then, the eccentricity of the optical pickup 101 to be inspected is calculated by performing data processing to be described later. Further, the processor 204 determines the quality of the optical pickup 101 based on the calculated amount of eccentricity.

  Based on the calculation result from the processor 204, the output unit 205 outputs a signal for displaying the calculation result as a character or a graph to the display 207.

  The input unit 206 includes a keyboard, a mouse, and the like, and receives data and commands input by the user. Further, the input unit 206 may include a data reading device from a recording medium such as a CD-ROM. In this case, a program stored in the CD-ROM is read and stored in the storage device 203.

<Outline of inspection method>
In the inspection method according to the present embodiment, the (simple) maximum point and (simple) minimum point included in the tracking error signal (differential push-pull signal DPP) acquired when the optical pickup 101 is traversed are extracted. Thus, using the evaluation criteria determined based on the extracted maximal points and statistic values, only the extracted maximal points and minimal points that are effective for calculating the eccentricity are selected. In this way, since only effective local maximum and local minimum points are selected, the calculation accuracy of the optical pickup eccentricity can be improved, and the evaluation criterion is determined using the statistic. The influence of fluctuation and the like can be suppressed, and thereby robustness can be enhanced.

  In the inspection method according to the present embodiment, when determining the mask area corresponding to the non-track range of the optical recording medium (optical disk), the pitch width (from one maximum point to the next maximum point) included in the tracking error signal is determined. And a range from a certain minimum point to the next minimum point), and a threshold value based on these extracted pitch width statistics is determined. Then, using the determined threshold value, an eccentric portion corresponding to the track range and a folded portion corresponding to the non-track range are distinguished. That is, the outermost circumference and the innermost circumference to be traversed are specified. As described above, since the threshold value for distinguishing the eccentric portion from the folded portion is dynamically determined, it is highly robust without being affected by the pitch of the track formed on the optical recording medium (optical disc). The mask region can be determined with high accuracy while maintaining the characteristics.

<Control structure of inspection device>
FIG. 6 is a block diagram showing a control structure of inspection apparatus 11 according to the embodiment of the present invention. The control structure shown in FIG. 6 is typically provided by the processor 204 executing a program. Note that all or part of the control structure shown in FIG. 6 may be implemented by hardware.

  Referring to FIG. 6, inspection apparatus 11 calculates the amount of eccentricity of optical pickup 101 based on the tracking error signal (differential push-pull signal DPP) output from optical pickup 101. More specifically, the inspection apparatus 11 selects, as its control structure, a command generation unit 250, a data buffer 252, an extreme value extraction unit 254, an extreme value storage unit 256, and a threshold value determination unit 258. Unit 260, noise removal unit 262, integration processing unit 264, effective data storage unit 266, mask area determination unit 268, count unit 270, extraction unit 272, smoothing unit 274, and eccentricity calculation unit 276 and a pass / fail judgment unit 278.

  The command generation unit 250 generates a control command for causing a traverse operation in response to a start command from a user or the like, and provides the control command to the optical pickup 101. More specifically, the command generation unit 250 generates a control command for rotating the spindle motor 112 (FIG. 1) at a predetermined speed, and only performs focus servo control while the tracking servo control by the actuator 111 is stopped. Generates a control command for enabling.

  The data buffer 252 stores time-series data 252a of the differential push-pull signal DPP obtained when the optical pickup 101 is traversed.

  That is, the command generation unit 250, the error signal generation unit 221 (FIG. 5), and the data buffer 252 cooperate to set the irradiation positions of the three beams (the main beam 121 and the two sub beams 122 and 123) on the optical disc 106. The time series data 252a of the tracking error signal (differential push-pull signal DPP) obtained when reciprocating in the direction intersecting the track (when traversing) is acquired.

  The extreme value extraction unit 254 extracts local maximum points and local minimum points included in the time-series data 252 a stored in the data buffer 252. That is, the extremum extraction unit 254 identifies the temporal signal strength switching points (the switching point from increasing to decreasing and the switching point from decreasing to increasing) indicated in the time-series data 252a. More specifically, the extreme value extraction unit 254, for each point included in the time series data 252a, when a certain point has an amplitude greater than or equal to the amplitude of two points located on the front and back on the time axis, A point is determined to be a local maximum point, and when a certain point has an amplitude equal to or lower than the amplitude of two points positioned on the front and back on the time axis, the point is determined to be a local minimum point. Then, the extreme value extraction unit 254 associates the extracted maximum point and minimum point position (time) with the corresponding amplitude (signal intensity), and outputs the result to the extreme value storage unit 256.

  The extreme value storage unit 256 stores the extreme value information 256 a extracted by the extreme value extraction unit 254. In this extreme value information 256a, data indicating the position (time) of the extracted local maximum point and local minimum point may be stored in association with data indicating the magnitude of the corresponding amplitude (signal strength). By referring to the time series data 252a of the differential push-pull signal DPP, only the position (time) corresponding to the type of extracted maximum point or minimum point (a mark to be described later) may be included.

  The threshold value determination unit 258 calculates an evaluation criterion for selecting a maximum point and a minimum point that are effective for calculating the amount of eccentricity, based on the statistic of the extreme value information 256a. That is, the threshold value determination unit 258 corresponds to means for determining an evaluation criterion. More specifically, the threshold value determination unit 258 uses the threshold value (maximum) used in the selection unit 260 based on the statistics (typically, average value and standard deviation) of the extreme value information 256a. The value threshold value UpThresh and the minimum value threshold value DownThresh) are dynamically determined. Note that the threshold value determination unit 258 preferably uses the statistical amount of the time-series data 252a when determining the threshold value.

  The selection unit 260 calculates the amount of eccentricity among the maximum and minimum points included in the extreme value information 256a stored in the extreme value storage unit 256 according to the evaluation criteria determined by the threshold value determination unit 258. Select valid local maxima and minima. More specifically, the selection unit 260 determines an effective maximum value from among the maximum points included in the extreme value information 256a that has a value larger than the maximum threshold value UpThresh determined by the threshold value determination unit 258. Select as a point. Similarly, the selection unit 260 selects a minimum point included in the extreme value information 256a as an effective minimum point if its magnitude is smaller than the minimum value threshold value DownThresh determined by the threshold value determination unit 258. To do.

  The noise removing unit 262 removes a noise component from the maximum and minimum points effective for calculating the amount of eccentricity selected by the selection unit 260. More specifically, the noise removing unit 262 removes a portion where a plurality of maximum points or a plurality of minimum points are concentrated in a relatively short time. In other words, the noise removal unit 262 removes the local maximum point and the local minimum point that are excessively detected by the high frequency component included in the tracking error signal (differential push-pull signal DPP). Therefore, the noise removal unit 262 functions as a kind of low-pass filter.

  The integration processing unit 264 detects the extreme points detected corresponding to substantially the same track among the effective maximum points and the effective effective minimum points after the noise component is removed by the noise removing unit 262. To integrate. That is, the integration processing unit 264 treats a portion where a maximum point or a minimum point appears continuously within a predetermined time (ConnectSize) on a time axis as one maximum point or a minimum point. Merge points or local minimums into a single local maximum or local minimum. ConnectSize used for integration processing in the integration processing unit 264 is set in advance by a user or the like.

  The effective data storage unit 266 stores the effective data group 266a obtained as a result of a series of processes in the selection unit 260, the noise removal unit 262, and the integration processing unit 264 as described above.

  Based on the effective data group 266a, the mask area determination unit 268 determines a folded portion and an eccentric portion in the measured differential push-pull signal DPP. Subsequently, the mask region determination unit 268 determines a mask region for excluding the folded portion from the calculation of the amount of eccentricity. That is, the mask area determination unit 268 determines a mask area corresponding to the non-track range of the optical disc 106 in the time series data 252a of the differential push-pull signal DPP based on the effective maximum point and the effective minimum point. Then, the mask area determination unit 268 outputs information indicating the determined mask area to the extraction unit 272 and the count unit 270.

  The extraction unit 272 extracts and extracts (effective) local maximum points and local minimum points existing in regions other than the mask region from the effective local maximum points and effective local minimum points included in the effective data group 266a. A series of data is generated by arranging local minimum points in time series. The extraction unit 272 outputs the generated series of data to the smoothing unit 274.

  The smoothing unit 274 performs a filtering process on a series of data including the local maximum points and the local minimum points extracted by the extraction unit 272. Typically, the smoothing unit 274 removes high frequency components using a known method such as moving average. Note that the smoothing unit 274 receives the sensitivity parameter n as a parameter of the filtering process.

As described above, the extraction unit 272 and the smoothing unit 274 reciprocate in the direction in which the irradiation position intersects the track of the optical disk 106 based on the effective maximum point and the effective minimum point included in the region other than the mask region. A temporal change characteristic of the tracking error signal (differential push-pull signal DPP) accompanying the above is generated.

  The counting unit 270 counts the number of traverses based on a series of data (time series data) included in an area other than the mask area among the time series data included in the valid data group 266a. That is, the count unit 270 calculates the wave number of the signal in an area other than the mask area.

  The eccentricity amount calculation unit 276 calculates the eccentricity amount of the optical pickup 101 based on the temporal change characteristic (time-series data) after being filtered by the smoothing unit 274. More specifically, the eccentric amount calculation unit 276 extracts the maximum amplitude and the minimum amplitude included in the temporal change characteristic after the filtering process, and calculates the ratio of the extracted amplitudes as the eccentric amount.

  The quality determination unit 278 compares the eccentricity calculated by the eccentricity calculation unit 276 with a preset threshold value, and outputs a quality determination result for the target pickup. Typically, the quality determination unit 278 determines that the target optical pickup 101 is a defective product when the calculated amount of eccentricity exceeds the threshold value, and if not, It is determined that the optical pickup 101 is a good product.

<Overall procedure>
Next, an overall processing procedure of the inspection method according to the present embodiment will be described.

  FIG. 7 is a flowchart showing an overall processing procedure of the inspection method according to the embodiment of the present invention. Each step shown in FIG. 7 is basically executed by the processor 204 (FIG. 5).

  Referring to FIG. 7, upon receiving a start command from a user or the like, processor 204 gives a control command to spindle motor 112 to rotate optical disk 106 at a predetermined speed (step S2). Subsequently, the processor 204 gives a control command to the actuator 111 so that only the focus servo control is valid in a state where the tracking servo control is stopped (step S4), and is output from the error signal generation unit 221. Data of the differential push-pull signal DPP, which is an error signal, is stored in time series (step S6).

  Then, the processor 204 determines whether or not the specified number of traverse operations by the optical pickup 101 have been completed (step S8). If the predetermined traverse operation by optical pickup 101 is not completed (NO in step S8), the processes in steps S4 and S6 are repeated.

  If the predetermined traverse operation by optical pickup 101 is completed (YES in step S8), processor 204 extracts the maximum point and the minimum point included therein based on the time series data of differential push-pull signal DPP. (Step S10).

  Thereafter, the processor 204 executes an input data processing subroutine, and selects a local maximum point and a local minimum point that are effective for calculating the amount of eccentricity from the local maximum point and local minimum point extracted in step S10 (step S12).

  Subsequently, the processor 204 executes a noise removal subroutine, and removes a noise component from the effective maximum point and the effective minimum point selected in Step S12 (Step S14).

  Subsequently, the processor 204 executes an integration processing subroutine, and integrates a plurality of continuous local maximum points or local minimum points into a single local maximum point or local minimum point (step S16). The local maximum points and local minimum points generated after the processing of step S16 are stored as a valid data group 266a (FIG. 6).

  Subsequently, the processor 204 executes a mask area determination subroutine to determine a folded portion and an eccentric portion included in the time series data of the measured differential push-pull signal DPP (step S18). That is, the processor 204 determines a mask area to be excluded from the eccentricity calculation target in the differential push-pull signal DPP.

  Subsequently, the processor 204 counts the number of traverses based on the time-series data included in the area other than the mask area determined in step S18 among the time-series data included in the valid data group 266a (step S20). .

  Subsequently, the processor 204 extracts an effective maximum point and an effective minimum point included in an area other than the mask area determined in step S18 from the time series data included in the effective data group 266a (step S22). . Then, the processor 204 executes a smoothing processing subroutine, and performs a filtering process on the temporal change characteristic defined by the maximum point and the minimum point extracted in step S22 (step S24).

  Subsequently, the processor 204 extracts the maximum amplitude and the minimum amplitude included in the time-series data after the filtering process in step S24, and calculates the ratio of the extracted amplitudes as an eccentric amount (step S26). Then, the processor 204 compares the amount of eccentricity calculated in step S26 with a preset threshold value to determine whether the target pickup is good or bad (step S28).

  Finally, the processor 204 displays the eccentricity calculated in step S26 and / or the quality determination result determined in step S28 on the display 207 or the like (step S30).

<Input data processing subroutine>
Next, processing contents of the input data processing subroutine executed in step S12 of the flowchart of FIG. 7 will be described.

  FIG. 8 is a flowchart showing the processing procedure of the input data processing subroutine shown in step S12 of FIG.

  Referring to FIG. 8, processor 204 calculates an average value (average amplitude value) m for the entire time-series data of the measured differential push-pull signal DPP (step S100). Subsequently, the processor 204 corrects the plurality of maximum points extracted in step S10 shown in FIG. 7 with the average value m (step S102).

  That is, the average value m represents an offset included in the differential push-pull signal DPP that is a tracking error. Therefore, the offset of the measured time-series data of the raw differential push-pull signal DPP is corrected using this average value m. More specifically, the processor 204 subtracts the average value m from the amplitudes of the extracted local maximum points to calculate the local maximum amplitude (after correction).

By including such processing for correcting the offset, the optical disc 106 and / or
Alternatively, even when an offset occurs due to some cause in the optical pickup 101, the amount of eccentricity can be accurately calculated.

  Subsequently, the processor 204 calculates a statistic about the amplitude (after correction) of the maximum point (step S104). More specifically, the processor 204 calculates the average value mU and the standard deviation σU for the amplitude (after correction) of the maximum point.

  Similarly, the processor 204 corrects the plurality of minimum points extracted in step S10 shown in FIG. 7 with the average value m (step S106). That is, the processor 204 subtracts the average value m from the amplitudes of the extracted minimum points, and calculates the amplitude (after correction) of the minimum points. Further, the processor 204 calculates a statistic about the amplitude (after correction) of the minimum point (step S108). More specifically, the processor 204 calculates an average value mD and standard deviation σD for the amplitude (after correction) of the local minimum point.

  After that, the processor 204 determines a maximum value threshold value UpThresh and a minimum value threshold value DownThresh (step S110). More specifically, the processor 204 calculates the maximum value threshold value UpThresh and the minimum value threshold value DownThresh according to the following mathematical formula.

Maximum threshold value UpThresh = average value m + average value mU−recipe value k × standard deviation σU (4)
Minimal value threshold DownThresh = average value m−average value mD + recipe value k × standard deviation σD (5)
Here, the recipe value k is a parameter indicating to what extent the maximum threshold value UpThresh and the minimum value threshold DownDown are included from the average value, and is a system incorporating the optical pickup 101 and the optical pickup 101. Determined by considering the whole. For example, “2” (that is, average value−2σ) is set as the recipe value k.

  In this way, the processor 204 determines the maximum threshold value UpThresh based on the average value and standard deviation of the amplitudes of the maximum points, and determines the minimum value based on the average value and standard deviation of the amplitudes of the minimum points. The threshold value DownThresh is determined.

  Subsequently, the processor 204 selects, from among the maximum points extracted in step S10, those having an amplitude larger than the maximum value threshold value UpThresh as effective maximum points for calculating the amount of eccentricity (step S112). . Similarly, the processor 204 selects a minimum point extracted in step S10 as an effective minimum point for calculating the amount of eccentricity (step S114). The minimum point is smaller in amplitude than the minimum threshold value DownThresh. . Furthermore, the processor 204 outputs the maximum point and the minimum point selected as effective points for calculating the amount of eccentricity as the extreme value information 256a (step S116). Then, the process returns to the main routine.

  FIG. 9 is a diagram showing an example of the processing result of the input data processing subroutine shown in step S12 of FIG. FIG. 9 shows an example of the frequency distribution of the maximum points extracted by the local maximum and minimum point extraction processing shown in step S10 of FIG. That is, the horizontal axis of FIG. 9 indicates the amplitude of the maximum point, and the vertical axis indicates the frequency.

It is assumed that a maximum value threshold value UpThresh having a size as shown in FIG. 9 is determined based on the statistics about these extracted maximum points. Then, a local maximum point having an amplitude larger than the local maximum threshold value UpThresh is selected as an effective local maximum point. On the other hand, a local maximum point having an amplitude smaller than the local maximum threshold value UpThresh is determined as an invalid local maximum point. That is, only the local maximum point located on the right side of the local maximum threshold value UpThresh shown in FIG. 9 is selected as an effective local maximum point.

  Although not shown, an effective local minimum point is selected for the local minimum point according to the same process.

  As described above, the evaluation criterion is dynamically determined based on the statistics of the maximum point and the minimum point, and the effective maximum point and the effective minimum point are selected according to the dynamically determined evaluation criterion. Therefore, even when affected by various disturbances (effects of typical sudden noise components), it is possible to select an effective maximum point and an effective minimum point while maintaining high robustness.

<Noise elimination subroutine>
Next, the processing content of the noise removal subroutine executed in step S14 in the flowchart of FIG. 7 will be described. In this noise removal subroutine, noise components due to the influence of foreign matter existing on the optical disc 106, noise generated by the light detection unit 110 itself, disturbance, and the like are removed. That is, a signal other than the true value of the tracking error signal (differential push-pull signal DPP) is used as a noise component.

  FIG. 10 is a flowchart showing the processing procedure of the noise removal subroutine shown in step S14 of FIG. FIG. 11 is a diagram showing an example of a pattern determined as noise in the noise removal subroutine shown in step S14 of FIG.

  Referring to FIG. 10, processor 204 sets a processing target range in a range including the first data with respect to the time-series data of the acquired differential push-pull signal DPP (step S <b> 200). This processing target range corresponds to a kind of processing window set for time-series data, and includes a predetermined number of continuous data.

  Subsequently, the processor 204 determines whether or not an effective maximum point or minimum point appears in a predetermined pattern in a predetermined number of data included in the set processing target range (step S202).

  If an effective maximum or minimum point does not appear in a predetermined pattern in a predetermined number of data included in the set processing target range (NO in step S202), the process proceeds to step S206.

  On the other hand, if a valid maximum or minimum point appears in a predetermined pattern in a predetermined number of data included in the set processing target range (YES in step S202), the processor 204 is unnecessary. A local maximum or minimum point is deleted (step S204).

  More specifically, the processor 204 adds (a1) “maximum point” → “non-maximum point and non-minimum point” → “maximum point” → “non-maximum point” to the data included in the set processing target range. And (a2) “maximum point” → “non-maximum point and non-minimum point” → “maximum point”, (a3) “maximum point” → “maximum point”, (a4) “maximum point” → If any pattern of “Non-maximal point and non-minimum point” → “Non-maximum point and non-minimum point” → “Maximum point” appears, the effective maximum point included in the processing target range Among them, the other local maximum points are deleted (invalidated) while leaving the local maximum points having larger amplitudes.

Similarly, the processor 204 adds (b1) “minimum point” → “non-maximum point and non-minimum point” → “minimum point” → “non-maximum point and non-minimum” to the data included in the set processing target range. point",
(B2) “Minimum point” → “Non-maximal point and non-minimum point” → “Minimum point”, (b3) “Minimum point” → “Minimum point”, (b4) “Minimum point” → “Non-maximum point and non- If any pattern of “Minimum point” → “Non-maximal point and non-minimum point” → “Minimum point” appears, the amplitude of the effective minimum points included in the processing target range is more Delete (invalidate) other local minimum points, leaving small local points.

  In other words, as shown in FIGS. 11A to 11D, high-frequency component noise is removed from changes in the amplitude of the differential push-pull signal DPP caused by crossing each track. In FIG. 11A to FIG. 11D, “◯” indicates a maximum point (or minimum point), and “■” indicates a point that is not a maximum point or a minimum point.

  More specifically, in FIG. 11A, the differential push-pull signal DPP includes (a1) “maximum point” → “non-maximum point and non-minimum point” → “maximum point” → “non-maximum point and An example in which a pattern of “non-minimal points” appears is shown. In the example shown in FIG. 11A, a drop in amplitude occurs in the vicinity of the original peak of the differential push-pull signal DPP. As a result, two non-maximal points 402 are located in a temporally close range. Three local maximum points 400 occur between the two.

  FIG. 11B shows an example in which a pattern of (a2) “maximum point” → “non-maximum point and non-minimum point” → “maximum point” appears in the differential push-pull signal DPP. In the example shown in FIG. 11B, a drop in amplitude occurs in the vicinity of the original peak of the differential push-pull signal DPP, and as a result, one non-maximal point 402 is located in a temporally close range. Two local maximum points 400 occur between the two.

  FIG. 11C shows an example in which a pattern of (a3) “maximum point” → “maximum point” appears in the differential push-pull signal DPP. In the example shown in FIG. 11C, the amplitude change in the vicinity of the original peak of the differential push-pull signal DPP is gentle. As a result, two local maximum points 400 are detected in a temporally close range. Yes.

  FIG. 11D shows the differential push-pull signal DPP. (A4) “maximum point” → “non-maximum point and non-minimum point” → “non-maximum point and non-minimum point” → “maximum point” An example in which the pattern appears. In the example shown in FIG. 11D, a drop in amplitude occurs in the vicinity of the original peak of the differential push-pull signal DPP. As a result, two non-maximal points 402 are located in a temporally close range. Two local maximum points 400 occur between the two.

  In the noise removal subroutine processing according to the present embodiment described above, the noise components as described above are removed. In addition to the patterns shown in FIGS. 11A to 11D, a generation pattern peculiar to a noise component can be used as a condition.

  Referring to FIG. 10 again, the processor 204 determines whether or not a processing target range has been set for all of the acquired time-series data of the differential push-pull signal DPP (step S206). That is, the processor 204 determines whether or not the occurrence of a predetermined pattern is determined in all the time series data of the differential push-pull signal DPP.

  If there is a portion in the time series data of the acquired differential push-pull signal DPP where the processing target range is not set (NO in step S206), the processor 204 sets the current processing target range for one data. And a new processing target range is set (step S208). And the process after step S202 is repeated.

If the processing target range has been set for all the time series data of the acquired differential push-pull signal DPP (YES in step S206), the process returns to the main routine.

  As described above, the processor 204 has a plurality of effective maximum points and a plurality of effective maximum points and effective minimum points that are obtained as a result of the execution of the input data processing subroutine and appear in a predetermined pattern. And extracting a part of a plurality of effective local minimum points appearing in the predetermined pattern, and removing a part of the plurality of effective local minimum points appearing in the predetermined pattern. Remove.

<Integration processing subroutine>
Next, processing contents of the integration processing subroutine executed in step S16 in the flowchart of FIG. 7 will be described. In this integration processing subroutine, local maximum points and local minimum points detected correspondingly to substantially the same track of the optical disk 106 are integrated.

  FIG. 12 is a flowchart showing the processing procedure of the integration processing subroutine shown in step S16 of FIG. FIG. 13 is a diagram showing an example of local maximum points integrated in the integration processing subroutine shown in step S16 of FIG.

  Referring to FIG. 12, processor 204 selects a local maximum point that exists first on the time axis from among the effective local maximum points output after execution of the above-described noise removal subroutine (step S300). Subsequently, the processor 204 determines whether or not there is another local maximum point in the range before and after a predetermined time (ConnectSize) on the time axis with the position of the local maximum point selected as the target at the center (step S302). ).

  If there is no other maximum point in the range around the predetermined time on the time axis with the position of the maximum point selected as the target as the center (NO in step S302), the process proceeds to step S306.

  If there is another local maximum point in the range around the predetermined time on the time axis centering on the position of the local maximum point selected as the target (YES in step S302), the amplitude of the local maximum points is the largest. The other local maximum points are deleted (invalidated) while leaving the large local maximum points (step S304).

  Subsequently, the processor 204 determines whether or not all of the valid local maximum points output after the execution of the above-described noise removal subroutine have been selected (step S306). That is, the processor 204 determines whether or not the integration process has been executed for all local maximum points.

  If there is a valid maximum point output after execution of the above-described noise removal subroutine that has not been selected as a target (NO in step S306), the processor 204 determines that the valid target currently selected as the target is valid. The local maximum point located next to the local maximum point is selected as a new target (step S308). And the process after step S302 is repeated.

  If all of the effective maximum points output after execution of the above-described noise removal subroutine are selected as targets (YES in step S306), among the effective minimum points output after execution of the above-described noise removal subroutine, The minimal point that exists first on the time axis is selected as a target (step S310). Subsequently, the processor 204 determines whether or not there is another local minimum point in the range before and after a predetermined time (ConnectSize) on the time axis with the position of the local minimum point selected as the target at the center (step S312). ).

  If there is no other minimum point in the range before and after the predetermined time on the time axis centering on the position of the minimum point selected as the target (NO in step S312), the process proceeds to step S316.

  If there is another local minimum point in the range around the predetermined time on the time axis centering on the position of the local minimum point selected as the target (YES in step S312), the amplitude of the local minimum points is the largest. Other minimum points are deleted (invalidated) while leaving a small minimum point (step S314).

  For example, as shown in FIG. 13, it is assumed that effective maximum points 411, 412 and 413 are extracted and selected on the differential push-pull signal DPP. It is assumed that the local maximum point 412, the local maximum point 412, and the local maximum point 413 exist in this order on the time axis. At this time, the local maximum point 412 is located in the range ahead of the local maximum point 412 by ConnectSize on the time axis, and the local maximum point 413 is within the range backward from the local maximum point 412 by the amount of ConnectSize on the time axis. Suppose it is located.

  If the amplitude of the maximum point 412 is the maximum among the maximum point 411, the maximum point 412, and the maximum point 413, the maximum point 412 and the maximum point 413 are deleted (invalidated). As a result, the three effective maximum points 411, 412 and 413 are integrated into a single maximum point 411 having the largest amplitude.

Note that the minimum points are also integrated by the same processing as in FIG.
Referring to FIG. 12 again, the processor 204 determines whether or not all the valid local minimum points output after the execution of the above-described noise removal subroutine have been selected (step S316). That is, the processor 204 determines whether or not the integration process has been executed for all the minimum points.

  If valid minimum points output after execution of the above-described noise removal subroutine remain unselected as targets (NO in step S316), the processor 204 determines that the valid target currently selected as a target is valid. The maximum point located next to the minimum point is selected as a new target (step S318). And the process after step S312 is repeated.

  If all of the effective local minimum points output after execution of the above-described noise removal subroutine have been selected (YES in step S316), the process returns to the main routine.

  As described above, the processor 204 extracts a plurality of effective local maximum points or effective local minimum points existing within a predetermined period (ConnectSize) on the time axis, and substantially extracts the extracted plurality of effective local maximum points. A single effective maxima is integrated into one effective maxima, and a plurality of effective minima extracted are substantially integrated into a single effective minima.

<Mask area determination subroutine>
Next, the contents of the mask area determination subroutine executed in step S18 of the flowchart of FIG. 7 will be described. In this mask area determination subroutine, the tracking error signal (differential push-pull signal DPP) of the tracking error signal (differential push-pull signal DPP) is determined based on the effective maximum points and effective minimum points obtained by executing the noise removal subroutine and the integration processing subroutine executed earlier. A mask area corresponding to the non-track range of the optical disc 106 is determined from the time series data. In particular, in the present embodiment, the mask region is determined based on a statistic about the pitch width of the waveform appearing in the differential push-pull signal DPP. Therefore, it is possible to avoid a situation in which the mask region is erroneously determined due to the influence of fluctuations and sudden noises appearing in the tracking error signal (differential push-pull signal DPP). Therefore, the mask region can be determined with high robustness.

  FIG. 14 is a flowchart showing the processing procedure of the mask area determination subroutine shown in step S18 of FIG. FIG. 15 schematically shows a folded portion and an eccentric portion appearing in the tracking error signal according to the embodiment of the present invention. FIG. 16 is a diagram showing an example of processing in the mask area determination subroutine shown in step S18 of FIG.

  Referring to FIG. 14, for the effective local maximum points and local minimum points output after execution of the above-described integrated processing subroutine, processor 204 determines a portion where local maximum points and local minimum points appear in a predetermined pattern on the time axis. Specify (step S400). More specifically, the processor 204 determines that “effective maximum point” → “effective minimum point” → “effective maximum point”, and “effective minimum point” → “effective maximum point”. ”→” Specify the part that is an “effective minimum point”. Further, the processor 204 calculates an interval (pitch) for each of the portions identified in step S400 (step S402). That is, the processor 204 calculates the interval between the “effective maximum points” located at both ends for the portion of “effective maximum point” → “effective minimum point” → “effective maximum point”. For the portion of “effective minimum point” → “effective maximum point” → “effective minimum point”, the interval between “effective minimum points” located at both ends is calculated.

  Subsequently, the processor 204 calculates statistics for the plurality of intervals calculated in step S402 (step S404). More specifically, the processor calculates an average value mP and a standard deviation σP for the calculated interval. Further, the processor 204 calculates a pitch threshold value SigmaThresh for determining a mask region using the standard deviation σP for the calculated interval (step S406). More specifically, the processor 204 calculates the pitch threshold SigmaThresh according to the following mathematical formula.

Pitch threshold SigmaThreshold = average value mP + recipe value 1 × standard deviation σT
... (6)
Here, the recipe value l is a parameter that determines the size to be set as the mask area.

  The interval (pitch) calculated in step S402 described above corresponds to a time width of one cycle that appears in the tracking error signal (differential push-pull signal DPP) as shown in FIG. As shown in FIG. 15, in the eccentric part, the differential push-pull signal DPP basically varies periodically at the same pitch (corresponding to one track pitch), whereas in the folded part, It can be seen that the pitch fluctuates with a pitch having a longer time width than the pitch. Therefore, in the above-described step S404, an evaluation criterion (threshold value) for distinguishing (grouping) the pitch difference appearing in the eccentric part and the folded part is determined using statistical processing.

  By using such statistical processing, an evaluation standard (threshold value) according to the type of the target optical disk 106 can be dynamically set even when inspection is performed using a plurality of optical disks 106 having different track pitches. Since it can be determined, the inspection can be basically performed with the same user setting (recipe).

Referring to FIG. 14 again, the processor 204 determines that the interval is larger than the pitch threshold value SigmaThresh among the portions of “effective maximum point” → “effective minimum point” → “effective maximum point”. A thing is specified (step S408). The processor 204 then
The “valid minimum point” included in the part specified in step S408 is deleted (invalidated) (step S410).

  Subsequently, the processor 204 identifies a portion whose interval is larger than the pitch threshold SigmaThresh among the portions of “effective minimum point” → “effective maximum point” → “effective minimum point” ( Step S412). Then, the processor 204 deletes (invalidates) the “effective maximum point” included in the portion specified in step S412 (step S414).

  Finally, the processor 204 determines that there is a valid local maximum point on the time axis from the remaining effective local maximum points and local minimum points excluding the local minimum point and local maximum point deleted (invalidated) in steps S410 and S414. A continuous part or a part where effective minimum points are continuous is determined as a mask region (step S416). At this time, two continuous local maximum points or local minimum points are also included in the mask region. Then, the process returns to the main routine.

  With reference to FIG. 16, an example of processing in the above-described mask area determination subroutine will be illustrated. For example, as shown in FIG. 16A, it is assumed that effective local maximum points and local minimum points appear in the order of the local maximum point 431, the local minimum point 442, and the local maximum point 432, and between the local maximum point 431 and the local maximum point 432. It is assumed that the interval (pitch) 452 is larger than the pitch threshold SigmaThresh.

  In this case, the effective minimum point 442 is deleted (invalidated). As a result, an effective maximum point 431 and an effective maximum point 432 appear in succession, and a range including both the maximum points is determined as the mask region 450.

  Note that invalid local maximum points and local minimum points may be excluded by the noise removal subroutine and the integration processing subroutine that are executed first. In such a case, for example, as shown in FIG. 16B, effective minimum points 445 and effective minimum points 446 appear continuously from the beginning, and a range including both minimum points is a mask. The region 452 is determined.

  As described above, the processor 204 identifies the portion appearing in the order of “effective maximum point” → “effective minimum point” → “effective maximum point”, and the interval between the two effective maximum points. (Pitch) is calculated, and the portion appearing in the order of “effective minimum point” → “effective maximum point” → “effective minimum point” is specified, and the interval between two effective minimum points ( Pitch) is calculated. Then, the processor 204 determines the mask region based on the calculated statistic about the interval (pitch) between the two effective maximum points and the interval (pitch) between the two effective maximum points. The pitch threshold value SigmaThresh, which is an evaluation criterion for the above, is determined. Further, the processor 204 determines that the interval (pitch) between two effective maximum points in the portion that appears in the order of “effective maximum point” → “effective minimum point” → “effective maximum point”. For a portion larger than the pitch threshold SigmaThresh, an effective minimum point included in the portion is excluded (invalidated). In parallel, the processor 204 determines the interval (pitch between two effective local minimum points) in the portion appearing in the order of “effective local minimum point” → “effective local maximum point” → “effective local minimum point”. ) Is excluded (invalidated) from the effective maximum point included in the portion of the portion larger than the pitch threshold SigmaThreshold. Finally, the processor 204 determines at least one of a range in which two effective maximum points exist continuously and / or a range in which two effective minimum points exist continuously as a mask region.

According to the mask area determination subroutine according to the present embodiment, it is possible to obtain an effect that the amount of calculation can be reduced as compared with the method of determining a mask area using frequency conversion as described in the prior art. Furthermore, the method using frequency conversion is suitable for extracting a portion having a relatively high frequency such as an eccentric portion (FIG. 15), but extracting a portion having a relatively low frequency such as a folded portion. However, the mask area determination subroutine according to the present embodiment can solve such a problem.

<Smoothing processing subroutine>
Next, the processing content of the smoothing processing subroutine executed in step S22 of the flowchart of FIG. 7 will be described. In this smoothing processing subroutine, the time series data represented by the effective local maximum points and the effective local minimum points included in the region other than the mask region determined by the previously executed mask region determining subroutine is filtered, Acquire substantial amplitude variation.

  FIG. 17 is a flowchart showing the processing procedure of the smoothing processing subroutine shown in step S22 of FIG. FIG. 18 is a diagram showing an example of processing in the smoothing processing subroutine shown in step S22 of FIG.

  Referring to FIG. 17, processor 204 extracts local maximum points and local minimum points included in regions other than the mask region from among the effective local maximum points and local minimum points output after execution of the above-described mask region determination subroutine ( Step S500). Then, the processor 204 generates time series data in which the extracted maximum points and minimum points are arranged in time order (step S502).

  Subsequently, the processor 204 calculates a smoothed size (2n + 1) from a preset sensitivity parameter n (step S504). Then, the processor 204 performs a filtering process with the calculated smoothing size on the data string generated in step S502 (step S506). More specifically, the processor 204 performs a so-called moving average process, sequentially sets windows having a smoothing size width for the data sequence generated in step S502, and sets the window at each point included in the window. The average value of the amplitude is sequentially calculated as the representative value of the corresponding position.

  Finally, the processor 204 outputs time-series data after the filtering process (step S508). Then, the process returns to the main routine.

  In the above-described smoothing processing subroutine, from the viewpoint of providing a more user-friendly interface, the configuration in which the sensitivity parameter n is changed in the filtering characteristic (frequency cutoff characteristic) is illustrated, but the smoothing size is directly set. You may do it.

  By the smoothing processing subroutine as described above, the raw signal of the tracking error signal (differential push-pull signal DPP) as shown in FIG. 18A is filtered as shown in FIG. A substantial temporal change characteristic of (differential push-pull signal DPP) can be obtained.

<Effect>
According to the inspection apparatus 11 that inspects the optical pickup according to the above-described embodiment, the eccentric amount of the optical pickup can be calculated with high accuracy while having high robustness.

<Other forms>
A program for executing the control as described in the above flow can be provided by an arbitrary method. Such a program is stored in a computer-readable recording medium such as a flexible disk attached to the computer, a CD-ROM (Compact Disk-Read Only Memory), a ROM (Read Only Memory), a RAM (Random Access Memory), and a memory card. And can be provided as a program product. Alternatively, the program can be provided by being recorded on a recording medium such as a hard disk built in the computer. A program can also be provided by downloading via a network.

  Such a program may be a program module that is provided as part of an operating system (OS) of a computer and that calls necessary modules in a predetermined arrangement at a predetermined timing to execute processing. In that case, the program itself does not include the module, and the process is executed in cooperation with the OS. A program that does not include such a module can also be included in the program according to the present embodiment.

  The program according to the present embodiment may be provided by being incorporated in a part of another program. Even in this case, the program itself does not include the module included in the other program, and the process is executed in cooperation with the other program. Such a program incorporated in another program can also be included in the program according to the present embodiment.

  The provided program product is installed in a program storage unit such as a hard disk and executed. The program product includes the program itself and a recording medium on which the program is recorded.

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description of the embodiments but by the scope of claims, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

  DESCRIPTION OF SYMBOLS 11 Inspection apparatus, 101 Optical pick-up, 102 Light source, 103 Collimator lens, 104 Diffraction grating, 105 Beam splitter, 106 Optical recording medium (optical disk), 107 Objective lens, 108 Condensing lens, 109 Cylindrical lens, 110 Light detection part, 111 Actuator, 112 spindle motor, 121 main beam, 122, 123 sub beam, 125, 126, 127 track, 131, 132, 133 detector, 141, 142, 143, 146 subtractor, 144 adder, 145 constant multiplier, 146 Subtracter, 201 Computer, 202 A / D conversion unit, 203 Storage device, 204 Processor, 205 Output unit, 206 Input unit, 207 Display, 208A, 208B Communication interface, 22 DESCRIPTION OF SYMBOLS 1 Error signal generation part, 250 Command generation part, 252 Data buffer, 252a Time series data, 254 Extreme value extraction part, 256 Extreme value storage part, 256a Extreme value information, 258 Threshold value determination part, 260 Selection part, 262 Noise Removal unit, 264 integration processing unit, 266 effective data storage unit, 266a effective data group, 268 mask area determination unit, 270 count unit, 272 extraction unit, 274 smoothing unit, 276 eccentricity calculation unit, 278 pass / fail judgment unit, A ~ H Light receiving element.

Claims (6)

An optical pickup inspection device,
Error signal generating means for generating a tracking error signal based on a detection result detected by the detector of the optical pickup, the optical pickup including a main beam obtained by separating light from the light source and at least two The optical recording medium is configured to irradiate the optical recording medium with a plurality of beams including a sub-beam in a predetermined relative relationship, and the detector includes a plurality of reflected lights generated by reflecting the plurality of beams on the optical recording medium, respectively. And acquiring time series data of the tracking error signal obtained when the irradiation positions of the plurality of beams are reciprocated in a direction intersecting the track of the optical recording medium. Means,
Extraction means for extracting the maximum point and the minimum point included in the time series data;
An evaluation criterion determining means for determining an evaluation criterion based on the extracted statistics of the maximum point and the minimum point;
According to the evaluation criteria, selecting an effective maximum point among the maximum points, a selection means for selecting an effective minimum point among the minimum points;
Mask area determining means for determining a mask area corresponding to a non-track range of the optical recording medium in the time series data based on the effective maximum point and the effective minimum point;
Temporal change of the tracking error signal accompanying the reciprocation of the irradiation position in a direction intersecting the track of the optical recording medium based on the effective maximum point and the effective minimum point included in an area other than the mask area Change characteristic generation means for generating a characteristic;
An inspection apparatus for an optical pickup, comprising: an eccentricity amount calculating means for calculating an eccentricity amount of the optical pickup based on the temporal change characteristic. The evaluation criterion determining means includes
Means for determining a first threshold value based on an average value and a standard deviation of amplitudes of the maximum points;
Means for determining a second threshold value based on an average value and a standard deviation of amplitudes of the local minimum points,
The selection means includes
Means for extracting, as the effective maximum point, one having an amplitude larger than the first threshold value among the maximum points;
The inspection apparatus for an optical pickup according to claim 1, further comprising: means for extracting, as the effective minimum point, one having an amplitude smaller than the second threshold value among the minimum points. The mask area determining means includes
Means for identifying a portion appearing in the order of the effective maximum point, the effective minimum point, and the effective maximum point, and calculating an interval between the two effective maximum points;
Means for identifying a portion appearing in order of the effective minimum point, the effective maximum point, and the effective minimum point, and calculating an interval between the two effective minimum points;
Means for determining a third threshold based on the calculated statistics of the interval between the two effective maximum points and the interval between the two effective minimum points;
Of the portion appearing in the order of the effective maximum point, the effective minimum point, and the effective maximum point, a portion where the interval between two effective maximum points is larger than the third threshold value is included in the portion. Means for excluding said effective local minimum points,
Of the portion appearing in the order of the effective minimum point, the effective maximum point, and the effective minimum point, a portion where the interval between two effective minimum points is larger than the third threshold value is included in the portion. Means for excluding said effective local maximum,
And means for determining at least one of a range in which the two effective maximum points are continuously present and a range in which the two effective minimum points are continuously present as the mask region. Optical pickup inspection device. The change characteristic generation means includes:
Means for generating a series of data by arranging the effective maximum points and the effective minimum points existing in regions other than the mask region in time series;
The optical pickup inspection apparatus according to claim 1, further comprising: a unit that generates the temporal change characteristic by filtering the series of data. And a step of mounting an inspection device on the optical pickup, wherein the optical pickup has a plurality of beams including a main beam obtained by separating light from a light source and at least two sub beams in a predetermined relative relationship. And a detector for detecting a plurality of reflected lights respectively generated by the plurality of beams reflected on the optical recording medium, and the inspection apparatus detects the detected by the detector. A circuit for generating a tracking error signal based on the result, and
Obtaining time-series data of the tracking error signal obtained when the irradiation positions of the plurality of beams are reciprocated in a direction crossing a track of the optical recording medium;
Extracting local maximum points and local minimum points included in the time series data; and
Determining an evaluation criterion based on the extracted statistics of the maximum point and the minimum point;
According to the evaluation criteria, selecting an effective maximum point among the maximum points, and selecting an effective minimum point among the minimum points;
Determining a mask region corresponding to a non-track range of the optical recording medium in the time series data based on the effective maximum point and the effective minimum point;
Temporal change of the tracking error signal accompanying the reciprocation of the irradiation position in a direction intersecting the track of the optical recording medium based on the effective maximum point and the effective minimum point included in an area other than the mask area Generating a characteristic; and
And a step of calculating an eccentric amount of the optical pickup based on the temporal change characteristic. An inspection program for an optical pickup executed by an inspection apparatus including a processor, wherein the inspection program includes the processor,
Based on the detection result detected by the detector of the optical pickup, the optical pickup functions as an error signal generating means for generating a tracking error signal, and the optical pickup has at least two main beams obtained by separating light from the light source. The optical recording medium is configured to irradiate the optical recording medium with a plurality of beams including one sub-beam in a predetermined relative relationship, and the detector includes a plurality of reflections generated by reflecting the plurality of beams on the optical recording medium, respectively. Each of which is configured to detect light, and the inspection program further includes:
Obtaining means for obtaining time-series data of the tracking error signal obtained when the irradiation positions of the plurality of beams are reciprocated in a direction crossing a track of the optical recording medium;
Extraction means for extracting the maximum point and the minimum point included in the time series data;
An evaluation criterion determining means for determining an evaluation criterion based on the extracted statistics of the maximum point and the minimum point;
According to the evaluation criteria, selecting an effective maximum point among the maximum points, a selection means for selecting an effective minimum point among the minimum points;
Mask area determining means for determining a mask area corresponding to a non-track range of the optical recording medium in the time series data based on the effective maximum point and the effective minimum point;
Temporal change of the tracking error signal accompanying the reciprocation of the irradiation position in a direction intersecting the track of the optical recording medium based on the effective maximum point and the effective minimum point included in an area other than the mask area Change characteristic generation means for generating a characteristic;
An inspection program for an optical pickup that functions as an eccentricity calculating means for calculating an eccentricity of the optical pickup based on the temporal change characteristic.

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