TWI789811B - Measurement system and measurement method - Google Patents

Abstract

This present disclosure relates to a measurement system and a method for testing an object under test. According to the embodiment of the present disclosure, the measurement system includes a test unit. The test unit includes a test probe, an electrical detection module, an image capture module, an information collection module, an AI determination module, and a processing module. The test probe is configured to contact an object under test, and test the object under test. The electrical detection module is configured to obtain electrical data of the object under test. The image capture module is configured to capture image data of the object under test. The information collection module is connected to the electrical detection module and the image capture module, and is configured to collect the electrical data and the image data of the object under test. The AI determination module is connected to the information collection module, and is configured to receive and determine the electrical data and the image data. The processing module is connected to the AI determination module, which is configured to respond to the commands from the AI determination module.

Description

Measurement system and measurement method

The present disclosure is related to a measurement system and a method for testing an object under test, in particular to a measurement system with an artificial intelligence judgment module and an operation method thereof.

The test surface of the object to be tested is dirty or the test probe is dirty or deformed, which may affect the electrical test results. The test probe is dirty or deformed as an example. When the problem occurs, it must be cleaned or replaced immediately with a cleaning pad, because the test signal is very sensitive to the needle condition of the needle. If the needle tip is dirty, the needle is worn or the spring is deformed Problems such as this will affect the stability of the test signal, which may lead to a high retest rate and affect the efficiency of the equipment; If it is high, it will affect the measurable delivery time of the test machine, otherwise, it will lead to poor stability and high retest rate.

According to one or more embodiments of the present disclosure, a measurement system includes a test unit, an information collection module, an artificial intelligence judgment module, and a processing module. The test unit is configured to contact an object under test to obtain information. The information collection module is connected to the test unit and configured to collect the information of the DUT. The artificial intelligence judgment module is connected to the information collection module and is configured to receive and judge the information. The processing module connects the An artificial intelligence judgment module configured to issue commands in response to the artificial intelligence judgment module.

According to one or more embodiments of the present disclosure, a method for testing an object under test includes providing an object under test; using a test unit to obtain information about the object under test; using an artificial intelligence judgment module to evaluate the information judging; and using a processing module to issue an instruction in response to the judgment of the artificial intelligence judgment module.

100: Measurement system

110:Electrical detection module

120:Picture capture module

130: Test probe

140:Information collection module

150: Artificial Intelligence Judgment Module

160: Processing module

170:Information storage module

190: Test unit

200: Production history module

300: The object to be tested

310: Test circuit board

320: probe cleaning device

400:Robot Arm

500: Test fixture

510: step

520: step

530: step

540: step

6: Test process

610: Step

620: Step

630: step

640: step

650: step

660: step

FIG. 1 is a schematic diagram of a measurement system according to an embodiment of the present disclosure.

2a to 2c are schematic diagrams of the operation of the test unit according to the embodiment of the present disclosure.

3 a to 3 c are probe traces on the surface of the object to be tested according to an embodiment of the present disclosure.

4a to 4h are image data of the surface of the object under test determined to be abnormal according to an embodiment of the present disclosure.

FIG. 5 is a test flow chart of testing an object under test using a measurement system according to an embodiment of the present disclosure.

Reference numerals are indicated in the drawings and are used in the embodiments to denote the same or similar components. The detailed description of this disclosure will be included in the implementation mode and the corresponding drawings.

In response to the development of 5G millimeter-wave antennas and products, the stability requirements for electrical testing are higher than other low-frequency products. For example, if the test surface of the DUT is dirty or the test needle is dirty or deformed, it may affect the electrical testing. As a result, the test needle is dirty or deformed as an example. When the problem occurs, it must be cleaned or replaced immediately with a clean pad, because the test signal is very sensitive to the condition of the needle. If the needle tip is dirty, Problems such as needle wear or spring deformation will affect the stability of the test signal, which may lead to an excessively high retest rate and affect the device. equipment efficiency. In addition, the contact electrical measurement will leave probe marks on the object to be tested, and the area of the probe marks will affect the test quality.

As mentioned above, the result of the electrical test may be affected due to the dirt on the test surface of the object to be tested or the dirt or deformation of the test probes. This disclosure records a measurement system that can improve test efficiency. The measurement system can combine the two steps of functional testing and appearance testing, which were originally independent, into one. For example, an artificial intelligence (AI) processor can be used to test electrical Summarize and analyze the test results of sexual data and appearance inspection information, so as to further carry out the follow-up related processes. For example: if abnormal electrical measurement data or image data (such as: probe marks) are found to be abnormal, start cleaning or replacing the probe, so as to effectively find out the appropriate timing for cleaning the needle and further improve the quality. The use efficiency of the measurement system and maintain good appearance inspection quality, and reduce the demand for operators operating the measurement system, this design can improve the automation of the measurement system and further improve the measurement quality.

FIG. 1 is a schematic diagram of a measurement system 100 according to an embodiment of the present disclosure. The measurement system 100 includes a test unit 190 , a production history module 200 , an information collection module 140 , an information storage module 170 , an artificial intelligence judgment module 150 , and a processing module 160 .

In some embodiments, the testing unit 190 further includes an electrical testing module 110 , a picture capturing module 120 , and a testing probe 130 .

The test probe 130 of the test unit 190 can serve as a medium between an object under test 300 (as shown in FIG. 2 a ) and the measurement system 100. In some embodiments, the test probe 130 is configured to contact the object under test 300. Further perform measurement and test on the object under test 300, for example, perform measurement and test of electrical data.

In some embodiments, the testing unit 190 further has a robotic arm 400, which configured to pick up and/or carry an object, and move the object to and from the test probe 130, and the object may include the test object 300 and probe cleaning Device 320 (probe cleaning device 320 may include cleaning pads, brushes, air spray guns, etc. for cleaning the probes).

The electrical detection module 110 of the test unit 190 can be used to measure and/or test the electrical function of the object under test 300. In some embodiments, the electrical detection module 110 is configured to use the test probe 130 to Obtain the electrical data of the object under test 300 . In some embodiments, the electrical testing module 110 includes a network analyzer or an automatic test equipment (ATE).

The image capture module 120 of the test unit 190 is configured to capture image data of the object under test 300 . In some embodiments, the image capture module 120 can be an appearance inspection device, which performs an automatic appearance inspection of the object under test 300 before the object under test 300 is measured. After the appearance inspection, the object to be tested can be classified into good and defective products; if the object to be tested is classified as a good product after the appearance inspection, it will be further tested in the next step, such as electrical measurement; DUTs that are classified as defective after testing will be transported to the defective area without further testing. In some embodiments, the appearance detection can be judged by the artificial intelligence judgment module 150 . In some embodiments, the image capture module 120 is configured to capture a surface image of the object under test 300 after the object under test 300 is in contact with the test probe 130 and performs an electrical measurement, so as to obtain The image data of the object under test, and the image data will be further compared and judged, which will be described in detail in the following content. In some embodiments, the image capture module 120 includes a charge-coupled device (Charge-coupled Device "CCD") or an automatic optical inspection (Automated Optical Inspection "AOI") device. The picture capture module 120 includes an automatic camera module, which is configured to automatically take the image data on the surface of the object under test 300 after the electrical data measurement of the object under test 300 (for example: detecting needle marks) shooting and Store in this way to obtain the probe marks or other traces left on the surface of the test object 300 after the test probe 130 is in contact with the test object 300 .

As shown in FIG. 1 , in some embodiments, the information collection module 140 is connected to the electrical detection module 110 and the picture capture module 120 of the test unit 190, which are configured to receive and collect the electrical detection module The electrical data of the object under test 300 measured at 110 and/or the image data of the object under test 300 captured by the image capture module 120 (for example: probe marks or the object under test 300 other images or traces on the surface). In some embodiments, the information collection module 140 can be used to collect equipment parameters (such as test time, measurement time, various equipment-related parameters and environmental parameters, etc.).

Referring to FIG. 1 , in some embodiments, the production history module 200 is connected to the information collection module 140 . The production history module 200 can be used to provide product information associated with the object under test 300; in some embodiments, the associated product information can be input by the user into the production history module 200; in some embodiments , the associated product information can be provided to the production history module 200 by other equipment or devices in the production line or factory; in some embodiments, the production history module 200 can collect and After identification, it is sent to the production history module 200. The production history module 200 is configured to combine the product information associated with the DUT 300 (such as labeling multiple DUTs 300 of the same batch with relevant product information such as IDs and barcodes) and the information collected by the information collection module 140 The electrical data or the image data and/or the electrical data or the image data stored by the information storage module 170 are combined into a comparison database (to be further described below). In some embodiments, the production history module 200 connected to the information collection module 140 is configured to provide a product information associated with the object under test 300 and obtained by the electrical testing module 110 of the test unit 190 The electrical data and/or the image data obtained by the image capture module 120 of the testing unit 190 are combined. in some implementations In an example, the product information provided by the production history module 200 can be used as an identification record of the electrical data and/or image data of the object under test 300 obtained by the test unit 190, so as to record the data obtained by the test unit 190. Which model or batch number does the electrical data and/or image data of the object under test 300 belong to, and provide subsequent processing of these electrical data and/or image data, such as interpretation or analysis of one of these data identify. In some embodiments, the production history module 200 may include an incoming inspection device. The incoming inspection equipment can inspect the complete production record of the object under test 300 . In some embodiments, the production history record may include a public and traceable complete record of the test object 300 from production, processing, sub-packaging, transportation, and the like. The production history module 200 can transmit the complete production history record to the information collection module 140 . In some embodiments, the production history module 200 can integrate the product information associated with the object under test 300 with the production history record of the object under test 300 . For example, by integrating relevant product information such as IDs and barcodes on multiple DUTs 300 of the same batch with production history records. In some embodiments, the production record of the object under test 300 can be added to the comparison database as another type of comparison data. For example, the artificial intelligence judgment module 150 can use production history records as another benchmark for comparison and judgment (for example, the same process factory uses a special process (such as a copper process) for a special DUT 300, and may have the same abnormality at the same time. electrical value). After the object under test 300 is measured or tested, the artificial intelligence judgment module 150 makes a judgment based on the obtained electrical data of the object under test 300 and the production history records, and the artificial intelligence judgment module 150 can judge by comparison The obtained electrical data and production history records, and then found that the abnormality of the electrical data may be related to a certain process step of the process factory (such as process factors/or other upstream product problems), and issued instructions to remind the operator to notify the pending The process engineer of the DUT 300 conveys that the electrical data of the DUT 300 is abnormal or the yield rate is low, and the manufacturing process of the DUT 300 needs to be adjusted to improve the yield rate of the DUT 300 or improve the process problem. question.

Referring to FIG. 1 again, in some embodiments, the information storage module 170 is connected to the information collection module 140, which is configured to store the DUT 300 measured and/tested by the test unit 190. electrical information and the image data.

In some embodiments, the artificial intelligence judgment module 150 is connected to the information collection module 140; the artificial intelligence judgment module 150 is configured to receive and judge the correctness of the electrical data and the image data. In some embodiments, the artificial intelligence judgment module 150 is configured to combine the collected electrical data and/or image data of the object under test 300 with the object under test 300 provided by the production history module 200 An associated product information is integrated to make a judgment on the collected electrical data and the image data of the object under test 300 . In some embodiments, the artificial intelligence judging module 150 includes an artificial intelligence processor configured to judge the electrical data and the image data. In some embodiments, the artificial intelligence processor is configured to determine the statistical process control chart (Statistic Process Control Chart "SPC Chart") of the electrical data of the object under test 300, for example, when multiple objects under test 300 The electrical data of the continuous exceeding specification falls outside 3 standard deviations. In some embodiments, the artificial intelligence processor can determine the electrical data of a single DUT 300, for example, when the electrical data of a single DUT 300 exceeds the specifications provided by the production history module 200, the artificial intelligence The processor will issue a warning command and issue an abnormality/interruption indication to the processing module 160 to interrupt the test/measurement operation and perform subsequent processing actions (such as test machine calibration or test needle cleaning operation).

In some embodiments, the artificial intelligence processor can summarize the electrical data of a plurality of DUTs 300 (that is, generate an SPC chart (SPC chart)), and the artificial intelligence processor can use the data from the production history module 200 The target value of the provided electrical data defines the center line (CL), upper control limit (UCL) and lower control limit (LCL) of the SPC chart, so as to be based on the difference of the SPC chart The conventional rule is to judge multiple electrical data, for example, when measuring multiple DUTs 300, when 9 consecutive electrical data of different DUTs fall on the same side of the center line, and for example, when 6 consecutive When the electrical data of different DUTs show that their values continue to increase or decrease, and for example, when 11 consecutive pieces of electrical data of different DUTs show that their values continuously and alternately rise and fall, the artificial intelligence processor will Send a warning command and send an abnormality/interruption indication to the processing module 160 to interrupt the test/measurement operation and perform subsequent processing actions (such as test machine calibration or test needle cleaning operation). In some embodiments, the artificial intelligence judging module 150 is configured to judge whether the electrical test performed by the testing unit 190 on the DUT 300 is abnormal according to the continuous test failure data of a plurality of DUTs 300 .

In some embodiments, the electrical data obtained by the test unit 190 from measuring the DUT 300 includes functional test data, and the artificial intelligence judgment module 150 is configured to judge the DUT through the obtained functional test data. Yield rate of 300 items. In some embodiments, the electrical data may include values such as commonly measured voltage, resistance, current, and/or inductance, and the functional test data may include tests such as open or short. In some embodiments, the functional test data includes the following abnormal rule of SPC Chart: if the electrical data of the above-mentioned multiple DUTs continuously exceeds the specification and falls outside 3 standard deviations, 9 consecutive different DUTs The electrical data fall on the same side of the average value, 6 consecutive electrical data of different DUTs show their values continuously increasing or decreasing, 11 consecutive electrical data of different DUTs show their values continuously and alternately rising and For example, the artificial intelligence processor will issue a warning command and issue an abnormality/interruption indication to the processing module 160 to interrupt the test/measurement operation, and perform subsequent processing actions (such as test machine calibration or test needle cleaning) Operation).

In some embodiments, the artificial intelligence processor is configured to store a plurality of image data in a comparison database (see FIGS. 3a-3c and FIG. 4a to FIG. 4h), and use multiple image data as comparison data for judging and training the artificial intelligence processor. In some embodiments, the comparison database (not shown) can be included in the production history module 200, and the production history module 200 can combine the product information associated with the object under test 300 with the information collection module 140 The collected electrical data or the image data and/or the electrical data or the image data stored by the information storage module 170 are combined and stored in the comparison database. In some embodiments, the production history module 200 can combine the product information associated with the object under test 300 with the collected electrical data or the image data, and further combine the electrical data or the image The product information associated with the image data is stored in the comparison database for the training and judgment of the artificial intelligence processor. In other words, the artificial intelligence processor of the artificial intelligence judgment module 150 can obtain the product information associated with the object under test 300 and the collected electrical data or image data of the object under test 300 from the comparison database To compare the collected data of the object under test 300 or for its self-learning and training. In some embodiments, the comparison database includes a plurality of default comparison reference image data from the production history module 200 . In some embodiments, the plurality of image data and the plurality of preset comparison reference image data include an appearance image of the object under test 300 and a probe mark. In some embodiments, the plurality of preset comparison reference image data includes multiple types of abnormal reference images, and in some embodiments, the abnormal reference images include objects with dirt on the surface of the test object Reference image, there are probe marks on the surface of the object to be tested that exceed 1/4 of the size of the crystal grain, scratches on the surface of the object to be tested, and one of the crystal grains on the surface of the object to be measured There are no probe marks on the grain or more than three probe marks on one grain, there are probe marks exceeding 25% of the grain size on the surface of the object to be tested, and there are holes on the surface of the object to be tested. There are probe mark shifts or probe mark shifts on the surface of the object to be tested, no needle marks or too light needle marks on the surface of the object to be measured, etc. Therefore, artificial intelligence The cutting module 150 can compare the image data of the object under test 300 with the abnormal reference images according to the complete data in the comparison database, so as to determine whether the image data of the object under test 300 conforms to these abnormal reference images. One of the abnormal fiducials in the abnormal fiducial image.

In some embodiments, the plurality of preset comparison reference image data is not limited to the above-mentioned multiple forms or types, and the plurality of preset comparison reference image data may include more types of reference images, For example, any comparison reference data manually set or input can be set as the reference image in the comparison database, and the reference image is used to further train the artificial intelligence processor to judge whether the image data is normal or benchmarked. In other words, any set or input comparison data can be set as reference comparison image data or abnormal image data.

The artificial intelligence processor is configured to store multiple batches of electrical data in a comparison database, and use the multiple batches of electrical data as comparison data for judging and training the artificial intelligence processor. Judging the electrical data and the image data includes when the artificial intelligence judgment module 150 judges that the image data conforms to a preset image data, then the artificial intelligence judgment module 150 issues an instruction to instruct to continue the measurement operation. Judging the electrical data and the image data includes when the artificial intelligence judging module 150 judges that the electrical data conforms to a preset electrical data, then the artificial intelligence judging module 150 issues an instruction to instruct to continue the measurement operation.

In some embodiments, the artificial intelligence judging module 150 is configured to judge the yield rate of the electrical data or the failure of several consecutive tests of the DUT 300 .

In some embodiments, the processing module 160 is connected to the artificial intelligence judgment module 150 . The processing module 160 is configured to issue commands in response to the artificial intelligence judgment module 150 . In some embodiments, the processing module 160 is further connected to the test unit 190; and the command issued by the processing module 160 can drive the test unit 190 to perform related operations.

In some embodiments, when the artificial intelligence judgment module 150 judges that the test value of the electrical data of a single DUT 300 deviates from the center value or the electrical data of multiple batches conform to the abnormal rule of SPC Chart, the artificial intelligence judges The module 150 provides relevant information to the processing module 160 , and the processing module 160 responds to the relevant information provided by the artificial intelligence judgment module 150 to send an instruction to the testing unit 190 . In some embodiments, when the artificial intelligence judgment module 150 judges that the yield rate of multiple batches of electrical data has decreased, the artificial intelligence judgment module 150 will provide relevant information to the processing module 160, and the processing module 160 responds The relevant information of yield reduction provided by the artificial intelligence judgment module 150 is used to issue instructions to the test unit 190 . In some embodiments, when the artificial intelligence judging module 150 judges that the image data conforms to a preset abnormal image data or the judged abnormal image data, the artificial intelligence judging module 150 will provide relevant information to the processing module Group 160 , the processing module 160 issues instructions to the testing unit 190 . In some embodiments, when the artificial intelligence judging module 150 judges that the image data conforms to a preset comparison reference image data, the artificial intelligence judging module 150 will not send an exception/interruption indication to the processing module 160 to continue the test/measurement job. In some embodiments, when the artificial intelligence judgment module 150 judges that the image data conforms to a preset comparison reference image data, the artificial intelligence judgment module 150 sends a test/measurement instruction to the processing module 160, to continue the test/measurement operation.

In some embodiments, the probe cleaning device 320 may include a cleaning pad, a brush (nano brush or plastic brush) or an air spray gun (not shown). The processing module 160 sends a command to the testing unit 190 to drive the robotic arm 400 to operate the probe cleaning device 320 , for example, to grab or grasp a cleaning pad (Clean Pad) to move to the testing probe 130 for cleaning the probe. In some embodiments, the probe cleaning device 320 includes a brush (nano brush or plastic brush) or an air spray gun. In some embodiments, the processing module 160 issues instructions to drive the probe cleaning device, such as A brush (nano brush or plastic brush) or an air spray gun is used to clean the test probe 130 . In some embodiments, when an abnormal situation occurs, the processing module 160 first performs a needle cleaning operation on the test probe 130, and then performs a retest operation on the object under test 300. If the abnormal situation still occurs after the retest, then An instruction is issued to remind the operator to perform a pin change operation. In some embodiments, the processing module 160 sends instructions to the test unit 190 to perform hardware calibration.

2a to 2c are schematic diagrams of the operation of the testing unit 190 according to an embodiment of the present disclosure. See Figure 2a. The testing unit 190 includes a robotic arm 400 capable of grasping the object under test 300 . Referring to FIG. 2b, the testing unit 190 is in a testing state. A test circuit board 310 is disposed under the test unit 190 and the robotic arm 400 . A test fixture 500 and a test probe 130 are disposed on the test circuit board 310 . The test probe 130 is disposed in the test fixture 500 , and the test fixture 500 is used to fix the test probe 130 . In some embodiments, the robot arm 400 grabs the object under test 300 and aligns it with the test probe 130 to measure and test the object under test 300. During the measurement and test, the test probe 130 physically Contact the object under test 300 to measure the electrical data of the object under test 300 . Referring to FIG. 2c, the test unit 190 is in the state of automatically cleaning the test probes. The robot arm 400 grips or grabs a cleaning pad and aligns it with the test probe 130 , and uses the cleaning pad to physically contact the test probe 130 to clean the test probe 130 , so as to complete the cleaning of the test probe 130 . When an abnormal situation occurs in the test and measurement, clean the probe first, and then retest the object 300 to be tested. If the abnormal situation still occurs after retesting, an instruction is issued to remind the operator to replace the probe. . If after changing the probe, the abnormal situation still occurs after the test and measurement (for example, the electrical data of multiple consecutive DUTs exceeds the specification and falls outside 3 standard deviations, and the electrical data of 9 consecutive DUTs When the data falls on the same side of the average value, the electrical properties of 6 consecutive different DUTs If the data shows that its value continues to increase or decrease, or the electrical data of 11 consecutive different DUTs show that its value continuously and alternately rises and falls, etc.), it is an online process problem, and the artificial intelligence processor will issue instructions to remind the operation The staff confirms whether the measurement system 100 is faulty or needs to be adjusted and repaired, or informs the process engineer of the DUT 300 to convey that the yield rate of the DUT 300 is low, and the manufacturing process of the DUT 300 needs to be adjusted to improve the yield of the DUT 300 Rate.

3 a to 3 c are probe traces on the surface of the object under test 300 according to an embodiment of the present disclosure. Fig. 3a is an example of probe marks (image data) judged to be normal after testing. 3b and 3c are examples of probe marks (image data) that are judged to be abnormal after testing. The arrows in Figure 3b indicate scratches on the probe, which are left when the probe accidentally scratches the surface of the object under test 300 during measurement. The scratches on the probe are abnormal, and they are different from the normal probe marks on the side. (e.g. multiple full circle probe marks, black circles enclosing white round bright spots). The arrows in Figure 3c point to traces of poor contact of the probe, which are white round bright spots that are incompletely covered by black circles when the force of the probe is too light or the contact area is too small, similar to the traces of the moon. Needle marks are abnormal.

4a to 4h are image data of the surface of the object under test 300 determined to be abnormal according to an embodiment of the present disclosure. In some embodiments, the image data judged to be abnormal include: there must be no dirt on the surface of the object to be tested (see Figure 4a), and there must be no particles exceeding 1/4 of the grain size on the surface of the object to be tested. Probe marks (see Figure 4b), there must be no probe marks on the surface of the object to be tested Scratch (scratch) (see Figure 4c), there must be no probe marks on one of the dies on the surface of the object to be tested Or more than three probe marks on one grain (see Figure 4d), the surface of the object to be tested must not have probe marks exceeding 25% of the grain size area (see Figure 4e), and the surface of the object to be tested There must be no damage to holes (see Figure 4f), no probe mark shift or shift marks (probe mark shift) on the surface of the object to be tested (see Figure 4g), the surface of the object to be tested There must be no needle marks or too light needle marks on the surface (see Figure 4h).

FIG. 5 discloses a test process 6 of testing an object under test 300 using the measurement system 100 according to an embodiment of the present disclosure.

In step 610 , the DUT 300 is provided to the testing unit 190 . In some embodiments, the picture capture module 120 of the test unit 190 will take pictures of the test unit 190 for incoming inspection, so as to capture the initial state of the object under test 300 (that is, it has just been provided to the test unit 190 and has not yet been electrically charged. In some embodiments, the picture capture module 120 can send the captured image data of the incoming inspection to the information collection module 140 . If it is found that the object under test 300 is a defective product during the incoming material inspection and photographing, it will be transported to the defective product collection area (such as a fail tray), and the remaining objects under test 300 that have been photographed during the incoming material inspection can be sent to the next One step test.

In step 620 , electrical data of the object under test 300 is collected through the electrical testing module 110 . In some embodiments, the electrical property detection module 110 uses the test probe 130 to contact the object under test 300 to measure and obtain the electrical property data of the object under test 300 . In some embodiments, the electrical property detection module 110 can send the collected electrical property data to the information collection module 140 . If it is found that the DUT 300 is a defective product during the electrical data measurement, it will be transported to the defective product collection area (such as a fail tray), and the remaining DUTs 300 that have undergone electrical data measurement can be Proceed to the next step of the test.

In step 630 , the image data of the object under test 300 is captured through the image capture module 120 . After the object under test 300 is electrically measured, the test probe 130 will leave probe marks on the object under test 300 , and the image capture module 120 is used to test the object 300 under test. After the performance measurement, the visual inspection is taken to obtain the image data of the needle mark on the object 300 to be tested. In some embodiments, the image capture module 120 can send the collected electrical data to the information collection module 140 . If it is found that the object under test 300 is a defective product when the appearance inspection is taken, it will be It is transported to a defective product collection area (such as a fail tray), and the other UUTs 300 that have been photographed through visual inspection can be regarded as good products, and then they are transported to a good product collection area (such as a pass tray).

In step 640 , the electrical data of the object under test 300 collected by the electrical property detection module 110 and the image data of the object under test 300 captured by the image capture module 120 are further processed. In some embodiments, the electrical data of the object under test 300 collected by the electrical property detection module 110 and the image data of the object under test 300 captured by the image capture module 120 are stored in the Information storage module 170 . In some embodiments, the electrical data or the image data collected by the product information associated with the DUT 300 in the production history module 200 and the information collection module 140 and/or the information storage module 170 A combination of the stored electrical data or the image data. In some embodiments, the image data of the object under test 300 obtained by taking pictures of the incoming material inspection and the electrical data collected by the information collection module 140 or the image data and/or information storage module 170 A combination of the stored electrical data or the image data.

In step 650, the collected electrical data and image data of the object under test 300 are combined with the related product information from the production history module 200 to make a judgment. In some embodiments, the artificial intelligence judging module 150 will receive and judge the electrical data and experience of the object under test 300 collected by the electrical testing module 110 through the associated product information from the production history module 200 The image data of the object under test 300 captured by the image capturing module 120 . In some embodiments, the artificial intelligence judgment module 150 is configured to judge whether the test values of multiple batches of electrical data in the Statistical Process Control (SPC) of the object under test 300 deviate from the central value. In some embodiments, the artificial intelligence judgment module 150 is configured to judge whether the yield rate of multiple batches of electrical data is reduced. In some embodiments, the artificial intelligence judging module 150 is configured to judge whether the image data conforms to a preset abnormal image data or determine abnormal image data.

In step 660, a processing instruction is issued according to the judgment result. In some embodiments, processing The module 160 is configured to issue instructions to process the test unit 190 in response to the information provided by the artificial intelligence judgment module 150 . In some embodiments, the processing module 160 responds to the information provided by the artificial intelligence judgment module 150 and sends instructions to the testing unit 190 to drive the robotic arm 400 to grab or grab a cleaning pad and move to the testing probe 130 for testing. Clean probe work. In some embodiments, the processing module 160 responds to the information provided by the artificial intelligence judgment module 150 to issue instructions to drive a brush (nano brush or plastic brush) or an air spray gun to clean the test probe 130. . In some embodiments, the processing module 160 sends an instruction in response to the information provided by the artificial intelligence judgment module 150 to remind the operator to perform a pin change operation. In some embodiments, the processing module 160 responds to the information provided by the artificial intelligence judgment module 150 and sends instructions to the testing unit 190 for hardware/software calibration. In some embodiments, the test unit 190 can automatically perform software calibration operations, such as electrical data calibration. In some embodiments, an operator may perform manual calibration of the hardware/mechanics of the system 100 , such as adjusting the distance, height or level between the components of the system 100 to be accurate. In some embodiments, when an abnormal condition occurs in the test and measurement, the probe is cleaned first, and then the object under test 300 is retested. If the abnormal condition still occurs after the retest, an instruction is issued to remind The operator performs the work of changing the probe. If after changing the probe, the abnormal situation still occurs after the test and measurement (for example, the electrical data of multiple consecutive DUTs exceeds the specification and falls outside 3 standard deviations, and the electrical data of 9 consecutive DUTs When the data fall on the same side of the average value, the electrical data of 6 consecutive different DUTs show that their values continue to increase or decrease, or the electrical data of 11 consecutive different DUTs show that their values continuously and alternately rise and decline, etc.), it is determined to be a process factor, and the artificial intelligence processor will issue an instruction to remind the test operator/test engineer to confirm whether the measurement system 100 is faulty or needs to be adjusted and repaired, or to notify the process engineer of the object under test 300 to convey the object under test 300 The yield rate is low, and the manufacturing process of the DUT 300 needs to be adjusted.

In some embodiments, a method for testing a DUT includes: measuring and collecting electrical data of the DUT; judging the electrical data; and processing according to a judgment result of the electrical data A test unit for testing the DUT.

In some embodiments, the artificial intelligence processor can use the analysis of big data to assist test engineers to accurately judge the timing and interval of starting the probe cleaning device such as needle cleaning and needle replacement. When the probe cleaning device starts And the interval time is set as a rule (the judgment rule of the artificial intelligence processor can be continuously trained and updated by using big data), which can more effectively reduce the judgment time and judgment error required by the test engineer.

In some embodiments, the electrical data includes functional test data or yield data of the DUT.

In some embodiments, the method of testing an analyte further includes: storing the electrical data.

In some embodiments, the method of testing a DUT further includes: combining a product information of the DUT with the electrical data.

In some embodiments, the judging the electrical data includes: judging that test values of multiple batches of electrical data in the Statistical Process Control (SPC) of the DUT deviate from a central value.

In some embodiments, the judging the electrical data includes: including judging and predicting the failure of several consecutive tests of the object under test.

In some embodiments, processing/cleaning the test unit for testing the DUT includes: performing a needle cleaning operation, performing a needle replacement operation, or performing a hardware calibration operation.

In some embodiments, the method for testing an object under test further includes: capturing image data of the object under test after measuring and collecting the electrical data of the object under test.

In some embodiments, a method for testing an object under test includes capturing image data of the object under test; judging the image data; Test the test unit of the DUT.

In some embodiments, the image data includes a probe trace image file of the object under test.

In some embodiments, the method of testing an object under test further includes: storing the image data.

In some embodiments, the method of testing an object under test further includes: combining product information of the object under test with the image data.

In some embodiments, the judging the electrical data includes: judging an image of a probe trace state of the analyte.

In some embodiments, a probe mark file judging criterion is used to determine whether the state of the probe mark of the object under test is abnormal.

In some embodiments, the method of testing an object under test further includes: measuring and collecting electrical data of the object under test before capturing the image data of the object under test.

In some embodiments, the electrical testing module includes a network analyzer or an automatic test equipment (ATE).

In some embodiments, the image capture module includes a charge-coupled device (Charge-coupled Device "CCD") or an automatic optical inspection (Automated Optical Inspection "AOI") device.

In some embodiments, the processing module is configured to drive a robotic arm.

The terms "approximately", "substantially" and "about" in this disclosure are used to describe minor variations. When these terms are applied to a general situation or situation, they may mean a precise description of the situation or situation or they may mean a situation or situation close to that described. For example, when these terms are used to describe a value, it can mean that the value can have a variation of less than or equal to ±10%, a variation of less than or equal to ±5%, a variation of less than or equal to ±4%, and a variation of less than or equal to ±3%. A change of less than or equal to ±2%, a change of less than or equal to ±1%, a change of less than or equal to ±0.5%, a change of less than or equal to ±0.1%, or a change of less than or equal to ±0.05%. For example, if two values are considered to be "substantially" the same, it may mean that the difference between the values and the mean of those values is less than or equal to ±10%, less than or equal to ±5%, less than or equal to ±4%, less than Equal to ±3%, less than or equal to ±2%, less than or equal to ±1%, less than or equal to ±0.5%, less than or equal to ±0.1%, or less than or equal to ±0.05%.

Two surfaces may be considered coplanar or substantially coplanar if the displacement between the surfaces is not greater than 5 μm, not greater than 2 μm, not greater than 1 μm, or not greater than 0.5 μm.

Furthermore, where totals, proportions or other values are expressed in ranges, this is not intended to be limiting to that particular number but rather includes any number within the range or a subrange thereof.

Although the technical content and characteristics of the present invention are as described above, those who have ordinary knowledge in the technical field of the present invention can still make many changes and modifications without departing from the teaching and disclosure of the present invention. Therefore, the scope of the present invention is not limited to the disclosed embodiments but includes other changes and modifications without departing from the present invention, which are covered by the scope of the following claims.

100: Measurement system

110:Electrical detection module

120:Picture capture module

130: Test probe

140:Information collection module

150: Artificial Intelligence Judgment Module

160: Processing module

170:Information storage module

190: Test unit

200: Production history module

Claims (16)

A measurement system, which includes: a test unit configured to contact an object under test to obtain information, wherein the test unit includes an electrical detection module configured to obtain information on the object under test The electrical data of the information: an information collection module, which is connected to the test unit, and which is configured to collect the information of the DUT; an artificial intelligence judgment module, which is connected to the information collection module, And it is configured to receive and judge the information, wherein the artificial intelligence judgment module is configured to judge the test of multiple batches of electrical data in Statistic Process Control (SPC) of the DUT The value deviates from the central value; and a processing module, which is connected to the artificial intelligence judgment module and is configured to issue commands in response to the artificial intelligence judgment module. The measurement system according to claim 1, wherein the test unit includes an image capture module configured to obtain an image data of the information of the object under test. The measurement system of claim 1 further includes a production history module connected to the information collection module, which is configured to provide product information associated with the DUT. The measurement system of claim 1 further includes an information storage module connected to the information collection module and configured to store the information of the object under test. The measurement system according to claim 1, wherein the processing module is configured to drive a probe cleaning device. The measurement system according to claim 1, wherein the processing module is configured to calibrate the test unit. The measurement system according to claim 1, wherein the artificial intelligence judging module is configured to judge the electrical data including judging and predicting the failure of several consecutive tests of the object under test. The measurement system according to claim 2, wherein the artificial intelligence judging module is configured to compare the image data with an abnormal reference image data to judge that the image data conforms to an abnormal reference. The measurement system according to claim 2, wherein the image data includes an appearance image of the object under test and/or an image of a probe trace. A method of using a measurement system, comprising: providing an object under test; using a test unit to obtain information about the object under test, wherein the test unit is used to obtain an electrical data of the information about the object under test; Use an artificial intelligence judgment module to judge the information, and use the artificial intelligence judgment module to judge the electrical data, wherein the electrical data is judged Judgment includes: judging that the test values of multiple batches of electrical data in the Statistic Process Control (SPC) of the DUT deviate from a central value or judging the DUT based on the continuous test failure data of the DUT. Whether the test unit is abnormal; and using a processing module to issue an instruction in response to the judgment of the artificial intelligence judgment module. The method according to claim 10 further includes: using an information collection module to receive the information of the object under test obtained by the test unit, and sending the information to the artificial intelligence judgment module for judgment. The method according to claim 10, further comprising: using a production history module to provide one of the associated product information of the DUT so that the artificial intelligence judgment module can be identified by the associated product information and used for matching with the test unit The obtained information of the analyte is combined for judgment. The method as claimed in item 10, wherein the electrical data includes functional test data of the DUT, and wherein the artificial intelligence judgment module is configured to use the functional test data to judge whether the DUT is good or not Rate. The method of claim 10, wherein the test unit is used to obtain an image data of the information of the object under test, and the artificial intelligence judgment module is used to compare the image data with an abnormal reference image data, to It is judged that the image data meets an abnormal criterion. The method according to claim 10, wherein the test unit is used to obtain electrical data and an image data of the information of the object under test, wherein the object under test is obtained after obtaining the electrical data of the object under test the image data. The method of claim 10, wherein the command issued by the processing module includes: cleaning a probe of the test unit or calibrating the test unit.

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