DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12131454 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to train a classifier based on substrate images determining state of substrate based on the classifications.
Current Application
US Patent No. 12131454 B2
Claim 1. A method for classifying at least one state of at least one substrate in a substrate carrier, the method comprising:
detecting at least one substrate in the substrate carrier, the detecting including capturing one or more images with at least one camera of the at least one substrate in at least one location of the substrate carrier;
preprocess the one or more images to create preprocessed one or more images;
sending the preprocessed one or more images to a pre-trained deep-convolutional neural-network; and
classifying a state of the at least one substrate from the one or more images using the pre-trained deep-convolutional neural-network.
Claim 1. A method for classifying a state of a plurality of substrates for a plurality of locations in a substrate carrier, the method comprising:
detecting at least a portion of the plurality of substrates in the substrate carrier, the detecting including capturing one or more images of the portion of the plurality of substrates for the plurality of locations that is proximate to the portion of the plurality of substrates;
sending the one or more images to a pre-trained deep-convolutional neural-network;
classifying the state of the portion of the plurality of substrates for the plurality of locations within the substrate carrier from the one or more images using the pre-trained deep-convolutional neural-network; and
using the state of the portion of the plurality of substrates to set how a substrate of the plurality of substrates within the substrate carrier is handled.
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Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (CN 207398069 U) in view of Bagramyan et al. (EP 3623882 A1).
Regarding Claim 1,
Su discloses A method for classifying at least one state of at least one substrate in a substrate carrier, the method comprising: detecting at least one substrate in the substrate carrier, the detecting including capturing one or more images with at least one camera of the at least one substrate in at least one location of the substrate carrier (Su, Contents of unitality model, discloses according to the purpose of the utility model claims a wafer detecting device, applied to detecting states of a plurality of wafers in a wafer cleaning process. Wafer detecting device comprises a light emitting element, a light receiving element, and a processing device. the light emitting element is provided in the emission range of the light in the plurality of wafers, a light emitting element configured to emitting the light for a plurality of wafers. the light receiving element is set at one side of the light emitting element and the light receiving element is reflected from a plurality of wafers to receive light. processing device signal connected to the light receiving element, wherein the processing device comprises a processing unit and a judging unit, a processing unit configured to convert light received by the light receiving element into detecting light intensity signal and/or detecting the outline shape. a judging unit configured to detect the light intensity signal and/or detecting outline judging the state of a plurality of wafers; according to one embodiment of this utility model, the light emitting element and the light receiving element are respectively laser transmitting element with a laser receiving element; wafer cleaning machine further comprises a lifting device which is connected with wafer placing frame or wafer lifting device configured to drive the wafer frame or the wafer to change the wafer placed in the relative position of the frame and the wafer clamp arm; from the above, the wafer detecting device and wafer cleaning machine of the utility model is to use the light emitting element detecting the position and number of wafers in a wafer cleaning process is correct or not and the optical receiving component, if the wafer position is not correct. number is not correct or not correct such abnormal condition, can provide the operator immediately stops wafer cleaning technique is to avoid the influence of operation so as to avoid the subsequent cleaning process, due to position deviation of the wafer makes the wafer chipping and so on, and it can make sure the smooth wafer cleaning technique; according to the purpose of the utility model claims a wafer detecting device, applied to detecting states of a plurality of wafers in a wafer cleaning process. Wafer detecting device comprises a light emitting element, a light receiving element, and a processing device. the light emitting element is provided in the emission range of the light in the plurality of wafers, a light emitting element configured to emitting the light for a plurality of wafers. the light receiving element is set at one side of the light emitting element and the light receiving element is reflected from a plurality of wafers to receive light. processing device signal connected to the light receiving element, wherein the processing device comprises a processing unit and a judging unit, a processing unit configured to convert light received by the light receiving element into detecting light intensity signal and/or detecting the outline shape. a judging unit configured to detect the light intensity signal and/or detecting outline judging the state of a plurality of wafers. the common type wet-cleaning machine comprises clamping a wafer clamp and cleaning groove, the clamp can clamp the wafer and placing the wafer in the cleaning slot, after wafer cleaning, the wafer clamp after cleaning out of the groove; However, in the whole cleaning process, wafer holding or sometimes due to position shift or collision factors such as wafer position, wafer chipping or falling in the cleaning groove and so on. when the wafer is broken has occurred before entering the cleaning groove, lacking or position deflection, if forced into the cleaning tank to clean, may seriously affect the element clamp or operates in the cleaning tank, even it will cause debris scratch the wafer, causing the wafer yield is reduced; plurality of wafers (substrates) and their proximate locations are detected by its reflected lights and classified according to their state including chipped wafer or not)
Su does not explicitly disclose preprocess the one or more images to create preprocessed one or more images; sending the preprocessed one or more images to a pre-trained deep-convolutional neural-network; and classifying a state of the at least one substrate from the one or more images using the pre-trained deep-convolutional neural-network.
Bagramyan discloses images and preprocess the one or more images to create preprocessed one or more images; (Bagramyan, discloses the training of a neural network. For this specific training it is necessary to provide two different data sets. In a first step there is provided a first data set of images of plural workpieces, wherein the first data set comprises images from several points of view for each workpiece of the plural workpieces. For instance, if there are four different workpieces there should be provided a sufficient number of different images from each of the four workpieces. The images of one workpiece represent one type of workpiece. For example, images from different sides of the workpiece shall be provided. Images may be captured from a front view, a diagonal right view, a diagonal left view, a top view or other perspective views. These views typically characterize the 3D structure of the workpiece. According to this 3D structure the workpiece can be identified reliably; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; images are provided to neural network for training of features)
sending the preprocessed one or more images to a pre-trained deep-convolutional neural-network; and classifying a state of the at least one substrate from the one or more images using the pre-trained deep-convolutional neural-network. (Bagramyan, discloses the training of a neural network. For this specific training it is necessary to provide two different data sets. In a first step there is provided a first data set of images of plural workpieces, wherein the first data set comprises images from several points of view for each workpiece of the plural workpieces. For instance, if there are four different workpieces there should be provided a sufficient number of different images from each of the four workpieces. The images of one workpiece represent one type of workpiece. For example, images from different sides of the workpiece shall be provided. Images may be captured from a front view, a diagonal right view, a diagonal left view, a top view or other perspective views. These views typically characterize the 3D structure of the workpiece. According to this 3D structure the workpiece can be identified reliably; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; Finally, the type and alignment of the workpiece is identified with the trained neural network. The identification is based on the feature extraction and the classification performed by the different parts of the neural network. Due to the specific training of the classifying part, there is a high certainty that the actual type and actual orientation of the workpiece is determined accurately; plurality of image data sets are pretrained on their features using neural networks and used to classify images with different features)
Both Su and Bagramyan are directed to detecting classifying object signals or images. Su discloses the claimed invention except for the training neural network classifier using substrate images for classification. Bagramyan teaches that it is known to use the captured images of objects (substrates or wafer images) to train neural network and classify wafer or substrate types and group into specific categories for further adjustment of positions in proximities. It would have been obvious to one having ordinary skill in the art at the time the invention was made to use the training of neural network of images input as taught by Bagramyan in order to improve classification of objects into specific categories for further processing and optimization in fabrication or manufacturing processes position adjustments including substrates, circuit boards or semiconductor wafers.
Regarding Claim 2,
The combination of Su and Bagramyan further discloses training the neural network to classify a state of the substrate locations. (Su, Utility content model, discloses according to the purpose of the utility model claims a wafer detecting device, applied to detecting states of a plurality of wafers in a wafer cleaning process. Wafer detecting device comprises a light emitting element, a light receiving element, and a processing device. the light emitting element is provided in the emission range of the light in the plurality of wafers, a light emitting element configured to emitting the light for a plurality of wafers. the light receiving element is set at one side of the light emitting element and the light receiving element is reflected from a plurality of wafers to receive light. processing device signal connected to the light receiving element, wherein the processing device comprises a processing unit and a judging unit, a processing unit configured to convert light received by the light receiving element into detecting light intensity signal and/or detecting the outline shape. a judging unit configured to detect the light intensity signal and/or detecting outline judging the state of a plurality of wafers. the common type wet-cleaning machine comprises clamping a wafer clamp and cleaning groove, the clamp can clamp the wafer and placing the wafer in the cleaning slot, after wafer cleaning, the wafer clamp after cleaning out of the groove; However, in the whole cleaning process, wafer holding or sometimes due to position shift or collision factors such as wafer position, wafer chipping or falling in the cleaning groove and so on. when the wafer is broken has occurred before entering the cleaning groove, lacking or position deflection, if forced into the cleaning tank to clean, may seriously affect the element clamp or operates in the cleaning tank, even it will cause debris scratch the wafer, causing the wafer yield is reduced; plurality of wafers (substrates) and their proximate locations are detected by its reflected lights and classified according to their state including chipped wafer or not). (Bagramyan, discloses the training of a neural network. For this specific training it is necessary to provide two different data sets. In a first step there is provided a first data set of images of plural workpieces, wherein the first data set comprises images from several points of view for each workpiece of the plural workpieces. For instance, if there are four different workpieces there should be provided a sufficient number of different images from each of the four workpieces. The images of one workpiece represent one type of workpiece. For example, images from different sides of the workpiece shall be provided. Images may be captured from a front view, a diagonal right view, a diagonal left view, a top view or other perspective views. These views typically characterize the 3D structure of the workpiece. According to this 3D structure the workpiece can be identified reliably; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; Finally, the type and alignment of the workpiece is identified with the trained neural network. The identification is based on the feature extraction and the classification performed by the different parts of the neural network. Due to the specific training of the classifying part there is a high certainty that the actual type and actual orientation of the workpiece is determined accurately; plurality of image data sets are pretrained on their features using neural networks and used to classify images with different features). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 3,
The combination of Su and Bagramyan further discloses wherein the automatic tagging includes classifying each of the plurality of locations as at least one classification type selected from types including properly loaded, cross-slotted, double-loaded, protruded, and empty. (Su, Fig. 1, discloses in the whole cleaning process, wafer holding or sometimes due to position shift or collision factors such as wafer position, wafer chipping or falling in the cleaning groove and so on. when the wafer is broken has occurred before entering the cleaning groove, lacking or position deflection, if forced into the cleaning tank to clean, may seriously affect the element clamp or operates in the cleaning tank, even it will cause debris scratch the wafer, causing the wafer yield is reduced; slot is filled with wafer chip is or not is determined). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 5,
The combination of Su and Bagramyan further discloses wherein the classifying further comprises identifying a plurality of substrate types within the substrate carrier from the portion of the plurality of substrates. (Su, Utility content model, discloses according to the purpose of the utility model claims a wafer detecting device, applied to detecting states of a plurality of wafers in a wafer cleaning process. Wafer detecting device comprises a light emitting element, a light receiving element, and a processing device. the light emitting element is provided in the emission range of the light in the plurality of wafers, a light emitting element configured to emitting the light for a plurality of wafers. the light receiving element is set at one side of the light emitting element and the light receiving element is reflected from a plurality of wafers to receive light. processing device signal connected to the light receiving element, wherein the processing device comprises a processing unit and a judging unit, a processing unit configured to convert light received by the light receiving element into detecting light intensity signal and/or detecting the outline shape. a judging unit configured to detect the light intensity signal and/or detecting outline judging the state of a plurality of wafers. the common type wet-cleaning machine comprises clamping a wafer clamp and cleaning groove, the clamp can clamp the wafer and placing the wafer in the cleaning slot, after wafer cleaning, the wafer clamp after cleaning out of the groove; However, in the whole cleaning process, wafer holding or sometimes due to position shift or collision factors such as wafer position, wafer chipping or falling in the cleaning groove and so on. when the wafer is broken has occurred before entering the cleaning groove, lacking or position deflection, if forced into the cleaning tank to clean, may seriously affect the element clamp or operates in the cleaning tank, even it will cause debris scratch the wafer, causing the wafer yield is reduced; plurality of wafers (substrates) and their proximate locations are detected by its reflected lights and classified according to their state including chipped wafer or not). (Bagramyan, discloses the training of a neural network. For this specific training it is necessary to provide two different data sets. In a first step there is provided a first data set of images of plural workpieces, wherein the first data set comprises images from several points of view for each workpiece of the plural workpieces. For instance, if there are four different workpieces there should be provided a sufficient number of different images from each of the four workpieces. The images of one workpiece represent one type of workpiece. For example, images from different sides of the workpiece shall be provided. Images may be captured from a front view, a diagonal right view, a diagonal left view, a top view or other perspective views. These views typically characterize the 3D structure of the workpiece. According to this 3D structure the workpiece can be identified reliably; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; Finally, the type and alignment of the workpiece is identified with the trained neural network. The identification is based on the feature extraction and the classification performed by the different parts of the neural network. Due to the specific training of the classifying part there is a high certainty that the actual type and actual orientation of the workpiece is determined accurately; plurality of image data sets are pretrained on their features using neural networks and used to classify images with different features). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 6,
The combination of Su and Bagramyan further discloses selecting a wavelength of a light source used in capturing the one or more images based on at least one of a reflection characteristic of the substrates within the substrate carrier, a transmission characteristic of the substrates within the substrate carrier, and a film or coating on the substrates. (Bagramyan, discloses the light emitting element 410 and the light receiving element 420 can also be set at other appropriate places, as long as it can scan the wafer arrangement position and number. In addition, when the light emitting element 410 and light receiving element 420 are respectively laser emitting element and a laser receiving element, a light emitting element 410 and light receiving element 420 can be set at the groove body 200 far place, or can be covered with a layer of glass protecting cover the light emitting element 410 and the light receiving element 420 of the surface, thus it can avoid chemical substances of the groove erosion near 200 light emitting element 410 and the light receiving element 420;). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 7,
The combination of Su and Bagramyan further discloses selecting an angle-of-incidence, a polarization state, and an intensity of radiation of the light source. (Bagramyan, a second step S2 there is provided a second data set of images comprising images for each workpiece from several pregiven alignment angles. Whereas the images of the first data set serve to learn the type of the workpiece, the images of the second data set are used for learning the alignment of the workpiece. Therefore, the respective angle of the point of view from which the respective image is taken is very decisive for the images of the second data set. In contrast to that the angles of the points of view of the images of the first data set are not decisive for the alignment of the workpiece. Again, the second data set may be provided by an internal or external storage device with respect to the manufacturing device. Specifically, also the images of the second data set may be provided via a data network connected to the manufacturing device; incident angle to capture various orientation views of objects is disclosed). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 8,
The combination of Su and Bagramyan further discloses scaling each of the one or more images into a trained convnet size. (Bagramyan, discloses the feature map 18 of the feature extractor 13 is input into a fully-connected layer classifier 19 added on top of the feature extractor 13. Both the feature extractor 13 and the fully-connected layer classifier 19 form the complete neural network. In the present case the classifier 19 consists of the following layers: a flatten layer 20, a dense layer 21 (dimension 256) with ReLu (rectified linear unit function) activation, dropout layer 22 (dropout value 0,5) and dense layer 23 (dimension 4) with Softmax activation. The output of the neural network 13, 19 is a classification vector (i.e. a set of classification values); during training all the weights of the feature extractor 13 are frozen (they are not being updated) and only the weights of the fully-connected layer classifier are optimized using e.g. SGD (stochastic gradient descent) with a learning weight of 1e-4 and a momentum of 0,9; Fig 4 the deep learning model architecture replicates the architecture of a MobileNetv2 neural network up to the last convolutional layer. This part may also be called feature extractor 13. It includes a respective number of blocks 24 and at the end a 1x1 convolutional layer 25. The image 15 input to the feature extractor 13 is also processed to a feature map 18. Again on top of the feature extractor 13 a (fully-connected layer) classifier 19 is added for processing the feature map 18. The classifier 19 may include the following layers: a global average pooling layer 26 (2D), a (1x1) convolutional layer 27 (or fully-connected) and a dense layer 28 (dimension N for workpiece identification and four for coarse rotation) with SoftMax activation. The output of the classifier 19 will be a classification vector 29 similar to the embodiment of FIG 3. The classification vector 29 includes probability values for each class). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Claim 9 recite system with elements corresponding to the method steps recited in Claim 1. Therefore, the recited elements of the system Claim 9 are mapped to the proposed combination in the same manner as the corresponding steps of Claim 1. Additionally, the rationale and motivation to combine the Su and Bagramyan references presented in rejection of Claim 1, apply to this claim.
Furthermore, the combination of Su and Bagramyan further discloses A substrate-mapping system, comprising: a camera to collect one or more images of substrates and potential locations of substrates in a substrate carrier, the one or more images including a relationship of the substrates and locations of the substrates relative to a plurality of substrate slots in the substrate carrier; and a data-collection and control system, including one or more hardware-based processors of a machine coupled to the camera, the data-collection and control system. (Bagramyan, Additionally, there may be provided a computer program product comprising computer readable means, on which a computer program is stored, wherein the computer program, when executed on a processor, causes the processor with the neural network to carry out a method as described above. Specifically, the processor may be implemented on the manufacturing device and the processor causes the manufacturing device to carry out the method).
Regarding Claim 10,
The combination of Su and Bagramyan further discloses a light source to illuminate at least some of the substrates and the potential locations of the substrates in the substrate carrier. (Su, discloses according to the purpose of the utility model claims a wafer detecting device, applied to detecting states of a plurality of wafers in a wafer cleaning process. Wafer detecting device comprises a light emitting element, a light receiving element, and a processing device. the light emitting element is provided in the emission range of the light in the plurality of wafers, a light emitting element configured to emitting the light for a plurality of wafers. the light receiving element is set at one side wave of the light emitting element and the light receiving element is reflected from a plurality of wafers to receive light. processing device signal connected to the light receiving element, wherein the processing device comprises a processing unit and a judging unit, a processing unit configured to convert light received by the light receiving element into detecting light intensity signal and/or detecting the outline shape. a judging unit configured to detect the light intensity signal and/or detecting outline judging the state of a plurality of wafers; wafers (substrates) at different locations are illuminated with light source). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 11,
The combination of Su and Bagramyan further discloses wherein the light source is a broadband source. (Su, Fig.1-2, discloses the processing device 430 signal is connected with the light receiving element 420. processing device 430 mainly comprises a processing unit 431, a determining unit 432 and a storage unit 433. storage unit 433 configured to store the initial light intensity signal and the initial outline. initial light intensity signal and the initial outline is The wafer arrangement manner and number accords with demand of workers constructed signal waveform or profile. Referring to Fig. 3A, which is shown according to established by the wafer meets the demand of initial outline diagram, shown in Figure 3A, wafer is tidily arranged on the wafer 300 or the wafer placing frame 210 under the normal condition. to long;, wafer number Fig. 3 A shown is 5 is only for exemplary explanation, and not for limiting this utility model; processing unit 431 configured to convert the light received by the light receiving element 420 into detecting light intensity signal and/or detecting the outline shape. detecting the light intensity signal herein refers to the waveform of the intensity of light refers to light emitting element 410 transmits light to the wafer W and the wafer W is reflected to the light receiving element 420. detecting outline arranged refers to wafer to wafer arm 300 or the wafer is placed on the frame 210 outline (such as Fig. 3 B to FIG. 3 D, but not is limited to this); different intensity lights are projected onto the wafer substrates to detect and substrates and classify). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 12,
The combination of Su and Bagramyan further discloses wherein the light source is a monochromatic source. (Su, Fig.1-2, discloses the processing device 430 signal is connected with the light receiving element 420. processing device 430 mainly comprises a processing unit 431, a determining unit 432 and a storage unit 433. storage unit 433 configured to store the initial light intensity signal and the initial outline. initial light intensity signal and the initial outline is wafer arrangement manner and number accords with demand of workers constructed signal waveform or profile. Referring to Fig. 3A, which is shown according to established by the wafer meets the demand of initial outline diagram, shown in Figure 3A, wafer is tidily arranged on the wafer 300 or the wafer placing frame 210 under the normal condition. to long; wafer number Fig. 3 A shown is 5 is only for exemplary explanation, and not for limiting this utility model. processing unit 431 configured to convert the light received by the light receiving element 420 into detecting light intensity signal and/or detecting the outline shape. detecting the light intensity signal herein refers to the waveform of the intensity of light refers to light emitting element 410 transmits light to the wafer W and the wafer W is reflected to the light receiving element 420. detecting outline arranged refers to wafer to wafer arm 300 or the wafer is placed on the frame 210 outline (such as Fig. 3 B to Fig. 3 D, but not is limited to this); different intensity lights are projected onto the wafer substrates to detect and substrates and classify). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 13,
The combination of Su and Bagramyan further discloses wherein a wavelength of light emitted from the light source is selected based on at least one of a reflection characteristic of the substrates within the substrate carrier, a transmission characteristic of the substrates within the substrate carrier, and a film or coating on the substrates. (Su, Fig.1-2, discloses the processing device 430 signal is connected with the light receiving element 420. processing device 430 mainly comprises a processing unit 431, a determining unit 432 and a storage unit 433. storage unit 433 configured to store the initial light intensity signal and the initial outline. initial light intensity signal and the initial outline is The wafer arrangement manner and number accords with demand of workers constructed signal waveform or profile. Referring to FIG. 3A, which is shown according to established by the wafer meets the demand of initial outline diagram, shown in Figure 3A, wafer is tidily arranged on the wafer 300 or the wafer placing frame 210 under the normal condition. to long;, wafer number FIG. 3 A shown is 5 is only for exemplary explanation, and not for limiting this utility model.
processing unit 431 configured to convert the light received by the light receiving element 420 into detecting light intensity signal and/or detecting the outline shape. detecting the light intensity signal herein refers to the waveform of the intensity of light refers to light emitting element 410 transmits light to the wafer W and the wafer W is reflected to the light receiving element 420. detecting outline arranged refers to wafer to wafer arm 300 or the wafer is placed on the frame 210 outline (such as FIG. 3 B to FIG. 3 D, but not is limited to this); different intensity lights are projected onto the wafer substrates to detect and substrates and classify). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 14,
The combination of Su and Bagramyan further discloses wherein one or more characteristics of the light source include selection of an angle-of- incidence, a polarization state, and an intensity of radiation. (Bagramyan, discloses there is provided a second data set of images comprising images for each workpiece from several pregiven alignment angles. This second data set is necessary to identify the alignment of the workpiece. If, for example, the workpiece could be orientated in four orthogonal directions, it is reasonable to take images from these four different orientations. Otherwise, if the workpiece is a hexagonal prism it may be necessary to take six different images for each different orientation of the prism. Thus, a proper number of images shall be taken from the workpiece to guarantee the correct identification of the alignment of the workpiece; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; images are captured from various angles to include orientations from different view points to train neural networks). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 15,
The combination of Su and Bagramyan further discloses wherein the automatic tagging includes classifying each of the plurality of locations as at least one classification type selected from types including properly loaded, cross- slotted, double-loaded, protruded, and empty. (Su, Fig. 1, discloses in the whole cleaning process, wafer holding or sometimes due to position shift or collision factors such as wafer position, wafer chipping or falling in the cleaning groove and so on. when the wafer is broken has occurred before entering the cleaning groove, lacking or position deflection, if forced into the cleaning tank to clean, may seriously affect the element clamp or operates in the cleaning tank, even it will cause debris scratch the wafer, causing the wafer yield is reduced; slot is filled with wafer chip is or not is determined). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 17,
The combination of Su and Bagramyan further discloses characterizing a size of the substrate carrier. (Su, Fig. 1, discloses the determining unit 432 uses the image comparison mode, to compare the initial outline (such as shown in FIG. 3 A) and the sensing profile shape (e.g., FIG. 3 B to FIG. 3 D). For example, FIG. 3 B for detecting the outline indicated in part A1 3 A shown in the initial outline thickness and appears from detecting outline B in FIG. 3 right most situation of the wafer sheet, representative A1 wafer portion B indicated in FIG. 3 lamination occurs. For another example, the height of the portion A2 shown in FIG. 3 C for detecting outline indicated with FIG. 3 A low, which represents a condition of wafer chipping or unfilled corner appears portion A2 of wafer C indicated in FIG. 3. and the pattern pitch of part A3 3 D for detecting outline indicated with FIG. 3 A, which indicates the wafer in portion D indicated in FIG. 3 A3 wafer the skew occurs; wafer (substrate) thickness and size of carrier is disclosed). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 18,
The combination of Su and Bagramyan further discloses wherein the system is initially trained in a training mode based on the one or more images to produce a pre-trained deep-convolutional neural-network, subsequent to substrate- mapping system being configured to be used in a in a normal-operation mode within a fabrication facility to detect a placement of substrates within substrate carriers within the fabrication facility based on the pre- trained deep-convolutional neural-network. (Bagramyan, discloses the training of a neural network. For this specific training it is necessary to provide two different data sets. In a first step there is provided a first data set of images of plural workpieces, wherein the first data set comprises images from several points of view for each workpiece of the plural workpieces. For instance, if there are four different workpieces there should be provided a sufficient number of different images from each of the four workpieces. The images of one workpiece represent one type of workpiece. For example, images from different sides of the workpiece shall be provided. Images may be captured from a front view, a diagonal right view, a diagonal left view, a top view or other perspective views. These views typically characterize the 3D structure of the workpiece. According to this 3D structure the workpiece can be identified reliably; Furthermore, there is provided a neural network having a feature extraction part and a classifying part. Thus, the inventive method does not employ a single standard neural network but a modified neural network having an extraction part and on top a classifying part. The feature extraction part serves for extracting features from the images in a usual manner. The classifying part is used to perform a classification so that a present image or a situation can be classified in accordance with the type and alignment of the workpiece. Only the classifying part of the neural network is trained with the first and second data set. The first and second data sets are not used to train the feature extraction part of the neural network. The weights of the feature extraction part are rather frozen when the classifying part is trained. However, the feature extraction part can be pretrained with other specific data, whereas the classifying part is trained on the actually used workpieces and orientations; Finally, the type and alignment of the workpiece is identified with the trained neural network. The identification is based on the feature extraction and the classification performed by the different parts of the neural network. Due to the specific training of the classifying part there is a high certainty that the actual type and actual orientation of the workpiece is determined accurately; plurality of image data sets are pretrained on their features using neural networks and used to classify images with different features and trained neural network classifier is applied to current incoming images of workpieces (substrates)). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Regarding Claim 19,
The combination of Su and Bagramyan further discloses wherein the data-collection and control system is further configured to scale each of the one or more images into a trained convnet size. (Bagramyan, discloses the feature map 18 of the feature extractor 13 is input into a fully-connected layer classifier 19 added on top of the feature extractor 13. Both the feature extractor 13 and the fully-connected layer classifier 19 form the complete neural network. In the present case the classifier 19 consists of the following layers: a flatten layer 20, a dense layer 21 (dimension 256) with ReLu (rectified linear unit function) activation, dropout layer 22 (dropout value 0,5) and dense layer 23 (dimension 4) with Softmax activation. The output of the neural network 13, 19 is a classification vector (i.e. a set of classification values); during training all the weights of the feature extractor 13 are frozen (they are not being updated) and only the weights of the fully-connected layer classifier are optimized using e.g. SGD (stochastic gradient descent) with a learning weight of 1e-4 and a momentum of 0,9; Fig 4 the deep learning model architecture replicates the architecture of a MobileNetv2 neural network up to the last convolutional layer. This part may also be called feature extractor 13. It includes a respective number of blocks 24 and at the end a 1x1 convolutional layer 25. The image 15 input to the feature extractor 13 is also processed to a feature map 18. Again, on top of the feature extractor 13 a (fully-connected layer) classifier 19 is added for processing the feature map 18. The classifier 19 may include the following layers: a global average pooling layer 26 (2D), a (1x1) convolutional layer 27 (or fully-connected) and a dense layer 28 (dimension N for workpiece identification and four for coarse rotation) with Softmax activation. The output of the classifier 19 will be a classification vector 29 similar to the embodiment of Fig. 3. The classification vector 29 includes probability values for each class). Additionally, the rational and motivation to combine the references Su and Bagramyan as applied in rejection of claim 1 apply to this claim.
Claim 20 recite computer readable medium with program instructions corresponding to the method steps recited in Claim 1. Therefore, the recited program instructions of the computer readable medium Claim 20 are mapped to the proposed combination in the same manner as the corresponding steps of Claim 1. Additionally, the rationale and motivation to combine the Su and Bagramyan references presented in rejection of Claim 1, apply to this claim.
Furthermore, the combination of Su and Bagramyan further discloses A computer-readable medium containing instructions that, when executed by a machine, cause the machine to perform operations (Bagramyan, Additionally, there may be provided a computer program product comprising computer readable means, on which a computer program is stored, wherein the computer program, when executed on a processor, causes the processor with the neural network to carry out a method as described above. Specifically, the processor may be implemented on the manufacturing device and the processor causes the manufacturing device to carry out the method).
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. nonstatutory double patenting, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 4 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
US-10867108-B2 (Chao et al., applicant's disclosure: According to some embodiments, the present disclosure provides a method for determining wafer inspection parameters. The method includes identifying an area of interest in an IC design layout, performing an inspection simulation on the area of interest by generating a plurality of simulated optical images from the area of interest using a plurality of optical modes, and selecting, based on the simulated optical images, at least one of the optical modes to use for inspecting an area of a wafer that is fabricated based on the area of interest in the IC design layout, Abstract)
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/Pinalben Patel/Examiner, Art Unit 2671
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