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WO2018105462A1 - Procédé et programme de traitement de signaux - Google Patents

Procédé et programme de traitement de signaux Download PDF

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Publication number
WO2018105462A1
WO2018105462A1 PCT/JP2017/042854 JP2017042854W WO2018105462A1 WO 2018105462 A1 WO2018105462 A1 WO 2018105462A1 JP 2017042854 W JP2017042854 W JP 2017042854W WO 2018105462 A1 WO2018105462 A1 WO 2018105462A1
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Prior art keywords
signal
waveform
fitted
processing method
peak
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PCT/JP2017/042854
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English (en)
Japanese (ja)
Inventor
康敏 梅原
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東京エレクトロン株式会社
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Priority to JP2018554949A priority Critical patent/JP6742435B2/ja
Publication of WO2018105462A1 publication Critical patent/WO2018105462A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry

Definitions

  • the present invention relates to a signal processing method and a program.
  • Patent Document 1 It has been proposed to detect fine particles in a body to be inspected such as a liquid by using the inspection light irradiated to the liquid or gas being shielded by the fine particles contained in the liquid or gas. See).
  • a target signal and noise in the signal to be inspected are classified according to the result of matching between the signal to be inspected and a waveform template specified in advance.
  • the frequency band of the target signal is different from the frequency band of noise. Further, the intensity (spectrum) of the target signal is larger than the noise intensity.
  • the signal-to-noise ratio (noise-to-signal ratio) is a signal with a low signal-to-noise ratio such as 2 or less, and the frequency band of the target signal is close to the frequency band of noise, It is difficult to distinguish from noise.
  • the signal to be inspected by the above-mentioned patent document is a signal having a relatively simple waveform that can be represented by a Gaussian waveform, a Lorentz shape or the like.
  • the signal processing method disclosed in the above-mentioned patent document further makes it difficult to separate the target signal and noise.
  • an object of one aspect of the present invention is to accurately determine a signal of a target particle from a detection signal regardless of the S / N ratio in detecting a particle in a liquid or a gas. To do.
  • a signal of light irradiated on a fluid inspection object is detected, a peak of the waveform of the detected signal is searched, and a waveform width of the searched peak And calculating the feature quantity of the fitted waveform, and based on whether the calculated feature quantity of the waveform is within a predetermined threshold range, the fitted waveform signal is a fine particle in the inspection object.
  • a signal processing method in which a computer executes a process for determining whether or not the signal indicates a signal.
  • route is detected is calculated using the Hermite-Gauss mode formula based on a Gaussian function.
  • the figure for demonstrating the fitting of the waveform which concerns on one Embodiment. The figure which shows an example of the threshold value of the rule of the feature-value which concerns on one Embodiment.
  • the figure which shows an example of the feature-value and determination result of the detection signal and waveform which concern on one Embodiment. The flowchart which shows an example of the signal processing according to S / N ratio which concerns on one Embodiment.
  • the cleaning apparatus 100 can be incorporated in a resist pattern forming apparatus or the like.
  • the cleaning apparatus 100 includes a substrate support portion 31 that supports the wafer W substantially horizontally, a rotation mechanism 32, and a cup 41.
  • the substrate support portion 31 is a disc that supports the center of the back surface of the wafer W from below.
  • the rotation mechanism 32 rotates the wafer W together with the substrate support unit 31.
  • the rotating mechanism 32 can be moved up and down.
  • An opening 41a having a larger diameter than the wafer W is provided on the upper surface of the cup 41, and the wafer W is transferred to and from the transfer arm via the opening 41a.
  • the cleaning apparatus 100 includes a surface cleaning nozzle 6 for cleaning the surface of the wafer W.
  • the surface cleaning nozzle 6 includes a cleaning liquid nozzle 61 and a gas nozzle 62.
  • the cleaning liquid nozzle 61 supplies a cleaning liquid (for example, DIW: De-Ionized Water) for cleaning particles adhering to the surface of the wafer W toward the surface of the wafer W.
  • the gas nozzle 62 supplies, for example, a gas such as nitrogen (N 2 ) toward the surface of the wafer W in order to promote drying of the cleaning liquid on the wafer surface.
  • the cleaning liquid nozzle 61 and the gas nozzle 62 are supported by, for example, a common support portion 63, and are configured to be movable in the radial direction of the wafer W and vertically movable up and down by a driving mechanism.
  • the cleaning liquid nozzle 61 and the gas nozzle 62 of the surface cleaning nozzle 6 are connected to a cleaning liquid (DIW) source 65 and a nitrogen gas source 66 through supply paths 61a and 62a, respectively.
  • DIW cleaning liquid
  • the cleaning liquid source 65 and the nitrogen gas source 66 are controlled by a control device 200 that controls the operation of the entire coating and developing device.
  • the control device 200 is composed of a computer having a memory.
  • the memory stores a program for controlling the operation of the entire coating and developing apparatus.
  • the control device 200 controls the operation of the cleaning device 100 according to the procedure set in the program.
  • the memory is realized by storage means such as a hard disk, a compact disk, a magnetic optical disk, a memory card, and the like.
  • the cleaning apparatus 100 described above is provided with a measurement mechanism 300 for measuring particles contained in the cleaning liquid supplied from the cleaning liquid source 65.
  • the measurement mechanism 300 is provided in the supply path 61a that is separated from the cleaning liquid nozzle 61 upstream by about 10 cm to 20 cm.
  • the measurement mechanism 300 measures the state of particles in the cleaning liquid flowing through the supply path 61a.
  • the object to be inspected in the present embodiment may be a liquid such as a chemical liquid, an organic liquid, or water for cleaning the wafer, or may be a gas such as decompressed air. Details of the measurement mechanism 300 will be described below.
  • the cleaning liquid is irradiated with laser light in order to detect particles in the cleaning liquid flowing in the supply path 61a.
  • laser light is emitted from the laser light source 301 toward the measuring unit 305 provided in the supply path 61a upstream by about 10 cm to 20 cm near the discharge port of the cleaning liquid nozzle 61.
  • the laser light is scattered by particles contained in the cleaning liquid flowing inside the measuring unit 305.
  • the detector 302 receives laser light, converts it into an electrical signal (hereinafter referred to as “detection signal”) by photoelectric conversion, analyzes the detection signal, and detects the state of the particles in the cleaning liquid. Thereby, the state of nano-level particles can be detected.
  • a first detector 303 and a second detector 304 are used as the detector 302.
  • the first detector 303 and the second detector 304 are photodetectors that convert received laser light into electricity.
  • the state of particles in the cleaning liquid is detected using a difference signal (hereinafter also referred to as “detection signal”) of detection signals detected by the first detector 303 and the second detector 304.
  • detection signal a difference signal
  • the noise components included in the detection signals detected by the first detector 303 and the second detector 304 are canceled out, and the analysis can be performed based on the signal having a small noise component.
  • the detector may be either the first detector 303 or the second detector 304.
  • the analysis is performed based on the detection signal detected by the detector, not the difference signal.
  • noise generated by the laser light source 301 and the detector 302 is included in the signal for detecting the particles. Therefore, noise is also included in the differential signal of the detection signals detected by the first detector 303 and the second detector 304, respectively.
  • the noise component in the difference signal or detection signal is in the same or relatively close frequency band as the signal, it is not easy to distinguish the signal (hereinafter referred to as “particle signal”) and noise.
  • particle signal the signal
  • the signal waveform is extracted by waveform modeling and is distinguished from the noise waveform to increase the nominal S / N ratio, and about 99% even for a signal with a S / N level lower than 2.
  • a signal processing method capable of detecting a particle signal with a probability of.
  • a waveform a is an example of a signal output from the laser light source 301 when the power of the laser light source 301 is set to zero.
  • a waveform b is an example of a signal output from the laser light source 301 when the power of the laser light source 301 is set to 12 (mW / ch).
  • a waveform c is an example of a signal output from the laser light source 301 when the power of the laser light source 301 is 9 (mW / ch).
  • the noise of the laser light source 301 surrounded by a broken line frame exists in a frequency band close to the particle signal on the high frequency side of 3 ⁇ 10 5 (Hz) and 1 ⁇ 10 6 (Hz). Indicates that there is a case.
  • the light output from the laser light source 301 shown in FIG. 3 is applied to the liquid (an example of the object to be inspected) flowing through the supply path 61a and scattered by particles (fine particles) in the object to be inspected. Therefore, the detection signals detected by the first detector 303 and the second detector 304 are scattered light signals generated by particles in the object to be inspected.
  • the detection signal includes noise mainly generated by the laser light source 301, the first detector 303, and the second detector 304.
  • a signal having a low S / N ratio that is, a large noise component with respect to the signal
  • a target signal in this embodiment, a particle signal
  • a detection signal having a complicated waveform compared to a signal having a relatively simple waveform that can be represented by a Gaussian waveform or the like, it becomes difficult to further separate the particle signal and noise.
  • the horizontal axis represents time
  • the vertical axis represents signal intensity.
  • modeling with a simple Gaussian waveform is performed.
  • the waveform included in the detection signal is arbitrary, and includes, for example, an asymmetric waveform that does not necessarily generate wave peaks having different positive and negative. Therefore, in this embodiment, it is possible to define a waveform model according to the characteristics of the waveform.
  • the waveform model according to this embodiment is defined by the difference between intersecting Gaussian waveforms.
  • the waveform is modeled by the positive peak height A2 of the waveform, the negative peak height A3, and the time offset between those peaks.
  • the waveform is first modeled by aligning the peak position of the waveform and minimizing the width of the peak wave.
  • the width of the wave is indicated by “ ⁇ ” in Expression (1) indicating the Gaussian function. More specifically, when defining the waveform model of the detection signal, the width of the waveform having the positive peak height A2 and the width of the waveform having the negative peak height A3 are represented by ⁇ of the Gaussian function. By minimizing ⁇ , the width of these waveforms can be fitted.
  • I (x, y) indicates the intensity profile of the laser beam spot in the x direction and the y direction of the scattered light corresponding to the detection signal.
  • Equation (1) is not a simple Gaussian function, but is physically a “Hermite-Gauss mode laser beam pattern, and is expressed as a function combining Hermitian coefficients Hk. Combining Hermitian coefficients Hk The functions are described in, for example, https://en.wikipedia.org/wiki/Transverse_mode#Laser_modes.
  • the physical quantity represented by this function indicates a phenomenon in which a beam generated in a direction perpendicular to the traveling direction of the laser generated at the time of laser resonance is split.
  • a similar two-divided pattern is generated by transmitting the laser beam through the phase shift plate.
  • the two-divided beam spot generated at this time can be approximated by 10 patterns in the Hermite-Gauss mode.
  • this function is used.
  • the optical system is actually installed so that the beam pattern of the 00 mode in FIG. 5 crosses the divided first detector 303 and second detector 304 shown in FIG. The result is almost the same between 00 mode and 10 mode.
  • the same function as the signal waveform processing of this embodiment can be used.
  • the function represented by the above formula (1) can be used. Therefore, this embodiment can be applied to both modes.
  • the Gaussian function is a particle path, and the probability of detection when the part of the particle represented by the measurement unit 305 in FIG. 2 passes can be calculated.
  • the laser light pattern through which the particle passes is in the 10 mode of FIG. 5, instead of the probability of the particle path, the scattered light or shadow of the particle passing through the region is induced, and the change in the light Indicates the intensity of detection sensitivity when entering the photodetector.
  • this black portion is a portion where the light is canceled by interference due to the phase shift.
  • the passage of particles through this part contributes to improving the sensitivity of the entire system.
  • the system sets the positional relationship of the optical system, flow cell, and photodetector so that many particles pass through this region. As a result of the setting, the signal intensity of the particles passing through this portion increases the difference signal between the two photodetectors (the first detector 303 and the second detector 304), and thus the sensitivity is increased.
  • This black part indicates that the particles are transmitted. That is, the black portion corresponds to a region where light is blocked by particles.
  • FIG. 6 is a flowchart showing an example of signal processing by waveform modeling according to the present embodiment.
  • the signal processing by waveform modeling according to the present embodiment is performed mainly by the control device 200 by searching for a waveform ⁇ fitting a waveform ⁇ extracting a feature value ⁇ evaluating a feature value based on a rule ⁇ generating a residual signal after subtracting the waveform. ⁇ Executed in the order of signal judgment.
  • control device 200 acquires the signals detected by the first detector 303 and the second detector 304, and searches for the peak of the waveform of the detection signal using the difference as a detection signal (step S10). ).
  • the control device 200 filters the detection signal waveform (original waveform) in advance using an S-Golay Filter, A peak search may be performed on the waveform after filtering.
  • a peak search may be performed on the waveform after filtering.
  • a maximum peak (Max), a minimum peak (Min), and a peak in the vicinity thereof in 1024 data may be searched.
  • the waveform of the detection signal is not necessarily a vertically symmetric waveform, but includes an asymmetrical waveform.
  • the control device 200 sets the maximum peak height A ⁇ b> 2 and the minimum peak height A ⁇ b> 3, and uses the formula (1) indicating the Gaussian function to determine the maximum peak and minimum peak.
  • the waveform is fitted by optimizing (minimizing) the width (step S12).
  • the control device 200 first sets the maximum peak height A2 and the minimum peak height A3 from the search result.
  • the peaks of the waves that set A2 and A3 may be peaks other than the maximum and minimum peaks.
  • the control device 200 fits the width of the waveform using a Gaussian function.
  • the control device 200 aligns the peak positions and the heights A2 and A3, and fits the width of each peak. That is, as shown on the right side of the lower diagram of FIG.
  • the waveform of the maximum peak height A2 and the waveform of the minimum peak height A3 are normalized, and the sum of squares of the difference of ⁇ of the Gaussian function indicating those waveforms is obtained.
  • the minimum value is the maximum peak and the minimum peak width.
  • the control device 200 calculates the feature amount of the fitted waveform based on a preset rule (step S14).
  • FIG. 9 shows an example of the set rule and the threshold value of the feature amount indicated in the rule.
  • Each threshold in FIG. 9 is an example of a first threshold set for each feature amount.
  • the waveform having the maximum peak height A2 and the waveform having the minimum peak height A3 are normalized, and the minimum value Diff of the sum of squares of the difference between these normalized waveforms is normalized. Is defined as one of the feature quantities of the waveform.
  • the threshold value of the minimum value Diff of the sum of squares of the normalized waveform difference is set to 0 to 0.08.
  • the waveform R having the maximum peak height A2 and the waveform having the minimum peak height A3 is defined as one of the feature quantities of the waveform.
  • the waveform deviation R is the ratio (A2 / A3) of the maximum peak height A2 to the minimum peak height A3.
  • the threshold value of the waveform deviation R is set to 1.1 to 2.0.
  • the peak width Offset is defined as one of the feature quantities of the waveform.
  • the threshold value of the peak width Offset is set to 0-60.
  • the width of the waveform having the maximum peak height A2 and the width ⁇ of the waveform having the minimum peak height A3 are determined as one of the feature quantities of the waveform.
  • the width ⁇ of the waveform is obtained by the Gaussian function of Equation (1).
  • the threshold value of the waveform width ⁇ is set to 0 to 5.0.
  • the control device 200 calculates each feature amount of the fitted waveform based on each rule.
  • the control device 200 extracts the fitted waveform component and subtracts the waveform component from the detection signal (step S16). For example, as shown in FIG. 10A, the control device 200 extracts the component of the fitted waveform S1. Then, as illustrated in FIG. 10B, the control device 200 removes the component of the waveform S1 from the detection signal. The remaining signal shown in (b) of FIG. 10 obtained by removing the component of the waveform S1 from the detection signal becomes the next analysis target.
  • the control device 200 determines whether the maximum peak of the remaining signal is smaller than a predetermined threshold (second threshold) (step S18).
  • second threshold a predetermined threshold
  • the control device 200 returns to step S10, and when the maximum peak of the residual signal is smaller than the predetermined second threshold value in step S18. Until it is determined, the processes in steps S10 to S18 are repeated.
  • FIG. 10 (c) shows an example in which the signal components of the fitted waveforms S1 to S3 are sequentially subtracted from the detection signal after the feature amount is calculated.
  • step S ⁇ b> 18 when the control device 200 determines that the maximum peak of the residual signal is smaller than the second threshold value, each of the feature values of the fitted waveforms is included in the first threshold value. It is determined whether it is within a threshold range corresponding to each feature amount (step S20). Here, for example, when all of the four feature amounts shown in the rules 1 to 3 are within the respective threshold ranges, the control device 200 determines that all of the feature amounts are within the first threshold range. Then, it is determined that the signal of the waveform to be determined is a particle signal (step S22), and this process ends.
  • FIG. 11 shows an example of the determination result of the signal of the fitted waveform based on the rule of FIG. 11A is determined as noise, and FIG. 11B and FIG. 11C are determined as particle signals.
  • the fitted waveform signal when the waveform feature amount of the extracted signal is within the first threshold range, the fitted waveform signal is a particle signal. It is determined that there is. Further, when the extracted waveform feature amount is outside the first threshold range, the fitted waveform signal is determined to be noise.
  • a matching waveform is generated by comparing a predetermined template waveform and a detection signal, and a waveform having a high matching score, which is similar to the template, is generated. Judge as a signal.
  • the normalized cross-correlation method there is an original signal component in the waveform that is excluded from the signal and determined to be noise, and there is a possibility that an erroneous determination is made.
  • the signal processing according to the present embodiment instead of matching by a template, a waveform is modeled and extracted, and it is determined whether or not the feature amount of the extracted waveform is within the first threshold range.
  • the extracted waveform signal is a particle signal or noise.
  • a signal determined to be noise because the waveform is not similar to the template in the conventional normalized cross-correlation method can be determined to be a particle signal in the present embodiment.
  • FIG. 12 illustrates an example of a detection signal according to the present embodiment, a waveform feature amount extracted from the detection signal, and a determination result for the extracted waveform signal.
  • the upper part of FIG. 12 shows an example of the distribution of detection signals when the diameter of the laser light spot output from the laser light source 301 is 1.2 ⁇ m, the power of the laser light beam is 20 mW, and the number of detectors 302 is one.
  • the SN ratio of the detection signal shown in the upper left graph of FIG. 12 is 1.2
  • the SN ratio of the detection signal shown in the center graph is 1.5
  • the SN ratio of the detection signal shown in the right graph is 2. .0.
  • the lower part of FIG. 12 shows the distribution of the feature amount of the waveform extracted from each detection signal by fitting, the result of determining whether the extracted waveform is a signal or noise, and the probability of miscounting.
  • the waveform component having the feature value indicated by “ ⁇ ” in the lower part of FIG. 12 is determined as a particle signal, and the waveform component having the feature value indicated by “X” in the lower part of FIG. Is determined to be noise.
  • the signal-to-noise ratio which is difficult to recognize the signal waveform in the conventional signal processing, is 1.
  • the miscount was 0.2% even for signals of about .2.
  • the miscount was 0.05% for a signal with an SN ratio of 1.5
  • the miscount was 0% for a signal with an SN ratio of 2.0.
  • FIG. 13 is a flowchart illustrating an example of signal processing according to the SN ratio according to the present embodiment.
  • the control device 200 acquires a detection signal from the detector 302 (step S30).
  • the detection signal may be a signal detected by one detector or a difference between signals detected by two detectors.
  • control device 200 matches the detection signal with the waveform of the template using the normalized cross correlation method (XCOR), obtains a matching score (cross correlation value of the detection signal), and calculates the SN ratio from the matching score. (Step S32).
  • XCOR normalized cross correlation method
  • FIG. 14 shows an example of waveform extraction by matching with a waveform template.
  • the control device 200 matches the feature waveform template with the feature waveform portion in the detection signal.
  • the control device 200 determines the waveform as a detection signal (particle signal).
  • step S34 determines whether or not the SN ratio is 1.5 or more.
  • the control device 200 determines whether or not the detection signal is a particle signal based on a matching score by a normalized cross correlation method (XCOR) shown in FIG. Step S36). Thereafter, the control device 200 returns to step S30 to acquire the next detection signal, and repeats the processing after step S30.
  • XCOR normalized cross correlation method
  • step S34 when it is determined in step S34 that the SN ratio is less than 1.5, the control device 200 determines whether the SN ratio is 1.2 or more (step S38). When it is determined that the SN ratio is 1.2 or more, the control device 200 executes signal processing by waveform modeling according to the present embodiment shown in FIG. 6 (step S40), and returns to step S30. On the other hand, when determining in step S38 that the SN ratio is less than 1.2, the control device 200 immediately returns to step S30.
  • the signal processing using the normalized cross-correlation method (XCOR) can obtain a predetermined accuracy or more. Therefore, when the SN ratio is 1.5 or more, for example, a particle signal is detected by signal processing using the normalized cross correlation method (XCOR) shown in FIG.
  • the S / N ratio is less than 1.5-2, it is difficult to distinguish between the particle signal and noise in the detection signal.
  • the SNR is 1.5 to 2 or more, it is easy to distinguish between the particle signal in the detection signal and the noise. Therefore, by changing the signal processing method of the detection signal according to the SN ratio, it is possible to accurately determine whether the signal is a particle signal or noise regardless of the level of the SN ratio. Further, the processing addition can be reduced by changing the signal processing method of the detection signal in accordance with the SN ratio.
  • step S38 in FIG. 13 the lower limit value of the SN ratio is set to 1.2.
  • the lower limit value of the SN ratio may be a numerical value other than 1.2 (for example, 1.0), and the lower limit value of the SN ratio may not be provided.
  • step S34 in FIG. 13 the value of the S / N ratio that changes the signal processing method of the detection signal is set to 1.5.
  • the present invention is not limited to this.
  • step S34 any value in the range of 1.5 to 2, such as an SN ratio of 2 or more, may be used as the SN ratio value that changes the signal processing method of the detection signal.
  • the control device 200 is an information processing device such as a personal computer or a tablet-type terminal.
  • the control device 200 includes an input device 101, a display device 102, an external I / F 103, a RAM (Random Access Memory) 104, a ROM (Read Only Memory) 105, a CPU (Central Processing Unit) 106, a communication I / F 107, and an HDD ( Hard Disk Drive) 108 and the like are connected to each other via a bus B.
  • the input device 101 includes a keyboard and a mouse, and is used for inputting each operation signal to the control device 200.
  • the display device 102 includes a display and displays various processing results.
  • the communication I / F 107 is an interface that connects the control device 200 to a network. Thereby, the control apparatus 200 can perform data communication with other apparatuses (detector 302 etc.) via communication I / F107. Thereby, the control apparatus 200 acquires the detection signal of a laser beam from the detector 302.
  • the HDD 108 is a non-volatile storage device that stores programs and data.
  • the stored programs and data include basic software and application software that control the entire control device 200.
  • the HDD 108 may store various databases and programs.
  • External I / F 103 is an interface with an external device.
  • the external device includes a recording medium 103a.
  • the control device 200 can read and / or write the recording medium 103a via the external I / F 103.
  • the recording medium 103a includes a CD (Compact Disk), a DVD (Digital Versatile Disk), an SD memory card (SD Memory Card), a USB memory (Universal Serial Bus memory), and the like.
  • the ROM 105 is a nonvolatile semiconductor memory (storage device) that can retain internal data even when the power is turned off.
  • the ROM 105 stores programs and data such as network settings.
  • the RAM 104 is a volatile semiconductor memory (storage device) that temporarily stores programs and data.
  • the CPU 106 is an arithmetic unit that realizes control of the entire apparatus and mounting functions by reading programs and data from the storage device (for example, “HDD 108”, “ROM 105”, etc.) onto the RAM 104 and executing processing.
  • the storage device stores a signal processing program by waveform modeling according to the present embodiment, and the CPU 106 reads the program from the storage device and executes the processing according to the procedure indicated by the program.
  • the particle signal and noise included in the detection signal can be determined.
  • the target particle signal is accurately determined from the detection signal regardless of the SN ratio. be able to.
  • the signal processing target is, for example, 2 This is a differential signal of the above photodetector (for example, the first and second detectors in FIG. 2), and the signal waveform is complicated.
  • a signal having a complicated waveform is a signal with an increased number of features (parameters) included in the signal.
  • the difference signal of two or more photodetectors is a signal that is easy to extract the feature amount extracted by the signal processing according to the present embodiment, and as a result, the particle signal or noise based on the feature amount according to the present embodiment. It is thought that the determination accuracy is improved.
  • the laser light source 301 and the detector 302 include inexpensive devices although they have a relatively large noise component. In that case, there is a case where it is desired to use an inexpensive device even when the SN ratio is 1.5 to 2.0 or less. In addition, the S / N ratio tends to be lower in an organic liquid than water. As described above, even when the SN ratio is 1.5 to 2.0 or less, it is possible to accurately distinguish the particle signal and the noise from the detection signal by the signal processing based on the waveform modeling according to the present embodiment. it can.
  • waveform extraction and signal determination results by waveform modeling according to the present embodiment can be accumulated and machine learning can be performed.
  • the range of variation is defined in advance by a threshold, and if the feature amount is outside the range of the threshold, the signal is discarded without being subjected to machine learning.
  • the control device 200 extracts an optimum waveform in real time from the waveform of the particle signal accumulated in the storage unit based on the learned result, and the waveform according to the present embodiment. You may make it apply to the signal processing by modeling of.
  • the present invention when detecting particles such as particles in a liquid or gas inspected object flowing in tubes attached to various devices, it is possible to accurately determine whether or not the signal indicates the particles from the detection signal regardless of whether the SN ratio is high or low.
  • the present invention includes a capacitively coupled plasma (CCP) device, an inductively coupled plasma (ICP) processing device, a plasma processing device using a radial line slot antenna, a helicon wave excited plasma ( The present invention can also be applied to devices such as HWP: Helicon Wave) Plasma devices, electron cyclotron resonance plasma (ECR) devices, and surface wave plasma processing devices.
  • CCP capacitively coupled plasma
  • ICP inductively coupled plasma
  • ECR electron cyclotron resonance plasma
  • the signal processing method according to the present embodiment can be applied to various measuring apparatuses that detect fine particles such as particles in a fluid (liquid or gas) inspection object.
  • the measurement mechanism 300 in the cleaning apparatus 100 has been described with reference to FIGS. 1 and 2 as an example of the configuration.
  • a particle monitor 133 index measuring device using laser scattered light as shown in FIG.
  • the particle monitor 133 includes a laser oscillator 134 that oscillates laser light, a CCD camera 135 that observes scattered light in the chamber 112, and a pulse generator 136 connected to the laser oscillator 134 and the CCD camera 135. .
  • the laser oscillator 134 oscillates a laser beam toward the inside of the chamber 112 during processing such as etching through a slit window 137 provided in the chamber 112.
  • the particles p in the chamber 112 generate scattered light when irradiated with laser light.
  • the generated scattered light is observed by the CCD camera 135 through the slit window 138.
  • the particle monitor 133 measures the amount of particles p floating in the chamber 112 through the number and intensity of scattered light generation (index measurement step). Then, the measured amount of particles p is transmitted as a measurement result to the PC 132 via the Internet 131 or the like.
  • the pulse generator 136 transmits a synchronization signal to the laser oscillator 134 and the CCD camera 135, and thereby adjusts the timing of oscillation of the laser light and the timing of reception of the scattered light.
  • the particle monitor 133 includes a transmission device 139 that receives a device status signal indicating the operating status of the chamber 112 and the presence or absence of a failure from the chamber 112 and transmits the device status signal to the PC 132 via the Internet 131 or the like.
  • the PC 132 performs signal processing according to the present embodiment.
  • the measurement apparatus to which the signal processing method according to the present embodiment can be applied is not limited to the case where the laser is irradiated into the chamber 112 as described above to detect particles floating in the space on the substrate.
  • the signal processing method according to the present embodiment detects particles adhering to the substrate by irradiating the substrate placed in the chamber 112 with a laser beam from directly above and detecting reflected light from the substrate. It is also possible to apply to an apparatus.

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  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé de traitement de signaux dans lequel un ordinateur exécute les processus consistant : à détecter un signal de lumière projeté sur un objet fluide qui est inspecté ; à rechercher une crête de forme d'onde dans le signal détecté ; à adapter la largeur de la forme d'onde de la crête recherchée ; à calculer une valeur caractéristique de la forme d'onde adaptée ; et à déterminer, sur la base de la présence ou non de la valeur caractéristique de la forme d'onde calculée dans une plage de seuil préétablie, si le signal avec la forme d'onde adaptée est ou non un signal représentant une particule fine dans l'objet qui est inspecté.
PCT/JP2017/042854 2016-12-08 2017-11-29 Procédé et programme de traitement de signaux WO2018105462A1 (fr)

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KR20210000656A (ko) * 2019-06-25 2021-01-05 오므론 가부시키가이샤 외관 검사 관리 시스템, 외관 검사 관리 장치, 외관 검사 관리 방법 및 프로그램
WO2022024389A1 (fr) * 2020-07-31 2022-02-03 株式会社日立ハイテク Procédé de génération d'un modèle formé, procédé de détermination d'une séquence de base d'une biomolécule et dispositif de mesure de biomolécules

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WO2022024389A1 (fr) * 2020-07-31 2022-02-03 株式会社日立ハイテク Procédé de génération d'un modèle formé, procédé de détermination d'une séquence de base d'une biomolécule et dispositif de mesure de biomolécules

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