Intelligent defect identification system

Various defects in an electronic assembly can be intelligently identified with a system having at least a server connected to a first capture module and a second capture module. The first capture module may be positioned proximal a first manufacturing line while the second capture module is positioned proximal a second manufacturing line. Images can be collected of first and second electronic assemblies by respective first and second capture modules prior to the images being sent to a classification module of the server where at least one defect is automatically detected in each of the first and second electronic assemblies concurrently with the classification module.

SUMMARY OF THE INVENTION

In accordance with some embodiments, an intelligent defect identification system can have a server connected to a first capture module and a second capture module with the first capture module positioned proximal a first manufacturing line where a first electronic assembly is located and the second capture module positioned proximal a second manufacturing line where a second electronic assembly is located. A classification module of the server is adapted to automatically detect defects in each of the first and second electronic assemblies concurrently.

Various embodiments arrange an intelligent defect identification system with a server connected to a first capture module and a second capture module. The first capture module may be positioned proximal a first manufacturing line while the second capture module is positioned proximal a second manufacturing line. Images can be collected of first and second electronic assemblies by respective first and second capture modules prior to the images being sent to a classification module of the server where at least one defect is automatically detected in each of the first and second electronic assemblies concurrently with the classification module.

In other embodiments, an intelligent defect identification system has a server connected to a first capture module and a second capture module. The first capture module may be positioned proximal a first manufacturing line while the second capture module is positioned proximal a second manufacturing line. Images can be collected of first and second electronic assemblies by respective first and second capture modules prior to the images being sent to a classification module of the server where at least one defect is automatically detected in each of the first and second electronic assemblies concurrently with the classification module. One or more performance metrics is predicted for each of the electronic assemblies with a prediction module of the server based on the at least one detected defect.

DETAILED DESCRIPTION

Various embodiments of the present disclosure are generally directed to an intelligent defect identification system that decrease fabrication time, while increasing accuracy, of electronic assemblies.

Automated inspection of complex electronic assemblies with variable topography and surface finishes is difficult with a rule-based analysis methodology. Instead, human visual analysis is commonly employed to evaluate the presence, and severity, of defects in a constructed electronic assembly. However, human involvement in defect identification is relatively slow, particularly compared to the rate at which commercial electronic component fabrication can produce assemblies to inspect.

In addition, the plethora of different types, sizes, locations, and appearances of defects can be difficult to reliably characterize via automated testing in a manner that provides the ability to accurately assess if a constructed electronic assembly can operate as intended. The sophistication of machine learning algorithms and techniques can, over time, improve the automated identification of defects, but have been confined to individual testing stations evaluating one constructed assembly at a time, which is tedious and slow. Hence, there is a continued interest in optimizing automated electronic assembly defect analysis to provide faster defect identification and evaluation with high accuracy.

Accordingly, embodiments are directed to a defect server concurrently testing a plurality of different constructed electronic assemblies with a testing module resident in the defect server to identify at least one defect and predict whether that at least one defect will inhibit operation of one of the plurality of different electronic assemblies. The testing module can employ a learning module where defect evaluation can be improved over time by utilizing machine learning techniques in combination with human oversight. By automating defect identification and characterization with a centralized defect server, numerous electronic assemblies from diverse locations can be concurrently evaluated, which can decrease overall assembly fabrication time without sacrificing accuracy.

FIG. 1depicts an example data storage system100which utilizes one or more electronic components tested in accordance with assorted embodiments. Any number of data storage devices102can be employed in the data storage system100. Those data storage devices102can have any capacity, data access speed, and memory type that connect to one or more remote hosts104via wired and/or wireless interconnections106.

In the non-limiting example shown inFIG. 1, a data storage device102has a local controller108, such as a microprocessor or programmable circuitry, that directs data access, and maintenance, operations to/from the rotating data storage media110via one or more transducing assemblies112. It is noted that the rotating media110and transducing assemblies112may be replaced with solid-state memory arrays in some embodiments. A transducing assembly112can comprise a suspension114that positions a transducing slider116proximal predetermined locations on the media110to allow a data writer118and/or data reader120to program data to, and retrieve data from, the media110.

Any portion of the data storage system100can be physically constructed of one or more electronic components assembled and interconnected together.FIG. 2illustrates a line representation of an example electronic assembly130that can be utilized in the data storage system100ofFIG. 1, such as the data storage device102or host104. Although not required or limiting, the electronic assembly130is a transducing slider where separate bond pads132are interconnected with conductive signal pathways134on a common substrate136. It is noted that the various bond pads132can be locations for any number of electrical components, such as a data writer118, a data reader120, or at least one sensor, like a differential-ended thermal coefficient of resistance sensor.

The electronic assembly130can experience one or more defects as a result of fabrication and/or transport during manufacturing. A defect can be characterized as any imperfection in a bond pad132, pathway134, substrate136, or electronic component physically attached to a bond pad132that materially degrades operation of any aspect of the electronic assembly130. For instance, a defect may be a physical imperfection that allows short-term functionality, but degrades long-term reliability. As another non-limiting example, a defect may degrade, but not stop function of one or more components attached to the assorted bond pads132.

Although not exclusive or exhaustive, the example electronic assembly130ofFIG. 2shows how a bond pad132may have an incorrect, asymmetrical shape, as illustrated by region138, that can be contributed to more, or less, bond pad132material being present. Region140conveys another bond pad132defect where a pad132is misaligned while having a correct, symmetrical shape. The varying topography of the electronic assembly130can contribute to the presence of reduced bond pad132thickness, as shown by regions142, where underlying material, such as copper, is exposed. A wire bond defect, as illustrated in region144by a segmented signal pathway134, can also degrade the function and/or performance of some, or all, of the electronic assembly.

Despite heightened fabrication tolerances and more sophisticated fabrication techniques, defects can inadvertently arise in electronic assemblies130.FIG. 3conveys an example timeline150of activity that can accompany manufacturing of an electronic assembly to mitigate the effect of defects in data storage devices shipped to end-users. An electronic assembly is constructed in step152in accordance with a predetermined layout to position electrical aspects to provide electrical functionality when incorporated into a data storage device. Step154then interconnects the various electrical aspects via one or more signal pathways.

It is noted that the interconnection of step154may be conducted in step152. Regardless, electrical interconnection of the aspects of an electrical assembly is followed by inspection of the assembly in step156. Inspection may be conducted visually by machine or human and may involve additional sensors, such as thermal, radar, or sonar systems. A preliminary inspection in step156may trigger additional inspection in step158, which may evaluate portions of the electrical assembly redundantly and/or with heightened scrutiny. Failure of both inspections156/158can prompt for the discarding of the electrical assembly as inoperative junk in step160. However, it is contemplated that the additional inspection of step158can identify the electronic assembly as operative, which proceeds to step162where the electronic assembly is packaged into a data storage device with one or more other electrical components, such as a controller.

Dual inspection steps are not always necessary and inspection in step156can result in the electronic assembly being deemed valid and operable before being packaged in step162. One or more functional tests can subsequently be conducted in step164on the packaged data storage device. Testing may identify a defect, failure, or other performance degrading condition that triggers step160to discard some, or all, of a data storage device. In the event the performance of the packaged data storage device is verified in step164, step166ships the data storage device to an end-user for incorporation into a computing system, such as a desktop computer, laptop, tablet, smartphone, smartwatch, cloud computing rack, mass data storage enclosure, or server.

With the inspection156/158and testing164activity, an electronic assembly can undergo a thorough analysis to ensure functionality and performance. In yet, certification of the structure and performance of an electronic assembly can be a bottleneck for the manufacturing of a data storage device. That is, visual inspection of an electronic assembly can be relatively slow, tedious, and plagued by additional handling and transportation risks.FIG. 4is a flowchart of an example visual inspection routine170that can be conducted as part of the manufacturing timeline150ofFIG. 3in accordance with various embodiments. The routine170can begin with any amount of construction of an electronic assembly in step172, such as placing electrical features on a substrate, forming electrical features on a substrate, and/or conducting lithography to fabricate electrically conductive aspects of an electrical assembly.

The electrical features constructed in step172are then electrically interconnected in step174via one or more signal pathways. It is expected that at the conclusion of step174, an electrical assembly is structurally complete and ready to be integrated into a data storage device unless the operations of steps172and/or174resulted in one or more defects being present. Step176conducts a visual inspection of the completed electrical assembly to identify any defects. Such visual inspection can be conducted by a human physically engaging the electrical assembly or optically sensing the electrical assembly from a remote location. The human involvement with step176has been deemed critical, in the past, to accurately assess the presence and severity of defects due to rule-based automated evaluation and/or inspection algorithms have not been sophisticated enough to provide accurate defect characterization.

The inclusion of human inspection in step176allows decision178to first identify if one or more defects are present. If so, step180then utilizes the human inspector to evaluate each defect. Such evaluation can be to classify a defect by type, location, size, and severity. The evaluation of step180may further engage the human inspector to speculate as to the functionality of the electronic assembly in decision182, but such speculation is not required as decision182may evaluate the functionality of the electronic assembly based on predetermined evaluation rules, such as threshold defect size and/or location. An electronic assembly with degraded functionality triggers step184to discard the assembly, which may involve returning to step172where portions of the defective electronic assembly are remanufactured.

A defect that does not detrimentally affect electronic assembly functionality or performance can cause decision182to proceed to step186where the electronic assembly is incorporated into a data storage device, such as part of step164ofFIG. 3. Alternatively, a lack of any defects from decision178causes step186to package the electronic assembly into a data storage device. Although logically straightforward, the involvement of human inspection is detrimental to routine170and timeline150at least in terms of electronic assembly evaluation time compared to what could be achieved by implementing automated computing intelligence that has previously been unachievable.

Accordingly, various embodiments employ a centralized computing server to concurrently evaluate multiple different electronic assemblies and characterize any discovered defects without human inclusion. The example intelligent inspection routine200ofFIG. 5depicts how electrical assemblies, such as data transducing sliders, can be optimally inspected as part of the manufacturing of a data storage device. Routine200can begin similarly to routine170with various electrical features placed and interconnected in one or more steps that result in step202submitting a structurally complete slider assembly for inspection.

Any number of sensors, such as optical lenses, optical cameras, and topographical detectors, can capture still, or moving, images in step204of at least some of a slider submitted in step202. The image(s) from step204are transmitted to a central server via a wired and/or wireless network in step206where the image(s) are processed digitally via automated digital analysis as directed by at least one controller of the central server. The digital processing of step206is not limited to a particular analysis, but can involve generating a defect map by comparing a submitted image to an image of a non-defective slider.

The image processing of step206can occur concurrently for numerous different sliders being manufactured in different physical locations, such as fabrication facilities in different cities, countries, and global hemispheres. It can be appreciated that the image taking, transmitting, and processing capabilities of multiple sliders with a central server provides an inspection system optimized for time and efficiency compared to a human visually inspecting individual sliders. The digital processing of step206allows decision208to quickly determine if a defect is present in a submitted slider.

A detected defect causes step210to then classify the defect. Such classification is not limited to a particular defect characteristic, but in some embodiments, classifies each detected defect by size, location, color, and proximity to other defects. The classification of step210can be conducted simultaneously for multiple different submitted sliders due to the computing capabilities of a central server, which is significantly faster than human classification of a single slider. Classified defects allow step212to then predict at least the functional and performance impact of the defect on the slider.

The central server can employ a prediction module that takes the defect classifications of step210and determines if a slider is fit for incorporation in a data storage device. The rules to determinate if a defect is severe enough to merit step214discarding the slider or minimal enough to merit step216packaging the slider into a data storage device are maintained by the server controller and can be updated in real-time to adapt to changing tolerances, defect identifiers, defect severity, and implication of defect classifications on data storage device functionality and performance.

FIG. 6illustrates a line representation of an example defect identification system220in which routine200can be practiced in accordance with some embodiments. Any number (N) of manufacturing lines222located in a common, or dissimilar, physical location can manufacture electronic assemblies224, such as a slider, at a predetermined rate that may, or may not, utilize automated manufacturing techniques. Each manufacturing line222has a capture module226that contains at least one image capturing device, such as an optical sensor and/or camera. It is contemplated that the capture module226contains multiple different electronic assembly imaging sensors227, such as radar, sonar, and acoustic sensors.

It is noted that the various capture modules226do not process images locally, and instead immediately sends any sensed data pertaining to a manufactured electronic assembly224to a single server228. Such data transmission, as opposed to processing sensed data locally, allows the capture modules226to operate as maximum efficiency and accuracy while the server228executes sophisticated computer processing to evaluate and classify sensed data for defects. The relatively low computing requirements for the respective capture modules226further allow the modules226to be space and cost effective, such as for replacement and power usage.

The server228is not limited to particular computing components, but does contain at least a local controller230that directs the processing of sensed data from the assorted capture modules226individually, sequentially, or concurrently. It is noted that components can be trained, such as a graphics processing unit, to operate alone, or in combination with, the server228. Sensed data received by the server228can be temporarily stored in a local memory232and permanently stored in a log234that organizes past defect identification operations, such as number of defects, average severity of identified defects, and average defect identification time. The local controller230can also conduct defect identification with a mapping module236, a classification module238, a prediction module240, and a learning module242. It is noted that each module236/238/240/242comprises programmable circuitry that may be independent, or shared, while being resident in the server228.

The mapping module236may be tasked by the controller230to initially analyze one or more sensed electronic assembly images to create a defect map that highlights, emphasizes, and/or clarifies areas of an image where defects are indicated. The mapping module236can conduct one or more comparisons of a sensed image to known defects to create a defect map. It is contemplated that the mapping module236can additionally indicate areas of concern that do not conform to any known defect, which can catch new defects and other structural issues that can degrade electronic assembly operation.

The classification module238can concentrate on the areas emphasized in a defect map to determine if a defect is indeed present. The utilization of the defect map from the mapping module236allows the classification module238to more quickly and efficiently analyze areas of interest instead of analyzing the entirety of an electronic assembly image. As a result, the classification module238can employ multiple different image analysis algorithms to repeatedly analyze concentrated areas of an electronic assembly image that are less than the entirety of the image to identify the type, size, location, and proximity of defects present in an electronic assembly.

Classifying defects with the classification module238provides information used by the prediction module240to forecast how the defect will affect the operation and/or performance of an electronic assembly. The prediction module240can employ the saved defect history of the server log234, one or more defect models, and at least one algorithm to determine if a defect, or plurality of separate defects, will inhibit data storage device operation if, and when, the electronic assembly is packaged with other data storage components, as shown inFIG. 1.

The prediction module240may predict certain performance metrics in response to the presence of one or more defects. For instance, the prediction module240can compute a predicted adjusted lifespan for the electronic assembly due to the weakened structure of the defect. As another non-limiting example, the prediction module240can prescribe adjusted operational parameters, such as power usage, heat production, media spinning rate, and continuous operating time, to allow the defective electronic assembly to be utilized in a data storage device instead of being discarded.

The mapping236, classification238, and prediction240modules may employ the learning module242to accurately identify, characterize, and predict defect criteria. The learning module242can provide defect identifying instructions that allow a defect to be discovered, analyzed, and characterized from one or more images. Such identifying instructions can generate particular shapes, shading, colors, and/or orientations of adjacent components to allow a defect to be efficiently identified and processed to provide accurate predicted defect performance metrics. The identifying instructions may further be used to train machine learning of the system to more accurately, and/or efficiently, identify defects.

FIG. 7conveys a block representation of an example learning module250that can be employed in a server of a defect identification system in accordance with various embodiments. The learning module250can store a derived defect criteria252, such as average size, color, shading, and position in an electronic assembly. The learning module250can also store past encountered defects254to allow human verification256at a later time. That is, the learning module250can improve the derived defect criteria252by storing defect images254to allow a human to visually evaluate the defect.

As a result of the human verification256, the defect criteria252can be changed to evolve and allow a defect to more accurately, and quickly, be identified then classified. Through repeated adjustments to the defect criteria252provided to the mapping236and classification238modules, the automated identification of defects will improve in both veracity and efficiency. It is contemplated that the learning module250can generate a defect accuracy metric258, such as percentage chance a defect identification, or prediction, is correct. The learning module250may further compute defect accuracy metrics258for different types of defects, such as asymmetrical shape, misalignment, and wire bond defects.

With the computing capability of a server, the learning module250can be concurrently utilized to process a plurality of electronic assemblies simultaneously.FIG. 8displays portions of an example defect identification system270configured in accordance with some embodiments. A mapping module236can intake multiple different electronic assembly images272and produce a defect map274for each electronic assembly. While not limiting, the defect map274can utilize natural, and artificial means to emphasize portions of an image272, such as color, zoomed in areas, and image enhancement, such as line smoothing, where defects may be present.

The emphasized elements in the defect map274are processed by the classification module238to characterize the elements as defects or anomalies. The emphasis of the defect map274can aid the classification module238in differentiating defect types and assessing the severity of a defect imperfection. Once the defects are identified and classified, the prediction module240can utilize the emphasized defects from the defect map274, or the original image272, to forecast the functionality, longevity, and performance of the defective electronic assembly. The ability to utilize the emphasized portions of the defect map274allows the prediction module240to make more accurate predictions in less time than if image272processing were executed by the prediction module240.

Through the various embodiments of the present disclosure, a centralized server can concurrently evaluate a number of different electronic assemblies for defects. The automation of defect identification with a single server allows for increased defect identification accuracy and speed compared to human visual inspections. By utilizing computing capabilities to process data sensed from a constructed electronic assembly, such as a transducing slider, machine learning, sophisticated modeling, and robust algorithms can classify defects so that the performance impact of a defect can be predicted, which allows defective electronic assemblies to be utilized in a practical manner instead of being discarded due to the mere presence of a defect.