Abnormality detection method, device, and program

Abstract

An objective of the present invention is to provide technology capable of appropriately detecting the occurrence of an abnormality in a substrate caused by a treatment solution related to substrate cleaning. This abnormality detection method comprises: discharging a cleaning solution from a nozzle 36 to a wafer W; using a camera 70 to continuously image an imaging region including at least an outer edge portion of the wafer W and an inner edge portion of a cup 3, and acquiring a plurality of captured images of the imaging region in a time series; identifying behavior of the cleaning solution on the basis of the plurality of captured images and detecting the occurrence of the abnormality in the wafer W on the basis of the behavior of the cleaning solution; and outputting an alert when the occurrence of the abnormality in the wafer W has been detected.

Description

Abnormal Detection Method, Apparatus, and Program

[0001] The present disclosure relates to an abnormal detection method, apparatus, and program.

[0002] Patent Documents 1 and 2 disclose a substrate cleaning method in which a processing liquid is supplied to the back surface of a substrate and a brush is pressed against it for cleaning.

[0003] Japanese Patent Application Laid-Open No. 2022-128166, Japanese Patent Application Laid-Open No. 2013-115207

[0004] In the substrate cleaning method as described above, a processing liquid is supplied to the substrate, and a brush slides on the area where the processing liquid is supplied. Here, the processing liquid that comes into contact with the sliding brush may reach a cup surrounding the substrate, and may further bounce back in the cup and land on the surface of the substrate. Such water droplets of the processing liquid that land on the substrate may cause watermarks or spherical defects on the substrate.

[0005] The present disclosure has been made in view of the above circumstances, and provides a technology capable of appropriately detecting the occurrence of abnormalities in a substrate caused by a processing liquid related to substrate cleaning.

[0006] An abnormal detection method according to an aspect of the present disclosure is an abnormal detection method performed by an apparatus related to substrate processing, including discharging a processing liquid from a nozzle onto a substrate, continuously imaging an imaging region including at least an outer edge portion of the substrate and an inner edge portion of a cup surrounding the substrate with a camera, obtaining a plurality of imaging images along a time series of the imaging region, specifying the behavior of the processing liquid based on the plurality of imaging images, detecting the occurrence of an abnormality in the substrate based on the behavior of the processing liquid, and outputting an alert when the occurrence of an abnormality in the substrate is detected.

[0007] According to the present disclosure, it is possible to provide a technology capable of appropriately detecting the occurrence of abnormalities in a substrate caused by a processing liquid related to substrate cleaning.

[0008] Figure 1 is a plan view of a cleaning apparatus that performs the anomaly detection method according to this embodiment. Figure 2 is a longitudinal cross-sectional view of the cleaning apparatus. Figure 3 is a diagram illustrating the camera's position. Figure 4 is a diagram illustrating an overview of the anomaly detection method. Figure 5 is a diagram illustrating an anomaly detection method according to the number of droplets. Figure 6 is a diagram illustrating an anomaly detection method according to the droplet area. Figure 7 is a diagram illustrating the highlighting of liquid movement. Figure 8 is a diagram illustrating the criteria for determining liquid splash risk. Figure 9 is a diagram illustrating an anomaly detection method using machine learning.

[0009] Various exemplary embodiments will be described in detail below with reference to the drawings. In each drawing, the same or corresponding parts will be denoted by the same reference numerals.

[0010] [Cleaning Apparatus] The cleaning apparatus 1 (apparatus for substrate processing) that performs the abnormality detection method according to this embodiment will be described with reference to the plan view in Figure 1 and the longitudinal cross-sectional side view in Figure 2. In the cleaning apparatus 1, cleaning is performed by simultaneously pressing two brushes against the back surface (bottom surface) of a wafer W, which is a circular substrate on which a resist film is formed on the surface (top surface), and sliding them relative to each other, thereby simultaneously processing different positions on the bottom surface of the wafer W. Thus, the cleaning apparatus 1 is a back surface cleaning apparatus that cleans the back surface of the wafer W. Note that the number of brushes is not limited to two, and may be one or three or more.

[0011] The wafer W, whose back surface has been cleaned in this manner, is transported to the exposure machine, where the resist film described above is exposed according to a predetermined pattern. If foreign matter adheres to the back surface of the wafer W during this exposure process, the wafer W will be placed so as to float above the stage of the exposure machine, causing the distance between the optical system of the exposure machine and the wafer W to deviate from the design value, resulting in defocusing. This problem is prevented by processing with the cleaning device 1.

[0012] The cleaning apparatus 1 comprises a base body 11, a spin chuck 12 (support part), a cup 3, and a cleaning processing unit 4. The base body 11 is formed in a rectangular shape in plan view, and a wafer W is transported to the cleaning apparatus 1 from one end of the base body 11 in the longitudinal direction by a transport mechanism (not shown) provided outside the cleaning apparatus 1. This end will be described as the front side. Also, left and right in the following description refer to left and right when viewed from rear to front. The base body 11 has a rectangular recess 13 with the front-to-back direction as its longitudinal direction, and the inside of this recess 13 is configured as a processing area for the wafer W. This front-to-back direction is the direction in which the center of the spin chuck 12 (= center of the wafer W that is held) and the pivot axis (central axis R1) that rotates the brush that cleans the wafer W are aligned.

[0013] A spin chuck 12 is provided on the front side of the processing area within the recess 13 described above. The spin chuck 12 is a circular stage that holds (supports) the wafer W horizontally by adsorbing the center of the lower surface of the wafer W. The lower side of the spin chuck 12 is connected to a rotation mechanism 15 via a shaft 14, and the rotation mechanism 15 rotates the spin chuck 12 in the circumferential direction so that the wafer W held by the spin chuck 12 rotates around a vertical axis, for example, clockwise in a plan view (top view). Three vertical support pins 16 (only two are shown in Figure 2) are arranged on the side of the spin chuck 12, spaced apart along the rotation direction of the spin chuck 12. The support pins 16 are raised and lowered by a lifting mechanism 17, allowing the wafer W to be transferred between the transport mechanism, the spin chuck 12, and the non-rotating chuck 35, which will be described later.

[0014] A cylindrical portion is provided extending upward from the bottom of the base body 11, surrounding the spin chuck 12, rotation mechanism 15, support pin 16, and lifting mechanism 17, and is configured as an air knife 18. The upper end surface of the air knife 18 forms an inclined surface that slopes inward. Discharge ports 19 for discharging air upward are provided on this inclined surface at intervals in the circumferential direction. When the back surface of the wafer W is adsorbed and held by the spin chuck 12, the upper end of the air knife 18 approaches the back surface of the wafer W, and air is discharged from the discharge ports 19 to prevent cleaning liquid from adhering to the center of the back surface of the wafer W.

[0015] A drain port 22 is provided at the bottom of the recess 13 of the base body 11. An upright exhaust pipe 23 is provided closer to the air knife 18 than the drain port 22 to exhaust the contents of the recess 13, and exhaust is performed from this exhaust pipe 23 during wafer processing. A flange 24 is provided that widens outward from the bottom of the air knife 18 and above the exhaust pipe 23, and the outer end of the flange 24 is bent downward on the outside of the exhaust pipe 23 to prevent waste liquid from flowing into the exhaust pipe 23.

[0016] Cup 3 is configured as a cylindrical body that surrounds the air knife 18 and has its upper end protruding inward. The side circumference of the wafer W being processed is surrounded by Cup 3, preventing the scattering of waste liquid. When the wafer W is held in the spin chuck 12 or the non-rotating chuck 35 described later, the wafer W is held concentrically with Cup 3. Support portions 31 extend from the left and right outer walls of Cup 3 toward the outer edge of the recess 13 and are connected to a horizontal movement mechanism 32 provided on the base body 11. This horizontal movement mechanism 32 allows Cup 3 to move back and forth within the recess 13. The front and rear sides of the movement range of Cup 3 are referred to as the front position and rear position, respectively. Figure 1 shows Cup 3 in the front position, where the center of Cup 3 in the front position coincides with the center of the spin chuck 12 in plan view. In the rear position, the center of Cup 3 is located behind the air knife 18.

[0017] The horizontal movement mechanism 32 is connected to the lifting mechanism 33 and can move up and down relative to the base body 11 together with the cup 3. This lifting mechanism 33 allows the cup 3 to move up and down between an upper position where the upper surface of the non-rotating chuck 35 (the support surface of the wafer W) is higher than the upper surface of the spin chuck 12 (the support surface of the wafer W) and a lower position where the upper surface of the non-rotating chuck 35 is lower than the upper surface of the spin chuck 12.

[0018] Inside the cup 3, two bridge sections 34 are formed, extending forward and backward while sandwiching the air knife 18 from the left and right. A non-rotating chuck 35 is provided on each bridge section 34. The non-rotating chuck 35 attracts the outer region of the center of the back surface of the wafer W, holding the wafer W horizontally. The non-rotating chuck 35 and the spin chuck each constitute a substrate holding section. When processing the center of the back surface of the wafer W, the wafer W is held in the non-rotating chuck 35, and when processing the peripheral edge of the back surface of the wafer W, it is held in the spin chuck 12, and processed by the brush.

[0019] A nozzle 36 is provided near the spin chuck 12 in the area enclosed by cup 3, and discharges, for example, pure water as a cleaning solution, diagonally upward and backward. By discharging the cleaning solution in this direction, when the wafer W is held in the non-rotating chuck 35 in the rear position, the cleaning solution is supplied to the center of the lower surface of the wafer W, and when the wafer W is held in the spin chuck 12 in the front position, the cleaning solution is supplied to the periphery of the lower surface of the wafer W. The brush 51, described later, slides over the area of ​​the lower surface (back side) of the wafer W to which the cleaning solution has been supplied in this manner. The nozzle 36 discharges the cleaning solution (processing solution) onto the wafer W.

[0020] The cleaning unit 4 includes a horizontal circular stage 41 provided within the recess 13. The stage 41 comprises a stage body 42 equipped with a lifting mechanism and rotating mechanisms 43 and 44. The circumferentially adjacent portions of the upper part of the stage 41 are configured as rotating mechanisms 43 and 44, and these rotating mechanisms 43 and 44 are each formed in a fan shape when viewed from above. The rotating mechanisms 43 and 44 can be raised and lowered independently of each other by the stage body 42.

[0021] The stage 41 described above is configured to be rotatable around its central axis R1. Therefore, the stage 41 rotates around the vertical axis, specifically the vertical axis. This central axis R1 is located behind the air knife 18 and near the periphery of the cup 3 in its forward position. The direction in which the central axis R1 and the center of the spin chuck 12 are aligned is aligned with the direction of forward and backward movement of the cup 3.

[0022] As described above, the stage 41 rotates, causing the positions of the rotating mechanisms 43 and 44 to shift. However, when both rotating mechanisms 43 and 44 are positioned towards the front, the right-side rotating mechanism is referred to as 43, and the left-side rotating mechanism as 44. Above these rotating mechanisms 43 and 44, horizontal circular brushes 51 are provided. These two brushes 51 are similarly constructed and made of an elastic material such as sponge or resin, with their upper end surfaces forming a sliding surface that is pressed against the lower surface of the wafer W. The lower side of each brush 51 is connected to the rotating mechanisms 43 and 44 via a shaft 52, and each brush 51 rotates around its central axis due to the rotation mechanisms 43 and 44. The central axis of the brush 51 extends in the vertical direction, more specifically in the vertical direction. With the cleaning processing unit 4 configured in this way, each brush 51 rotates around the central axis R1 of the stage 41 as its pivot axis. That is, it can move in a revolving manner around the central axis R1 as its axis of revolution. Furthermore, the brush 51 is configured to rotate on its own axis by the rotation mechanisms 43 and 44. In this embodiment, the rotation of the brushes 51A and 51B is performed counterclockwise in a plan view.

[0023] The cleaning apparatus 1 is equipped with a camera 70 (or camera 80), as shown in Figures 3(a) to 3(c). The camera 70 (or camera 80) continuously images an imaging area that includes at least the outer edge of the wafer W and the inner edge of the cup 3 surrounding the wafer W (the side adjacent to the wafer W). In the examples shown in Figures 3(a) and 3(b), the camera 70 is provided on the surface side (upper side) of the wafer W, and the camera 70 is imaging the inside of the cup 3 as the imaging area. By changing the tilt angle of the camera 70, imaging can be performed on the side closer to the camera 70 (Figure 3(a)) and on the side further away from the camera 70 (Figure 3(b)). In the example shown in Figure 3(c), a small camera 80 is provided on the back side (lower side) of the wafer W, and the camera 80 is imaging the side wall portion of the cup 3 as the imaging area. In either case, as described above, the camera 70 (or camera 80) continuously images an imaging area that includes at least the outer edge of the wafer W and the inner edge of the cup 3 surrounding the wafer W (the side adjacent to the wafer W). The camera 70 then transmits the imaging results to the control unit 10 (see Figure 1), which will be described later. In the following explanation, we will describe an example in which the control unit 10 acquires the imaging results from the camera 70.

[0024] As shown in Figure 1, the cleaning apparatus 1 is equipped with a control unit 10 configured by a computer, and the control unit 10 has a program. This program is configured with steps that output control signals to each part of the cleaning apparatus 1 so that it can perform a series of processing operations on the wafer W as described later, thereby controlling the operation of each part. The rotation of the spin chuck 12 by the rotation mechanism 15, the movement of the cup 3 by the horizontal movement mechanism 32, the raising and lowering of the support pin 16 by the lifting mechanism 17, the operation of each part constituting the cleaning processing unit 4, the discharge of air from the air knife 18, and the discharge of cleaning liquid from the nozzle 36 are all controlled. Specifically, the operation of each part of the cleaning processing unit 4 is, for example, the rotation, revolution and raising and lowering of the brush 51. The above program is stored in the control unit 10 in a storage medium such as a hard disk, compact disk, DVD, or memory card. The program is also a program that causes the cleaning apparatus 1 (device) to execute an abnormality detection method, which will be described later.

[0025] The control unit 10 is configured to acquire multiple images from the camera 70 in a time-series sequence of the imaging area, identify the behavior of the cleaning solution (processing solution) based on the multiple images, and detect the occurrence of an abnormality in the wafer W based on the behavior of the cleaning solution. Furthermore, the control unit 10 is configured to output an alert when an abnormality in the wafer W is detected. The details of the abnormality detection method, including the processing performed by the control unit 10, will be described below.

[0026] [Anomaly Detection Method] The anomaly detection method according to this embodiment is an anomaly detection method performed by the cleaning apparatus 1 (apparatus for substrate processing), which identifies the behavior of the cleaning liquid (processing liquid) based on a plurality of images continuously captured by the camera 70, and detects the occurrence of an anomaly in the wafer W based on said behavior. Here, detecting the occurrence of an anomaly includes not only detecting that an anomaly has (already) occurred, but also detecting that an anomaly will (in the future) occur.

[0027] The anomaly detection method includes discharging a cleaning solution from a nozzle 36 onto a wafer W, and continuously imaging an imaging region with a camera 70 that includes at least the outer edge of the wafer W and the inner edge of the cup 3 surrounding the wafer, thereby acquiring a plurality of imaging images in a time-series manner of the imaging region. Furthermore, the anomaly detection method includes the control unit 10 identifying the behavior of the cleaning solution based on the plurality of imaging images, detecting the occurrence of an anomaly in the wafer W based on the behavior of the cleaning solution, and outputting an alert by the control unit 10 when an anomaly in the wafer W is detected.

[0028] Figure 4 is a diagram illustrating the overview of the anomaly detection method. For the sake of explanation, only one of the two brushes 51A and 51B, brush 51A, is shown in Figure 4 (the same applies to Figures 5, 6, and 9 described later).

[0029] Figures 4(a), 4(b), and 4(c) show multiple images captured in a time series, taken sequentially. In the image shown in Figure 4(a), the brush 51A is in contact with the back surface of the wafer W to which the cleaning solution is supplied. The cleaning solution exists as multiple moving droplets DP. In the image shown in Figure 4(b), as the brush 51A slides, some of the droplets DP are scattered toward the cup 3. In the image shown in Figure 4(c), the number of scattered droplets DP increases, and the scattered droplets DP move closer to the cup 3. By superimposing the behavior of the cleaning solution (behavior of the droplets DP) in each of these images (each frame) onto a single frame, and showing the time-series positional changes of the same droplets DP with a continuous line, it is possible to display the trajectory TR of the droplets DP as shown in Figure 4(d). Furthermore, as shown in Figure 4(e), when displaying the behavior of the cleaning solution (behavior of droplets DP) in each image overlaid on a single frame, it is not necessarily required to show the trajectory TR, and multiple droplets DP themselves may be shown. In this way, by displaying the behavior of the cleaning solution in multiple images in chronological order overlaid on a single frame, the behavior of the cleaning solution becomes easier to see, and abnormalities in the wafer W caused by the behavior of the cleaning solution can be appropriately detected. Abnormalities in the wafer W here refer to, for example, watermarks or spherical defects on the wafer W caused by droplets DP reaching the cup 3 and then bouncing off the cup 3 and landing on the surface of the wafer W.

[0030] A specific example of anomaly detection will be explained. In the example shown in Figure 5(a), multiple droplets DP from captured images are displayed superimposed in a single frame. In such a case, the control unit 10 divides the imaging area into multiple regions (here, the upper left region A1, the upper right region A2, the lower left region A3, and the lower right region A4). The control unit 10 may detect an anomaly in the wafer W if the number of moving droplets DP detected in each of the divided regions A1, A2, A3, and A4 is equal to or greater than a predetermined droplet count threshold. In this case, the control unit 10 may set different values ​​for the droplet count threshold for each of the multiple regions. For example, for regions close to the cup 3 (such as the lower left region A3), the droplet count threshold may be set relatively lower because it is a region directly related to the bounce of droplets DP from the cup 3 (subsequent defect occurrence). As shown in Figure 5(b), the number of detected droplets DP is calculated for each region, and an alert is output if the number of detected droplets DP is equal to or greater than a predetermined droplet count threshold.

[0031] In the examples shown in Figures 6(a) and 6(b), the trajectories TR of droplets DP derived from multiple captured images are displayed in a single frame. Figure 6(b) shows a state later in time than Figure 6(a). In such a case, the control unit 10 may divide the imaging area into multiple regions and detect the occurrence of an abnormality in the wafer W if the cumulative area (pixel value) of the trajectories TR of the moving droplets DP detected in each of the divided regions A1, A2, A3, and A4 is greater than or equal to a predetermined area threshold. In this case, the control unit 10 may set different values ​​for the area threshold for each of the multiple regions. The multiple regions may include region Ax on the inner surface of the cup 3. The control unit 10 may detect the occurrence of an abnormality in the wafer W if the cumulative area of ​​the trajectories of the moving droplets DP detected in region Ax on the inner surface of the cup 3 is greater than or equal to the area threshold. The area Ax on the inner surface of cup 3 is directly related to the bounce of droplet DP in cup 3 (and subsequent defect formation), so the area threshold may be set relatively low.

[0032] When the control unit 10 displays the behavior of the cleaning solution (behavior of droplets DP) in each captured image superimposed on a single frame, it may highlight the cleaning solution based on multiple captured images. As described above, in order to detect the occurrence of an abnormality in the wafer W, it is important to determine whether or not droplets DP are likely to bounce back from the cup 3. Therefore, as shown in Figure 7(a), the movement of the cleaning solution toward the cup 3 (about to hit the cup 3) may be highlighted, or as shown in Figure 7(b), the movement of the cleaning solution bouncing off the cup 3 toward the wafer W may be highlighted.

[0033] As described above, the most important thing in detecting the occurrence of an abnormality in the wafer W is to determine whether or not droplets DP from the cup 3 are prone to bouncing back. For this reason, the control unit 10 may display the movement of the cleaning liquid bouncing back from the cup 3 toward the wafer W with more emphasis than the movement of the cleaning liquid moving toward the cup 3. Alternatively, the control unit 10 may detect the occurrence of an abnormality in the wafer W based on multiple captured images if the number of cleaning liquid droplets DP bouncing back from the cup 3 toward the wafer W is equal to or greater than a substrate droplet count threshold. Alternatively, the control unit 10 may identify the trajectory TR of the cleaning liquid droplets DP bouncing back from the cup 3 toward the wafer W based on multiple captured images, and detect the occurrence of an abnormality in the wafer W based only on this trajectory TR (bouncing trajectory only).

[0034] Figure 8 is a diagram illustrating the criteria for determining the risk of splashing (bounceback of droplets DP in cup 3). The control unit 10 may determine the risk of splashing and detect the occurrence of an abnormality based on the thickness of the highlighted cleaning liquid droplets DP (Figures 8(a) to 8(c)) or the trajectory of the droplets DP (Figures 8(d) to 8(f)). Regarding the thickness of the droplets DP, it may be determined that the risk of splashing is lowest when the droplets are thin (Figure 8(a)), and increases as they become standard (Figure 8(b)) and thicker (Figure 8(c)). Regarding the trajectory of the droplets DP, it may be determined that the risk of splashing is lowest when the liquid flow is weak (Figure 8(d)), and increases as they become standard (Figure 8(e)) and stronger (Figure 8(f)).

[0035] The control unit 10 identifies the trajectory TR of the cleaning liquid droplet DP that bounces off the cup 3 and heads toward the wafer W based on multiple captured images, and may detect the occurrence of an abnormality in the wafer W if the trajectory TR matches an NG trajectory that has been previously learned by machine learning. Figures 9(a) to 9(c) show the time change of the trajectory TR of the droplet DP. Machine learning is performed by associating such time changes in the trajectory TR with whether or not a defect occurred in the wafer W afterward. This makes it possible to detect the occurrence of an abnormality and stop the process or output an alert if the trajectory TR generation process follows the same path as past NG trajectories. That is, for example, as shown in Figure 9(d), when the control unit 10 detects droplet DP and trajectory TR at a relatively early stage, it determines whether or not the trajectory TR matches an NG trajectory that has been previously learned by machine learning, and if they match, it detects the occurrence of an abnormality in the wafer W.

[0036] Next, the effects and benefits of the anomaly detection method according to this embodiment will be described.

[0037] The abnormality detection method is an abnormality detection method performed by the cleaning apparatus 1. The abnormality detection method includes discharging cleaning liquid from the nozzle 36 onto the wafer W, and continuously imaging an imaging area that includes at least the outer edge of the wafer W and the inner edge of the cup 3 with the camera 70, and acquiring a plurality of imaging images in a time series along the imaging area. The abnormality detection method also includes identifying the behavior of the cleaning liquid based on the plurality of imaging images, detecting the occurrence of an abnormality in the wafer W based on the behavior of the cleaning liquid, and outputting an alert when an abnormality in the wafer W is detected.

[0038] The inventors focused on the fact that cleaning fluid that comes into contact with a sliding brush may reach the cup surrounding the wafer, and may even bounce off the cup and land on the surface of the wafer. Such droplets of processing fluid that land on the substrate may cause watermarks or spherical defects on the substrate. Therefore, the inventors conceived an anomaly detection method that continuously images the areas where bounce-off from the cup occurs (the outer edge of the wafer W and the inner edge of the cup 3) as the imaging area, and detects the occurrence of an anomaly in the wafer W from the behavior of the cleaning fluid identified from multiple captured images. With this anomaly detection method, the occurrence of an anomaly is detected from the time-series behavior of the processing fluid at the areas where bounce-off from the cup occurs (the outer edge of the wafer W and the inner edge of the cup 3), so the occurrence of watermarks and the like on the wafer W can be detected with high accuracy. As described above, the anomaly detection method according to this embodiment can appropriately detect the occurrence of anomalies in the wafer W caused by the processing fluid used in substrate cleaning.

[0039] The imaging area can be divided into multiple regions, and if the cumulative number of liquid droplets DP (moving objects) detected in each divided region exceeds a predetermined droplet count threshold, an abnormality in the wafer W may be detected. An increasing number of liquid droplets DP in each divided region of the imaging area indicates a state in which watermarks and other abnormalities are more likely to occur on the wafer W. Therefore, by detecting an abnormality in the wafer W when the cumulative number of liquid droplets DP exceeds a predetermined droplet count threshold, watermarks and other abnormalities on the wafer W can be detected with high accuracy.

[0040] The droplet count threshold may be set to a different value for each of the multiple regions. This makes it possible to relatively lower the droplet count threshold in regions that are particularly closely related to watermark generation on the wafer W (such as near the inner edge of cup 3), thereby enabling more timely and appropriate detection of watermark generation.

[0041] The imaging area may be divided into a plurality of areas, and when the cumulative area of the trajectory TR of the droplet DP, which is a moving object detected in each of the divided areas, is equal to or greater than a predetermined area threshold, an abnormality occurrence in the wafer W may be detected. A state where the area of the trajectory TR of the droplet DP is large in each area obtained by dividing the imaging area can be said to be a state where watermark generation or the like in the wafer W is likely to occur. Therefore, by detecting an abnormality occurrence in the wafer W when the cumulative area of the trajectory TR of the droplet DP is equal to or greater than a predetermined area threshold, watermark generation or the like in the wafer W can be detected with high accuracy.

[0042] The plurality of areas include the area of the inner surface of the cup 3, and when the cumulative area of the trajectory TR of the droplet DP, which is a moving object detected on the inner surface of the cup 3, is equal to or greater than the area threshold, an abnormality occurrence in the wafer W may be detected. Thereby, based on the area of the trajectory TR in an area having a particularly high correlation with watermark generation or the like in the wafer W, watermark generation or the like can be detected more appropriately.

[0043] The inside of the cup 3 may be imaged as an imaging area by a camera 70 provided on the front surface side of the wafer W. Thereby, the inside of the cup 3 can be appropriately imaged, and an abnormality can be appropriately detected.

[0044] Imaging may be performed by a camera 80 provided on the back surface side of the wafer W. For example, even when a camera cannot be provided on the front surface side of the wafer W due to structural reasons, by imaging the inner surface of the cup 3 or the like with the camera 80 on the back surface side of the wafer W, an abnormality can be appropriately detected.

[0045] Based on a plurality of captured images, the movement of the cleaning liquid toward the cup 3 may be highlighted. Thereby, the behavior of the cleaning liquid having a high correlation with watermark generation or the like can be appropriately displayed, and an abnormality can be appropriately detected.

[0046] Based on a plurality of captured images, the movement of the cleaning liquid rebounding toward the wafer W in the cup 3 may be highlighted. Thereby, the behavior of the cleaning liquid having a high correlation with watermark generation or the like can be appropriately displayed, and an abnormality can be appropriately detected.

[0047] Based on a plurality of captured images, the movement of the cleaning liquid toward the rebounding wafer W in the cup 3 may be highlighted more than the movement of the cleaning liquid toward the cup 3. Thereby, the behavior of the cleaning liquid directly leading to watermark generation or the like can be displayed with the highest priority, and abnormalities can be appropriately detected.

[0048] Based on a plurality of captured images, when the cumulative number of droplets DP of the cleaning liquid toward the rebounding wafer W in the cup 3 is equal to or greater than a predetermined substrate droplet number threshold, an abnormality occurrence in the wafer W may be detected. Thereby, abnormalities can be appropriately detected based on the behavior of the cleaning liquid directly leading to watermark generation or the like.

[0049] Based on a plurality of captured images, the trajectory TR of the droplets DP of the cleaning liquid toward the rebounding wafer W in the cup 3 is specified, and an abnormality occurrence in the wafer W may be detected based only on the trajectory TR. Thereby, abnormalities can be appropriately detected based only on the behavior of the cleaning liquid directly leading to watermark generation or the like.

[0050] Based on a plurality of captured images, the trajectory TR of the droplets DP of the cleaning liquid toward the rebounding wafer W in the cup 3 is specified, and when the trajectory TR matches an NG trajectory previously learned by machine learning, an abnormality occurrence in the wafer W may be detected. According to such a configuration, abnormalities can be detected with high accuracy and easily.

[0051] Finally, various exemplary embodiments included in the present disclosure are described in [E1] to [E16] below.

[0052] [E1] An abnormality detection method performed by an apparatus related to substrate processing, including: discharging a processing liquid from a nozzle onto a substrate; continuously imaging, by a camera, an imaging region including at least an outer edge portion of the substrate and an inner edge portion of a cup surrounding the substrate, and acquiring a plurality of captured images along a time series of the imaging region; specifying the behavior of the processing liquid based on the plurality of captured images, and detecting an abnormality occurrence in the substrate based on the behavior of the processing liquid; and outputting an alert when an abnormality occurrence in the substrate is detected.

[0053] [E2] The abnormality detection method according to [E1], wherein the imaging area is divided into a plurality of regions, and an abnormality in the substrate is detected when the cumulative number of liquid droplets, which are moving objects detected in each of the divided regions, is equal to or greater than a predetermined liquid droplet threshold.

[0054] [E3] The abnormality detection method according to [E2], wherein the droplet count threshold is set to a different value for each of the multiple regions.

[0055] [E4] An abnormality detection method according to any one of [E1] to [E3], wherein the imaging area is divided into a plurality of regions, and an abnormality in the substrate is detected when the cumulative area of ​​the trajectories of liquid droplets, which are moving objects detected in each divided region is greater than or equal to a predetermined area threshold.

[0056] [E5] The abnormality detection method according to [E4], wherein the plurality of regions include the region of the inner surface of the cup, and an abnormality in the substrate is detected when the cumulative area of ​​the trajectories of liquid droplets, which are moving objects detected on the inner surface of the cup, is greater than or equal to the area threshold.

[0057] [E6] An abnormality detection method according to any one of [E1] to [E5], wherein the camera provided on the surface side of the substrate is used to image the inside of the cup as the imaging area.

[0058] [E7] An abnormality detection method according to any one of [E1] to [E6], wherein imaging is performed using the camera provided on the back side of the substrate.

[0059] [E8] An abnormality detection method according to any one of [E1] to [E7], which highlights the movement of the processing liquid toward the cup based on the plurality of captured images.

[0060] [E9] An abnormality detection method according to any one of [E1] to [E8], which highlights the movement of the processing liquid that bounces back in the cup and heads toward the substrate based on the plurality of captured images.

[0061] [E10] An abnormality detection method according to any one of [E1] to [E9], which, based on the plurality of captured images, emphasizes the movement of the processing liquid that bounces off the cup and heads toward the substrate more than the movement of the processing liquid toward the cup.

[0062] [E11] An abnormality detection method according to any one of [E1] to [E10], wherein, based on the plurality of captured images, an abnormality in the substrate is detected when the accumulated number of droplets of the processing liquid that bounce off the cup and head toward the substrate is equal to or greater than a predetermined threshold for the number of droplets on the substrate.

[0063] [E12] An abnormality detection method according to any one of [E1] to [E11], wherein, based on the plurality of captured images, the trajectory of the liquid droplets of the processing liquid that bounce off the cup and head toward the substrate is identified, and an abnormality in the substrate is detected based solely on the trajectory.

[0064] [E13] An abnormality detection method according to any one of [E1] to [E12], wherein, based on the plurality of captured images, the trajectory of the liquid droplets of the processing liquid that bounce off the cup and head toward the substrate is identified, and an abnormality in the substrate is detected when the trajectory matches an NG trajectory that has been learned in advance by machine learning.

[0065] [E14] A device for processing substrates, comprising: a nozzle for discharging a processing liquid onto a substrate; a camera for continuously imaging an imaging region that includes at least the outer edge of the substrate and the inner edge of a cup surrounding the substrate; and a control unit, wherein the control unit is configured to acquire a plurality of images from the camera in a time series along the imaging region; identify the behavior of the processing liquid based on the plurality of images and detect the occurrence of an abnormality in the substrate based on the behavior of the processing liquid; and output an alert when an abnormality in the substrate is detected.

[0066] [E15] The apparatus according to [E14], further comprising a support portion for supporting the back surface of the substrate, and a brush that slides over the area of ​​the back surface of the substrate to which the processing liquid is supplied by the nozzle, wherein the apparatus functions as a back surface cleaning apparatus.

[0067] [E16] A program to cause the device to execute one of the anomaly detection methods described in any one of [E1] to [E13].

[0068] 1...Washing device (device), 3...Cup, 10...Control unit, 12...Spin chuck (support part), 36...Nozzle, 51A, 51B...Brush, 70, 80...Camera.

Claims

1. An anomaly detection method performed by a substrate processing apparatus, comprising: discharging a processing liquid from a nozzle onto a substrate; continuously imaging an imaging area using a camera that includes at least the outer edge of the substrate and the inner edge of a cup surrounding the substrate, and acquiring a plurality of images in a time series along the imaging area; identifying the behavior of the processing liquid based on the plurality of images, detecting the occurrence of an anomaly in the substrate based on the behavior of the processing liquid; and outputting an alert when an anomaly in the substrate is detected.

2. The abnormality detection method according to claim 1, wherein the imaging area is divided into a plurality of regions, and an abnormality in the substrate is detected when the cumulative number of liquid droplets, which are moving objects detected in each of the divided regions, is equal to or greater than a predetermined liquid droplet threshold.

3. The abnormality detection method according to claim 2, wherein the droplet count threshold is set to a different value for each of the multiple regions.

4. The abnormality detection method according to claim 1, wherein the imaging area is divided into a plurality of regions, and an abnormality in the substrate is detected when the cumulative area of ​​the trajectories of liquid droplets, which are moving objects detected in each of the divided regions is greater than or equal to a predetermined area threshold.

5. The abnormality detection method according to claim 4, wherein the plurality of regions include the region of the inner surface of the cup, and an abnormality in the substrate is detected when the cumulative area of ​​the trajectories of liquid droplets, which are moving objects detected on the inner surface of the cup, is greater than or equal to the area threshold.

6. The abnormality detection method according to claim 1, wherein the camera provided on the surface side of the substrate captures an image of the inside of the cup as the imaging area.

7. The abnormality detection method according to claim 1, wherein imaging is performed by the camera provided on the back side of the substrate.

8. The abnormality detection method according to claim 1, which highlights the movement of the processing liquid toward the cup based on the plurality of captured images.

9. An anomaly detection method according to claim 1, wherein, based on the plurality of captured images, the movement of the processing liquid that bounces off the cup and moves toward the substrate is highlighted.

10. An anomaly detection method according to claim 1, wherein, based on the plurality of captured images, the movement of the processing liquid that bounces off the cup and moves toward the substrate is emphasized more than the movement of the processing liquid toward the cup.

11. The abnormality detection method according to claim 1, wherein, based on the plurality of captured images, an abnormality in the substrate is detected when the accumulated number of droplets of the processing liquid that bounce off the cup and head toward the substrate is equal to or greater than a predetermined threshold for the number of droplets on the substrate.

12. The abnormality detection method according to claim 1, wherein, based on the plurality of captured images, the trajectory of the liquid droplets of the processing liquid that bounce off the cup and head toward the substrate is identified, and an abnormality in the substrate is detected based solely on the trajectory.

13. An anomaly detection method according to claim 1, wherein, based on the plurality of captured images, the trajectory of the liquid droplets of the processing liquid that bounce off the cup and head toward the substrate is identified, and an anomaly in the substrate is detected when the trajectory matches an NG trajectory that has been previously learned by machine learning.

14. A device for processing substrates, comprising: a nozzle for discharging a processing liquid onto a substrate; a camera for continuously imaging an imaging area that includes at least the outer edge of the substrate and the inner edge of a cup surrounding the substrate; and a control unit, wherein the control unit is configured to: acquire a plurality of images from the camera in a time series along the imaging area; identify the behavior of the processing liquid based on the plurality of images and detect the occurrence of an abnormality in the substrate based on the behavior of the processing liquid; and output an alert when an abnormality in the substrate is detected.

15. The apparatus according to claim 14, which further comprises a support portion for supporting the back surface of the substrate, and a brush that slides over the area of ​​the back surface of the substrate to which the processing liquid is supplied by the nozzle, and functions as a back surface cleaning apparatus.

16. A program for causing a device to execute the abnormality detection method described in any one of claims 1 to 13.

Images