WO2023153282A1 - Dispositif d'inspection, procédé d'inspection, dispositif de génération de modèle entraîné, programme d'inspection et programme de génération de modèle entraîné - Google Patents

Dispositif d'inspection, procédé d'inspection, dispositif de génération de modèle entraîné, programme d'inspection et programme de génération de modèle entraîné Download PDF

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WO2023153282A1
WO2023153282A1 PCT/JP2023/003157 JP2023003157W WO2023153282A1 WO 2023153282 A1 WO2023153282 A1 WO 2023153282A1 JP 2023003157 W JP2023003157 W JP 2023003157W WO 2023153282 A1 WO2023153282 A1 WO 2023153282A1
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Prior art keywords
value
measurement
measured
measured value
resistance
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PCT/JP2023/003157
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English (en)
Japanese (ja)
Inventor
昌史 小林
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日置電機株式会社
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Priority to CN202380020782.2A priority Critical patent/CN118679393A/zh
Publication of WO2023153282A1 publication Critical patent/WO2023153282A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/08—Measuring resistance by measuring both voltage and current
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/26—Measuring inductance or capacitance; Measuring quality factor, e.g. by using the resonance method; Measuring loss factor; Measuring dielectric constants ; Measuring impedance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass

Definitions

  • the present invention relates to an inspection device, an inspection method, a learned model generation device, an inspection program, and a learned model generation program, and for example, to an inspection device for inspecting inductor elements.
  • Patent Document 1 discloses that the AC resistance and inductance of an inductor element to be inspected are measured, the Q value is calculated using the measured values, and the quality of the inductor element is determined based on the calculated Q value.
  • An inspection device is disclosed.
  • Patent Document 1 as a method of measuring the AC resistance of an inductor element, an estimated value of the contact resistance of a measuring probe used in the measurement by the two-terminal method is obtained from the measured value of the AC resistance measured by the two-terminal method. Calculating the AC resistance by subtraction is described. Further, in Patent Document 1, an estimated value of contact resistance is calculated by subtracting a measured value of DC resistance measured by a two-terminal method from a measured value of DC resistance measured by a four-terminal method, and an estimated value of contact resistance is multiplied by a coefficient of 0 or more and 1 or less to correct the measured value of AC resistance.
  • the inspection device disclosed in Patent Document 1 corrects the measured value of the AC resistance on the premise that the relationship between the series resistance and the AC resistance in the inductor element is linear. However, the relationship between the series resistance and the AC resistance of the actual inductor element is unknown. For example, if the relationship is non-linear, there is a possibility that the measured value of the AC resistance will not be properly corrected. . In addition, the inspection device disclosed in Patent Document 1 corrects the measured value of AC resistance using a value obtained by multiplying the estimated value of contact resistance by a coefficient in order to avoid overcorrection of AC resistance. If the coefficients are not appropriate, AC resistance correction may not be performed properly.
  • the present invention has been made in view of the above-described problems, and aims to improve the reliability of inspection of electronic components.
  • An inspection apparatus comprises a first measurement value of the DC resistance of a measurement object measured by a four-terminal method, and a second measurement value of the DC resistance of the measurement object measured by a two-terminal method.
  • a data acquisition unit that acquires a measured value and a third measured value of the AC resistance of the measurement object measured by the two-terminal method, and the third measured value based on the input first measured value and the second measured value a storage unit that stores a trained model for causing a computer to calculate a measured value; and the first measured value that is acquired by the data acquisition unit based on the trained model that is stored in the storage unit. and an estimating unit that calculates an estimated value of the third measured value corresponding to the second measured value.
  • the inspection device According to the inspection device according to the present invention, it is possible to improve the reliability of inspection of electronic components.
  • FIG. 1 is a diagram showing the configuration of a measurement system including a learned model generation device and an inspection device according to an embodiment
  • FIG. It is a figure which shows an example of a structure of the learned model production
  • 6 is a flow chart showing the flow of generation of a trained model by the trained model generating device according to the embodiment; It is a figure which shows an example of a structure of the data-processing control apparatus in the inspection apparatus which concerns on embodiment.
  • FIG. 5 is a flow chart showing the flow of inspection by the inspection apparatus 2 according to the embodiment.
  • An inspection apparatus (2) measures a first measurement value (Rdc4) of the DC resistance of an object to be measured (DUT) measured by the four-terminal method, and by the two-terminal method
  • the third measurement is performed based on the first measurement value and the second measurement value acquired by the data acquisition unit according to the estimated value of the third measurement value.
  • a correction unit (24) may be further provided for performing a correction process of correcting the measured value and outputting the corrected third measured value as the value of the AC resistance of the object to be measured.
  • the learned model includes a first model (g(Rdc4, Rdc2)) representing a resistance component caused by a measurement system using a two-terminal method, and the object to be measured. and a second model (h(Rdc4)) representing a resistance component caused by the first model based on the first measured value and the second measured value obtained by the data obtaining unit.
  • the resistance component (Rc) caused by the measurement system by the two-terminal method is calculated according to the above, and as the correction process, the third measured value is corrected based on the resistance component caused by the measurement system by the two-terminal method.
  • the first model uses the first measured value and the second measured value as explanatory variables, and the resistance component caused by the measurement system according to the two-terminal method.
  • the second model is a regression model in which the first measured value is an explanatory variable and the value of the resistance component caused by the object to be measured is a regression model in which the objective variable is the value of the first
  • the first model and the second model are adjusted by machine-learning measured data for learning (34_1 to 34_n) in which the third measured value is associated with the first measured value and the second measured value. May contain parameters.
  • the correction unit calculates the estimated value of the third measurement value calculated by the estimation unit and the third measurement value obtained by the data acquisition unit.
  • ) from the three measured values may be calculated, and the correction process may be performed when the error is smaller than the threshold value (Rth).
  • the inspection device (2) includes a first measurement value (Rdc4) of the DC resistance of the measurement object measured by the four-terminal method, and a data acquisition unit (21) for acquiring a second measured value (Rdc2) of the DC resistance of the object to be measured and a third measured value (Rs) of the AC resistance of the object to be measured measured by the two-terminal method; , a storage unit (22) for storing a first model (35) indicating a correspondence relationship between the first measured value and the second measured value and the third measured value; and the first model stored in the storage unit (22).
  • the first model uses the first measured value and the second measured value as explanatory variables, and the value of the resistance component resulting from the measurement system according to the two-terminal method. and a third model having the first measured value as an explanatory variable and the value of the resistance component caused by the object to be measured as an objective variable, wherein the correction unit includes the correction
  • the correction unit includes the correction
  • a resistance component caused by the measurement system by the two-terminal method is calculated according to the second model, and the two-terminal method
  • the third measured value may be corrected based on the resistance component caused by the measurement system, and the corrected third measured value may be output as the value of the AC resistance of the object to be measured.
  • the trained model generation device (3) provides the first measured value (Rdc4) of the DC resistance of the measurement object measured by the four-terminal method, and the Learning measurement data (34_1) in which the third measurement value (Rs) of the AC resistance of the measurement object measured by the two-terminal method is associated with the second measurement value (Rdc2) of the DC resistance of the measurement object that has been measured 34_n), and machine-learning the measured data for learning to obtain the third measured data based on the input data including the first measured value and the second measured value.
  • a trained model generation unit (32) for generating a trained model (35) for causing a computer to function to calculate the measured value.
  • the trained model uses the first measured value and the second measured value as explanatory variables, and the resistance caused by the measurement system by the two-terminal method.
  • a first regression model (g (Rdc4, Rdc2)) whose objective variable is the value of the component
  • a second regression model g (Rdc4, Rdc2)
  • h(Rdc4) a regression model (h(Rdc4)) wherein the learned model generation unit adjusts the learned parameters of the first regression model and the second regression model by performing machine learning on the learning measurement data.
  • An inspection method includes a first measurement value (Rdc4) of the DC resistance of a measurement object measured by a four-terminal method, and the measurement object measured by a two-terminal method
  • a first step (S11 to S14) of acquiring a second measured value (Rdc2) of the DC resistance of and a third measured value (Rs) of the AC resistance of the measurement object measured by the two-terminal method, and input said first measured value obtained by said first step based on a trained model (35) for operating a computer to estimate said third measured value based on said first measured value and said second measured value;
  • a third step of correcting the third measured value based on the obtained first measured value and the second measured value, and outputting the corrected third measured value as an AC resistance value (Rsr) of the object to
  • An inspection method includes a first measurement value (Rdc4) of the DC resistance of the measurement object measured by the four-terminal method, and the measurement measured by the two-terminal method
  • a first step (S11 to S14) of acquiring a second measured value (Rdc2) of the DC resistance of the object and a third measured value (Rs) of the AC resistance of the object measured by the two-terminal method; the first measured value and the second measurement obtained in the first step, based on a first model (35) showing the correspondence relationship between the first measured value and the second measured value and the third measured value;
  • An inspection program is characterized by causing a computer to execute each step in the inspection method described in [10] or [11] above.
  • a trained model generation method includes a first measurement value (Rdc4) of the DC resistance of the measurement object measured by the four-terminal method, and Measurement data for learning (34_1 to 34_n) in which the third measurement value (Rs) of the AC resistance of the measurement object measured by the two-terminal method is associated with the second measurement value (Rdc2) of the DC resistance of the measurement object and performing machine learning on the learning measurement data acquired in the first step, based on the input data including the first measurement value and the second measurement value and a second step (S3) of generating a trained model (35) for activating a computer to calculate said third measure.
  • a trained model generation program is characterized by causing a computer to execute each step in the trained model generation method described in [13] above.
  • FIG. 1 is a diagram showing the configuration of an inspection system 1 including a trained model generation device 3 and an inspection device 2 according to an embodiment.
  • the inspection system 1 shown in FIG. 1 is a system for inspecting the quality of an object to be measured (hereinafter also referred to as "DUT").
  • an inspection system 1 includes a trained model generation device 3 that generates a trained model by learning measurement data for learning based on a plurality of measurement results of a DUT by machine learning, and a trained model that has been generated. and an inspection device 2 for inspecting the DUT using a.
  • the inspection device 2 is a device that measures the electrical characteristics of the DUT and inspects the quality of the DUT based on the measurement results.
  • the inspection device 2 is a device (a so-called chip taping machine) that inspects the quality of small electronic components (chip components) and packages the chip components determined to be non-defective in a state ready for shipment.
  • the DUT is an inductor element (eg, chip inductor element)
  • inductor element eg, chip inductor element
  • the inspection device 2 measures the electrical characteristics of the inductor element as the DUT using the learned model described later.
  • the inspection apparatus 2 includes a data processing control device 10, a first measurement section 11, a second measurement section 12, an operation section 13, an output section 14, and a transport mechanism 15, as shown in FIG. .
  • the first measurement unit 11 is a device that measures the electrical characteristics of an inductor element as a DUT by the four-terminal method.
  • an impedance measuring instrument such as a resistance meter or an LCR meter capable of measuring impedance by the four-terminal method can be exemplified.
  • the second measurement unit 12 is a device that measures the electrical characteristics of an inductor element as a DUT by the two-terminal method.
  • an impedance measuring instrument such as an LCR meter capable of measuring impedance by a two-terminal method can be exemplified.
  • first measurement unit 11 and the second measurement unit 12 are not limited to the above examples as long as they are devices capable of measuring electrical characteristics such as impedance of the DUT.
  • the first measurement unit 11 measures the DC resistance of the inductor element as the DUT by the four-terminal method in accordance with the instruction from the data processing control device 10 .
  • the first measurement unit 11 includes a moving mechanism (not shown) that moves the probes 61a to 61d, a current output unit and a voltage detection unit (not shown), and a measurement value calculation that calculates a measurement value based on the detection result. (not shown).
  • the movement mechanism of the first measurement unit 11 connects one terminal of the inductor element transported to the predetermined measurement position.
  • the probes 61a and 61c are brought into contact, and the other terminals of the inductor elements are brought into contact with the probes 61b and 61d.
  • the current output section of the first measuring section 11 supplies a DC current to the inductor element via the probes 61a and 61b.
  • the voltage detection section of the first measurement section 11 detects the voltage value between the inductor terminals through the probes 61c and 61d when the DC current is supplied to the inductor element.
  • the measured value calculator of the first measuring unit 11 calculates a measured value Rdc4 of the DC resistance of the inductor element based on the detected voltage value and the current value of the DC current supplied to the inductor element.
  • the second measurement unit 12 measures the DC resistance, AC resistance, and inductance of the inductor element as the DUT according to the instruction from the data processing control device 10 by the two-terminal method.
  • the second measurement unit 12 includes a moving mechanism (not shown) that moves the probes 62a and 62b, a current output unit and a voltage detection unit (not shown), and a measurement value calculator that calculates a measurement value based on the detection result. (not shown).
  • the movement mechanism of the second measurement unit 12 connects one terminal of the inductor element transported to the predetermined measurement position.
  • the probe 62a is brought into contact, and the other terminal of the inductor element is brought into contact with the probe 62b.
  • the current output section of the second measuring section 12 supplies a DC current to the inductor element via the probes 62a and 62b, and the voltage detecting section of the second measuring section 12 detects the voltage value between both terminals of the inductor element. are detected via probes 62a and 62b.
  • the measured value calculator of the second measuring unit 12 calculates a measured value Rdc2 of the DC resistance of the inductor element based on the detected voltage value and the current value of the DC current supplied to the inductor element.
  • the current output section of the second measuring section 12 outputs an alternating current to the inductor element through the probe.
  • the voltage detection section of the second measurement section 12 detects the AC voltage value between both terminals of the inductor element via the probes 62a and 62b.
  • the measured value calculator of the second measuring unit 12 calculates the detected AC voltage value (voltage effective value), the AC current value (current effective value) of the AC current supplied to the inductor element, and the position of the AC voltage and the AC current.
  • a measured value Rs of the AC resistance and a measured value L of the inductance of the inductor element are calculated based on the phase difference.
  • the data processing control device 10 may perform the above calculations by the measurement value calculation units of the first measurement unit 11 and the second measurement unit 12 .
  • the operation unit 13 is an input interface for the user to operate the inspection device 2 .
  • Various buttons, a touch panel, and the like can be exemplified as the operation unit 13 .
  • the user sets various inspection conditions and the like for inspecting an inductor element as a DUT in the inspection device 2, and instructs the inspection device 2 to perform and stop inspection and the like. can be done.
  • the output unit 14 is a functional unit for outputting various information such as inspection conditions and inspection results in the inspection device 2 .
  • the output unit 14 is, for example, a display device equipped with an LCD (Liquid Crystal Display) or an organic EL.
  • the output unit 14 displays information such as inspection results on the screen according to the control by the data processing control device 10 when the user operates the operation unit 13 to instruct execution of inspection of the DUT.
  • the output unit 14 may be a display device having a touch panel that realizes a part of the functions of the operation unit 13.
  • the output unit 14 may include a communication circuit or the like for outputting data such as test results to the outside by wire or wirelessly.
  • the transport mechanism 15 is a device that transports the inductor element to be inspected to an appropriate location within the inspection device 2 under the control of the data processing control device 10 .
  • the transport mechanism 15 transports the inductor element to be inspected to a predetermined measurement position by the first measurement unit 11 .
  • the transport mechanism 15 transports the inductor element to be inspected to a predetermined measurement position by the second measurement unit 12 .
  • the transport mechanism 15 transports inductor elements determined to be non-defective among the inductor elements that have been inspected to a position for packaging, and transports the packaged inductor elements to a predetermined location in the next step.
  • the data processing control device 10 is a functional unit that comprehensively controls each functional unit in the inspection device 2 and performs various data processing for inspection of the DUT.
  • the data processing control device 10 is a program processing device having a processor such as a CPU, storage devices such as ROM, RAM, and flash memory, and peripheral circuits such as timers.
  • Examples of program processing devices include MCUs and FPGAs.
  • the data processing control device 10 acquires the measurement results from the first measurement unit 11 and the second measurement unit 12, calculates an index indicating the performance of the inductor element based on the acquired measurement results, and based on the calculated index, Determining whether the inductor element to be inspected is good or bad.
  • the index indicating the performance of the inductor element is, for example, the Q value.
  • the data processing control device 10 of the inspection device 2 provides an index (Q value) indicating the performance of the inductor element to be inspected based on the measurement results of the first measurement unit 11 and the second measurement unit 12. is calculated, if necessary, the measured value Rs of the AC resistance measured by the second measuring unit 12 is corrected using a pre-generated learned model.
  • FIG. 2 is a diagram showing an example of the configuration of the learned model generation device 3 in the inspection system 1 according to the embodiment.
  • the trained model generation device 3 is realized by, for example, an information processing device (computer) such as a server or a personal computer (PC), and generates a plurality of learning measurement data 34_1 to 34_n ( n is an integer equal to or greater than 2), and machine-learning the generated measurement data for learning based on a predetermined algorithm to generate a trained model 35 .
  • a computer such as a server or a personal computer (PC)
  • PC personal computer
  • the trained model generation device 3 and the inspection device 2 are arranged side by side in FIG. No need.
  • the inspection device 2 and the trained model generation device 3 may be installed at different locations and connected via a communication network such as a LAN or the Internet.
  • the inspection device 2 and the learned model generation device 3 may transmit and receive various data such as measurement data by the inspection device 2 and the learned model 35 to and from each other via a communication network.
  • the inspection device 2 and the learned model generation device 3 do not have to be electrically connected to each other during inspection of the DUT.
  • measurement data by the inspection device 2 and various data such as the learned model 35 may be exchanged via a storage medium such as a memory card.
  • the learned model 35 is a model for estimating the measured value Rs of the AC resistance of the inductor element to be inspected.
  • the trained model 35 generated by the trained model generation device 3 may be distributed via a network, or may be stored in a computer-readable storage medium (non-transitory computer readable medium) such as a memory card. It may be written in and circulated.
  • the trained model generation device 3 has, for example, a learning measurement data acquisition unit 31, a trained model generation unit 32, and a storage unit 33 as functional blocks for generating a trained model 35.
  • Each of these functional blocks is composed of hardware resources such as a CPU and a memory that constitute an information processing device as the trained model generation device 3, and software installed in the information processing device (including a trained model generation program). It is realized by collaborating with various programs).
  • the learning measurement data acquisition unit 31 is a functional unit that acquires the learning measurement data 34_1 to 34_n necessary for generating the trained model 35.
  • the measured data for learning 34_1 to 34_n are the measured value Rdc4 (first measured value) of the DC resistance of the DUT measured by the four-terminal method and the measured value Rdc2 (first measured value) of the DC resistance of the DUT measured by the two-terminal method. 2 measured value) and the measured value Rs (third measured value) of the AC resistance of the DUT measured by the two-terminal method.
  • learning measurement data 34_1 to 34_n are not distinguished, they are simply referred to as "learning measurement data 34".
  • the learning measurement data acquisition unit 31 acquires the data 41 of the measured value Rdc4 of the DC resistance of the inductor element measured by the four-terminal method, for example, via wireless or wired communication (not shown) or a storage medium such as a memory card. , a data pair including data 42 of the measured value Rdc2 of the DC resistance of the inductor element measured by the two-terminal method and data 43 of the measured value Rs of the AC resistance measured by the two-terminal method.
  • the learning measurement data acquisition unit 31 acquires a data pair for each inspected inductor element. As data pairs, for example, measurement results of inductor elements inspected by the inspection apparatus 2 or the like in the past may be used.
  • the learning measurement data acquisition unit 31 associates the DC resistance measurement values Rdc4 and Rdc2 included in the acquired data pair with the AC resistance measurement value Rs included in the data pair as a correct value, thereby obtaining one Generate measurement data for learning 34 .
  • the learning measurement data acquisition unit 31 generates learning measurement data 34_1 to 34_n for each measurement result of the inspected inductor element, and stores the learning measurement data 34_1 to 34_n in the storage unit 33 .
  • the learning measurement data acquiring unit 31 receives the learning measurement data 34 generated by another information processing device or the like via communication or a storage medium. may be obtained by
  • the trained model generation unit 32 is a functional unit that generates a trained model 35 by performing machine learning on a plurality of learning measurement data 34_1 to 34_n acquired by the learning measurement data acquisition unit 31.
  • the trained model 35 is a program generated by machine learning based on a predetermined algorithm.
  • the predetermined algorithm include polynomial regression, multiple regression, and the like.
  • the trained model 35 includes a measured value Rdc4 (first measured value) of the DC resistance of the DUT measured by the four-terminal method and a measured value Rdc2 (second measured value) of the DC resistance of the DUT measured by the two-terminal method.
  • the learned model 35 performs calculations based on predetermined learned parameters on the input measurement data (measured values Rdc4 and Rdc2 of the DC resistance), and quantifies the AC resistance based on the measurement data. It is a program for causing an information processing device (computer) to function so as to output the calculated value (estimated value).
  • the learned model 35 includes a model representing the resistance component Rc caused by the measurement system by the two-terminal method and a model representing the resistance component caused by the object to be measured (DUT).
  • the resistance component caused by the measurement system by the two-terminal method includes, for example, the resistance component of the line composed of cables, probes, etc. existing between the second measuring section 12 and the DUT, and the contact between the probe and the DUT. and a resistance component (so-called contact resistance) caused by the state.
  • the model (first model) representing the resistance component Rc caused by the measurement system by the two-terminal method is, for example, the measured value Rdc4 (first measured value) of the DC resistance by the four-terminal method and the measured value of the DC resistance by the two-terminal method.
  • Rdc2 (second measured value) is an explanatory variable
  • the value of the resistance component caused by the measurement system by the two-terminal method is a regression model as an objective variable.
  • the model representing the resistance component (Rc) caused by the measurement system by the two-terminal method is a function for calculating the resistance component Rc caused by the measurement system by the two-terminal method from the measured values Rdc4 and Rdc2 of the DC resistance. .
  • a model (second model) representing the resistance component caused by the DUT has, for example, the DC resistance measured value Rdc4 (first measured value) by the four-terminal method as an explanatory variable, and the value of the resistance component caused by the DUT as the objective variable. It is a regression model with In other words, the model representing the resistance component caused by the DUT is a function that estimates the value of the AC resistance caused by the DUT from the measured value Rdc4 of the DC resistance by the four-terminal method.
  • the model representing the resistance component (Rc) due to the measurement system by the two-terminal method is g (Rdc4, Rdc2) and the model representing the resistance component due to the DUT is h (Rdc4)
  • the measured value of the AC resistance is estimated.
  • the estimated value Rse of the measured value of the AC resistance is the resistance component Rc due to the measurement system by the two-terminal method obtained by the model g (Rdc4, Rdc2) and the resistance due to the DUT obtained by the model h (Rdc4). It is represented by the sum of the components.
  • Model g (Rdc4, Rdc2) and model h (Rdc4) contain learned parameters.
  • the learned parameters are mechanically adjusted so as to calculate the estimated value Rse of the AC resistance measurement value using the learning measurement data 34_1 to 34_n as inputs to the learning program (the program based on the predetermined algorithm).
  • the learned model generation unit 32 adjusts the learned parameters of the model g (Rdc4, Rdc2) and the model h (Rdc4) by performing machine learning on the learning measurement data 34_1 to 34_n.
  • the trained model generation unit 32 first generates an estimated value Rse of the measured value of the AC resistance calculated by inputting the learning measurement data 34_1 to 34_n into the regression model, and the measured value of the AC resistance which is the correct value. A difference (error) from the value Rs is calculated.
  • the learned model generation unit 32 generates the regression model by sequentially updating the learned parameters (coefficients) of the regression model so that the calculated error becomes smaller, for example, by the error back propagation method, It is stored in the storage unit 33 as a trained model 35 .
  • the storage unit 33 is a functional unit for storing various data such as the learning measurement data 34_1 to 34_n necessary for generating the trained model 35 and the generated trained model 35.
  • the storage unit 33 is configured to be accessible from the outside, for example.
  • the inspection device 2 can read and acquire the learned model 35 from the storage unit 33 by communicating with the trained model generation device 3 . Further, for example, the inspection device 2 can write the data of the measurement results and the like to the storage unit 33 by communicating with the trained model generation device 3 .
  • FIG. 3 is a flow chart showing the flow of generation of the learned model 35 by the trained model generation device 3 according to the embodiment.
  • the learning measurement data acquisition unit 31 acquires learning measurement data 34_1 to 34_n (step S1). Specifically, as described above, the learning measurement data acquisition unit 31 acquires a data pair including the DC resistance measurement values Rdc4 and Rdc2 and the AC resistance measurement value Rs of inductor elements tested in the past, Learning measurement data 34 is generated by adding the AC resistance measurement value Rs as a correct value to the DC resistance measurement values Rdc4 and Rdc2 based on the acquired data pair.
  • the trained model generation device 3 determines whether or not the necessary number of learning measurement data 34 to generate the trained model 35 has been generated (step S2). For example, the trained model generation device 3 is preset with the number of learning measurement data 34 required to generate a trained model 35, and the trained model generation device 3 generates the learning measurement data 34 is generated, the number of generations is incremented by +1. Then, the trained model generation device 3 determines whether or not the number of times of generation that has been counted has reached the number of data set in advance, thereby determining whether or not the required number of measurement data for learning 34 has been generated. do.
  • step S2 If the required number of learning measurement data 34 has not been generated (step S2: NO), the trained model generating device 3 returns to step S1 to obtain data pairs related to new inductor element measurement results. Then, generation of the learning measurement data 34 regarding the inductor element is repeated (steps S1 and S2).
  • step S2 when the required number of learning measurement data 34 has been generated (step S2: YES), the trained model generation device 3 performs learning using the plurality of learning measurement data 34 generated in step S1.
  • a finished model 35 is generated (step S3).
  • the learned model generation unit 32 performs machine learning based on a predetermined algorithm on the plurality of learning measurement data 34_1 to 34_n created in step S1 by the method described above, thereby generating the learned model 35 to create
  • the learned model generation unit 32 stores the learned model 35 in the storage unit 33 when sufficient accuracy is obtained for the learned model 35 .
  • the trained model 35 generated in step S3 is registered in the inspection device 2 (step S4).
  • the learned model generation device 3 stores the The stored learned model 35 is transmitted to the inspection device 2 , and the learned model 35 received by the inspection device 2 is stored in the storage unit in the data processing control device 10 .
  • the registration of the learned model 35 in the inspection apparatus 2 may be performed using a storage medium such as a memory card, as described above.
  • the learned model generation program for causing the computer (information processing device) as the trained model generation device 3 to execute the above-described steps (S1 to S4) may be distributed via a network. However, it may be distributed by being written in a computer-readable storage medium (non-transitory computer readable medium) such as a memory card.
  • the learned model 35 for inspecting the inductor element is generated by the method described above.
  • FIG. 4 is a diagram showing an example of the configuration of the data processing control device 10 in the inspection device 2 according to the embodiment.
  • the data processing control device 10 of the inspection device 2 has, for example, a data acquisition unit 21, a storage unit 22, an estimation unit 23, a correction unit 24, and a determination unit 25.
  • These functional units are implemented by, for example, a program processing device as the data processing control device 10, in which the CPU executes various calculations according to programs stored in a memory and controls peripheral circuits such as counters. be.
  • the data acquisition unit 21 is a functional unit that acquires various data necessary for calculating an index (Q value) indicating the performance of the inductor element to be inspected.
  • the data acquisition unit 21 acquires, for example, the measured value Rdc4 of the DC resistance of the DUT measured by the first measurement unit 11 by the four-terminal method, and stores it in the storage unit 22 .
  • the data acquisition unit 21 obtains, for example, a measured value Rdc2 of the DC resistance of the DUT measured by the second measuring unit 12 by the two-terminal method and a measured value Rs of the AC resistance of the DUT measured by the second measuring unit 12 by the two-terminal method. , and the measured value L of the inductance of the DUT measured by the second measurement unit 12 by the two-terminal method, and stored in the storage unit 22 as measurement data 50 to be inspected. Further, the data acquisition unit 21 acquires, for example, the trained model 35 generated by the trained model generation device 3 and stores it in the storage unit 22 .
  • the storage unit 22 is a functional unit for storing various data necessary for calculating an index (Q value) indicating the performance of the inductor element to be inspected, the calculated Q value, and the like.
  • the storage unit 22 stores the measured values Rdc4 and Rdc2 of the DC resistance of the inductor element, the measured value Rs of the AC resistance of the inductor element, and the measured value L of the inductance of the inductor element, which are acquired by the data acquiring unit 21. , and the trained model 35 are stored respectively. Further, the storage unit 22 stores, for example, an estimated value Rse of a measured value of AC resistance, an estimated value of a resistance component Rc caused by a measurement system using a two-terminal method, an AC resistance value Rsr, and a Q value, which will be described later. remembered.
  • the estimation unit 23 is a functional unit that estimates the measured value of the AC resistance of the inductor element to be inspected. Based on the learned model 35 stored in the storage unit 22, the estimation unit 23 calculates the measured values of the AC resistance corresponding to the measured values Rdc4 and Rdc2 of the DC resistance of the inductor element to be inspected acquired by the data acquisition unit 21. Calculate the estimated value Rse. Specifically, the estimation unit 23 inputs (substitutes) the measured values Rdc4 and Rdc2 of the DC resistance of the inductor element to be inspected acquired by the data acquisition unit 21 into the learned model 35 (function). The value is stored in the storage unit 22 as an estimated value Rse of the measured AC resistance.
  • the correction unit 24 is a functional unit for correcting the measured value Rs of the AC resistance.
  • the correction unit 24 corrects the measured value Rs of the AC resistance based on the measured values Rdc4 and Rdc2 of the DC resistance acquired by the data acquisition unit 21 according to the estimated value Rse of the measured value of the AC resistance, and obtains the corrected AC resistance
  • a correction process is performed to output the measured value Rs of the DUT as the value Rsr of the AC resistance of the DUT.
  • the correction unit 24 calculates 2 A resistance component Rc due to a measurement system based on the terminal method is calculated. Data of the calculated resistance component Rc is stored in the storage unit 22, for example.
  • the correction unit 24 corrects the measured value Rs of the AC resistance of the inductor element to be inspected based on the resistance component Rc caused by the measurement system using the two-terminal method according to the estimated value Rse of the measured value of the AC resistance. Then, the corrected AC resistance measurement value Rs is output as the AC resistance value Rsr of the inductor element to be inspected.
  • the correction unit 24 determines the error
  • the correction unit 24 compares the error
  • the threshold Rth is an arbitrary preset value.
  • is smaller than the threshold value Rth, it can be considered that the AC resistance estimation accuracy of the learned model 35 is high with respect to the measurement result of the inductor element to be inspected. That is, it is considered that the model g (Rdc4, Rdc2) included in the trained model 35 has high estimation accuracy of the resistance component Rc due to the measurement system by the two-terminal method.
  • the correction unit 24 performs correction processing. Specifically, the correction unit 24 calculates the resistance component Rc caused by the measurement system by the two-terminal method using the model g (Rdc4, Rdc2), and calculates the calculated resistance component Rc caused by the measurement system by the two-terminal method. is used to correct the measured value Rs (third measured value) of the AC resistance.
  • the resistance component Rc caused by the measurement system by the two-terminal method is calculated using the model g (Rdc4, Rdc2), and the calculated resistance component Rc caused by the measurement system by the two-terminal method is used to If the resistance measurement value Rs (third measurement value) is corrected, the correction may be erroneous, and the AC resistance value Rsr may not be obtained appropriately.
  • the judgment unit 25 is a functional unit for judging whether the DUT (inductor element) is good or bad.
  • the determination unit 25 expresses the performance of the inductor element to be inspected based on the AC resistance value Rsr of the inductor element to be inspected and the measured inductance value L of the inductor element to be inspected, which are output from the correction unit 24.
  • a Q value (Q ⁇ L/Rsr), which is an index, is calculated.
  • the judging unit 25 judges the quality of the inductor element to be inspected by, for example, comparing the calculated Q value with a predetermined reference value.
  • the determining unit 25 controls the transport mechanism 15 to package the DUT determined as a non-defective product into a state ready for shipment by a packaging device (not shown).
  • FIG. 5 is a flow chart showing the flow of inspection by the inspection device 2 according to the embodiment.
  • the data processing control device 10 starts inspection of the inductor element to be inspected.
  • the data processing control device 10 controls the first measuring unit 11 to measure the DC resistance of the inductor element to be inspected by the four-terminal method (step S11). For example, the data processing control device 10 controls the transport mechanism 15 according to an instruction signal from the operation unit 13 to transport the inductor element to be inspected to a predetermined measurement position in the first measurement unit 11 . After that, the data processing control device 10 controls the first measuring unit 11 to measure the DC resistance of the inductor element to be inspected by the four-terminal method, and acquires the measured value Rdc4 of the DC resistance.
  • the data processing control device 10 controls the second measuring unit 12 to measure the DC resistance of the inductor element to be inspected by the two-terminal method (step S12).
  • the data processing control device 10 controls the transport mechanism 15 to transport the inductor element to be inspected to a predetermined measurement position in the second measuring section 12 .
  • the data processing control device 10 controls the second measuring section 12 to measure the DC resistance of the inductor element to be inspected by the two-terminal method, and acquires the measured value Rdc2 of the DC resistance.
  • the data processing control device 10 controls the second measuring unit 12 to measure the measured value Rs of the AC resistance and the measured value L of the inductance of the inductor element to be inspected by the two-terminal method (step S13). .
  • the data processing control device 10 controls the second measuring unit 12 to measure the AC resistance of the inductor element to be inspected, A measurement value Rs of AC resistance and a measurement value L of inductance are obtained respectively.
  • the data processing control device 10 controls the second measuring section 12 to measure the DC resistance of the inductor element to be inspected by the two-terminal method in the same manner as in step S12 (step S14).
  • the measured values Rdc4 and Rdc2 of the DC resistance, the measured value Rs of the AC resistance, and the measured value L of the inductance acquired by the data processing control device 10 in steps S11 to S14 are stored as the measured data 50 of the inductor element to be inspected. 22.
  • the data processing control device 10 calculates an estimated value Rse of the measured AC resistance of the inductor element to be inspected based on the measurement data 50 of the inductor element to be inspected (step S15). Specifically, the estimating unit 23 inputs the measured values Rdc4 and Rdc2 of the direct current resistance acquired in steps S11 and S12 (S14) to the learned model 35 by the method described above, so that from the learned model 35 An estimated value Rse of the measured value of the output AC resistance is obtained.
  • the estimating unit 23 compares, for example, the measured value Rdc2 of the DC resistance measured in step S12 with the measured value Rdc2 of the DC resistance measured in step S14, and uses the smaller measured value Rdc2 of the DC resistance.
  • An estimated value Rse of the measured value of AC resistance may be calculated.
  • the correction unit 24 determines whether or not the difference
  • the correction unit 24 uses the model g (Rdc4, Rdc2) to calculate the resistance caused by the measurement system by the two-terminal method.
  • a component Rc is calculated (step S17). Specifically, the correction unit 24 inputs the measured values Rdc4 and Rdc2 of the direct current resistance obtained in steps S11 and S12 (S14) to the model g (Rdc4, Rdc2), so that the measurement system using the two-terminal method A resulting resistance component Rc is obtained.
  • the determination unit 25 determines the Q of the inductor element to be inspected based on the AC resistance value Rsr output from the correction unit 24 in step S18 or step S19 and the measured inductance value L acquired in step S13. A value is calculated (step S20). After that, the judging section 25 judges whether the inductor element to be inspected is good or bad based on the Q value calculated in step S20. Inductor elements determined to be non-defective are transported by transport mechanism 15 and packaged.
  • the inspection program for causing the computer (information processing device) as the data processing control device 10 to execute the steps (S11 to S21) described above may be distributed via a network, or may be stored in a memory card. It may be distributed by being written on a computer-readable storage medium (non-transitory computer readable medium) such as.
  • the learned model generation device 3 includes the model g (Rdc4, Rdc2) for calculating the resistance component caused by the measurement system by the two-terminal method, and the DUT (inductor element).
  • the model g (Rdc4, Rdc2) is a regression model with the measured values Rdc4 and Rdc2 of the DC resistance as explanatory variables and the value of the resistance component resulting from the measurement system by the two-terminal method as the objective variable.
  • the model h(Rdc4) is a regression model with the measured value Rdc4 of the DC resistance as an explanatory variable and the value of the resistance component caused by the DUT as an objective variable.
  • the model g (Rdc4, Rdc2) and the model h (Rdc4) are adjusted by machine learning a plurality of learning measurement data 34_1 to 34_n in which the measured DC resistance values Rdc4 and Rdc2 are associated with the measured AC resistance values Rs. contains the learned parameters (coefficients).
  • the trained model 35 can be represented by a simpler function, so it is possible to avoid black boxing of the trained model 35, which is a concern in machine learning.
  • the inspection device 2 estimates the measured value of the AC resistance of the DUT using the learned model 35 generated by the trained model generation device 3, and the estimation result , the model g (Rdc4, Rdc2) included in the learned model 35 is used to calculate the resistance component Rc due to the measurement system by the two-terminal method. Then, the inspection apparatus 2 corrects the measured value Rs of the AC resistance based on the calculated resistance component Rc caused by the measurement system by the two-terminal method, and outputs the corrected value as the value of the AC resistance of the DUT.
  • the inspection device 2 calculates the error
  • is smaller than the threshold value Rth, output the AC resistance measured value Rs corrected based on the resistance component Rc caused by the measurement system by the two-terminal method as the AC resistance value Rsr ( Rse ⁇ Rc). do.
  • the error between the measured value of AC resistance and the estimated value based on the learned model 35 is Correction of the measured values of AC resistance is performed only for small inductor elements, that is, inductor elements for which it is considered appropriate to apply the learned model 35 (model g(Rdc4, Rdc2)). As a result, overcorrection can be prevented, and a more accurate AC resistance value can be obtained.
  • the learned model generation device 3 and the inspection device 2 according to the present embodiment it is possible to improve the reliability of inspection of electronic components.
  • the model (learned model 35) used for inspection by the inspection apparatus 2 may be a function indicating the correspondence between the measured values Rdc4 and Rdc2 of the direct current resistance and the measured value Rs of the alternating current resistance. It may be a model generated by
  • the inspection device 2 inspects the inductor element by the same method as above using models including models g (Rdc4, Rdc2) and h (Rdc4) whose coefficients are adjusted by a method other than machine learning. good too.
  • the inspection apparatus 2 integrates the components such as the data processing control device 10, the first measurement unit 11, the second measurement unit 12, the operation unit 13, the output unit 14, and the transport mechanism 15.
  • the data processing control device 10, the operation unit 13, and the output unit 14 are realized by a first device (for example, an information processing device such as a PC), and the first measurement unit 11, the second measurement unit 12, and the transport mechanism 15 may be implemented by a separate second device different from the first device.
  • the first device and the second device may be connected via a wired or wireless network.
  • Rdc4 measured value of DC resistance by 4-terminal method
  • Rs measured value of AC resistance by 2-terminal method
  • Rse estimated value of measured value of AC resistance by 2-terminal method
  • Rsr value of AC resistance
  • Rth threshold.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Resistance Or Impedance (AREA)

Abstract

La présente invention améliore la fiabilité d'inspection de pièces électroniques.　Un dispositif d'inspection (2) calcule une valeur d'estimation (Rse) d'une valeur mesurée de résistance en courant alternatif, à l'aide d'un modèle entraîné (35) qui amène un ordinateur à fonctionner de façon à calculer une valeur mesurée (Rs) de résistance en courant alternatif d'un objet de mesure mesuré par un procédé à deux bornes, sur la base d'une valeur mesurée (Rdc4) de résistance en courant continu de l'objet de mesure mesurée par un procédé à quatre bornes et d'une valeur mesurée (Rdc2) de résistance en courant continu de l'objet de mesure mesurée par le procédé à deux bornes.
PCT/JP2023/003157 2022-02-08 2023-02-01 Dispositif d'inspection, procédé d'inspection, dispositif de génération de modèle entraîné, programme d'inspection et programme de génération de modèle entraîné WO2023153282A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016147722A1 (fr) * 2015-03-19 2016-09-22 日本電気株式会社 Dispositif d'estimation, procédé et programme d'estimation
JP2017096733A (ja) * 2015-11-24 2017-06-01 日置電機株式会社 測定装置および測定方法
JP2019086460A (ja) * 2017-11-09 2019-06-06 日置電機株式会社 処理装置、検査装置および処理方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016147722A1 (fr) * 2015-03-19 2016-09-22 日本電気株式会社 Dispositif d'estimation, procédé et programme d'estimation
JP2017096733A (ja) * 2015-11-24 2017-06-01 日置電機株式会社 測定装置および測定方法
JP2019086460A (ja) * 2017-11-09 2019-06-06 日置電機株式会社 処理装置、検査装置および処理方法

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