Inspection processing apparatus and method for manufacturing semiconductor device
By designing an inspection and processing device including a storage unit and a processing unit, and using the characteristic quantity distribution area for analysis and comparison, the problem of low inspection efficiency in semiconductor device manufacturing is solved, and an efficient and high-precision inspection effect is achieved.
Patent Information
- Application Number
- CN202480003864.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2023-08-31
- Filing Date
- 2024-02-07
- Publication Date
- 2025-05-02
AI Technical Summary
The prior art has low inspection efficiency during the manufacturing process of semiconductor devices, and it is difficult to effectively improve inspection efficiency.
An inspection and processing device is designed, including a storage unit and a processing unit. The storage unit stores the first feature amount distribution area and the second feature amount distribution area related to the target device. The processing unit performs the current first check based on these feature quantity distribution areas, and uses past inspection results to analyze and compare the feature quantity distribution.
Through this method, inspection efficiency can be significantly improved, high-precision first inspection can be achieved, and crystal defects and other problems in semiconductor devices can be effectively identified.
Smart Images
Figure CN119923715A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to an inspection and processing apparatus and a method for manufacturing a semiconductor device. Background Art
[0002] For example, various inspections are performed during the manufacture of target devices such as semiconductor devices, and it is desired to improve the efficiency of the inspections.
[0003] Prior art literature
[0004] Patent Literature
[0005] Patent Document 1: Japanese Patent Application No. 2019-525450 Summary of the invention
[0006] Problems to be solved by the invention
[0007] Embodiments provide an inspection processing apparatus and a method for manufacturing a semiconductor device capable of improving efficiency.
[0008] Means used to solve problems
[0009] According to an embodiment, an inspection processing device includes a storage unit and a processing unit. The storage unit is capable of storing a feature quantity distribution including a first feature quantity distribution area related to an object device and a second feature quantity distribution area related to the object device. The processing unit is capable of implementing a current first inspection related to the object device based on the first feature quantity distribution area and the second feature quantity distribution area stored in the storage unit. The feature quantity distribution relates to a first past inspection result and a second past inspection result of the first inspection in the past related to the object device. The second past inspection result is obtained by a past second inspection of the object device after the first inspection in the past. The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 This is a flowchart illustrating the operation of the inspection processing device according to the first embodiment.
[0011] Figure 2 This is a block diagram illustrating the inspection processing device according to the first embodiment.
[0012] Figure 3 This is a schematic diagram illustrating characteristics used in the operation of the inspection processing device according to the first embodiment.
[0013] Figure 4 This is a schematic diagram illustrating characteristics used in the operation of the inspection processing device according to the first embodiment.
[0014] Figure 5 This is a schematic diagram illustrating the results of the operation of the inspection processing device according to the first embodiment.
[0015] Figure 6 This is a schematic diagram illustrating the results of the operation of the inspection processing device according to the first embodiment.
[0016] Figure 7 This is a flowchart illustrating the operation of the inspection processing device according to the first embodiment. DETAILED DESCRIPTION
[0017] Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0018] In the present specification and each drawing, regarding the drawings that have already appeared, the same reference numerals are given to the same elements as those described above, and detailed description thereof will be appropriately omitted.
[0019] (First Embodiment)
[0020] Figure 1 This is a flowchart illustrating the operation of the inspection processing device according to the first embodiment.
[0021] Figure 2 This is a block diagram illustrating the inspection processing device according to the first embodiment.
[0022] Figure 3 and Figure 4 This is a schematic diagram illustrating characteristics used in the operation of the inspection processing device according to the first embodiment.
[0023] like Figure 2 As shown, the inspection processing device 110 of the embodiment includes a storage unit 76m and a processing unit 76p. The processing unit 76p may include, for example, a computing device (such as a computer). The storage unit 76m may include any memory (including a hard disk). The inspection processing device 110 may include an interface 76f, a display 76d, and an input device 76i. The input device 76i may include, for example, at least one of a keyboard, a touch panel, and a mouse.
[0024] In the inspection processing device 110, the storage unit 76m can store a feature quantity distribution related to the inspection target device. The target device includes, for example, various electronic devices, etc. In one example, the target device may include a semiconductor device.
[0025] The feature quantity distribution includes multiple feature quantity areas related to the object device. In one example, the feature quantity distribution includes areas of multiple dimensions. The areas of multiple dimensions are, for example, divided into multiple partial areas. For example, the feature quantity distribution includes a first feature quantity distribution area related to the object device and a second feature quantity distribution area related to the object device.
[0026] Figure 4 The feature quantity distribution FD is illustrated. In this example, the feature quantity distribution FD is two-dimensional. In this example, the feature quantity distribution FD is represented in two dimensions including the first feature quantity F1 and the second feature quantity F2. The feature quantity distribution FD is represented by the first feature quantity F1 and the second feature quantity F2.
[0027] like Figure 4 As shown, the first feature quantity distribution region FR1 is one region in two dimensions. The second feature quantity distribution region FR2 is another region in two dimensions. In this way, the feature quantity distribution FD is divided into a plurality of regions by a plurality of feature quantities.
[0028] The processing unit 76p can perform a first inspection (current first inspection) on a target device (eg, a semiconductor device) based on the first feature quantity distribution area FR1 and the second feature quantity distribution area FR2 stored in the storage unit 76m.
[0029] In one example, when the object device is a semiconductor device, the above-mentioned "current first inspection" is an inspection of crystal defects of the semiconductor contained in the semiconductor device. For example, an image (microscope image, etc.) representing the crystal defect is acquired. For example, a feature quantity related to the shape of the crystal defect, etc. can be derived from the image representing the crystal defect. Based on the derived feature quantity, a first inspection related to the object device (such as a semiconductor device, etc.) is implemented. In one example, the feature quantity can be defined by a vector of multiple dimensions, etc. (such as a 128-dimensional value group), etc.
[0030] The feature quantity distribution FD relates to a first past inspection result and a second past inspection result of a past first inspection related to an object device (e.g., a semiconductor device, etc.). The second past inspection result is obtained by a past second inspection of the object device after a past first process (post-process) performed after the past first inspection. For example, "past first inspection", "past first process", and "past second inspection" are performed in sequence.
[0031] In one example, the "first inspection in the past" is an inspection of crystal defects of a wafer of a semiconductor device. In one example, in the "first processing in the past", various processing is performed on the wafer after the first inspection. Various processing includes, for example, the formation of a semiconductor layer, patterning, and the formation of electrodes. Through the "first processing in the past", a semiconductor device is manufactured from the wafer. After that, the "second inspection in the past" is performed. In the "second inspection in the past", for example, an inspection of the electrical characteristics of the semiconductor device is performed. In the "second inspection in the past", an appearance inspection of the semiconductor device may also be performed.
[0032] Sometimes the result of the "second past inspection" is related to the result of the "first past inspection". For example, the second past inspection result is related to the first past inspection result. Such first past inspection result and second past inspection result are stored in the storage unit 76m.
[0033] Figure 3 The feature quantity distribution FD is illustrated as an example. The feature quantity distribution FD is based on the results of “the first inspection in the past” and “the second inspection in the past” regarding the target device (for example, a semiconductor device). Figure 3 The horizontal axis is the first feature quantity F1. Figure 3 The vertical axis is the second feature quantity F2. The first feature quantity F1 and the second feature quantity F2 are based on the first past inspection result and the second past inspection result. In this example, the "first past inspection" is an inspection of crystal defects in semiconductor devices. The "second past inspection" is an inspection of electrical characteristics in semiconductor devices.
[0034] Figure 3 The illustrated black circle RS1 and white circle RS2 represent the first characteristic quantity F1 and the second characteristic quantity F2 of the unqualified result (e.g., crystal defect) in the "first inspection in the past". The black circle RS1 corresponds to the unqualified result (crystal defect) in the "first inspection in the past" and the unqualified result (abnormal electrical characteristics) in the "second inspection in the past". The white circle RS2 corresponds to the unqualified result (crystal defect) in the "first inspection in the past" but the qualified result (normal electrical characteristics) in the "second inspection in the past".
[0035] like Figure 3 As shown, in the feature quantity distribution FD, there is an area (first feature quantity distribution area FR1) where the generation density of the black circle RS1 is high. In this area, if the first inspection fails, the possibility of failing in the second inspection is high. The failure (crystal defect) in the first inspection contained in this area is likely to fail in the second inspection. The "suppression characteristics" of the failure in the first inspection contained in this area are high. In the first feature quantity distribution area FR1, there is a high correlation between the results of the first inspection and the results of the second inspection.
[0036] On the other hand, in the region other than the first feature quantity distribution region FR1 , the correlation between the result of the first inspection and the result of the second inspection is low.
[0037] Based on the results of such past inspections, we have Figure 4 The characteristic quantity distribution FD shown in the example. Figure 4 As shown, the feature quantity distribution FD includes a first feature quantity distribution region FR1 and a second feature quantity distribution region FR2. The second feature quantity distribution region FR2 may be a region other than the first feature quantity distribution region FR1, for example.
[0038] The first failure rate of the second past inspection result (the result of the second inspection) corresponding to the first feature quantity distribution area FR1 is higher than the second failure rate of the second past inspection result (the result of the second inspection) corresponding to the second feature quantity distribution area FR2. The feature quantity corresponding to the first feature quantity distribution area FR1 indicates that the possibility of failure in the second inspection is high. The feature quantity corresponding to the second feature quantity distribution area FR2 indicates that the possibility of failure in the second inspection is relatively low. In an embodiment, based on such a first feature quantity distribution area FR1 and a second feature quantity distribution area FR2, a "current first inspection" is implemented.
[0039] like Figure 1 As shown, in the embodiment, for example, data of the "current first inspection" is obtained (step S109). Feature quantities (for example, the first feature quantity F1 and the second feature quantity F2, etc.) are derived from the data of the "current first inspection" (step S110). The derived feature quantities are compared with the first feature quantity distribution area FR1 based on past data (step S111). At this time, information related to the first feature quantity distribution area FR1 and the second feature quantity distribution area FR2 is read out from the storage unit 76m and used. Steps S110 and S111 correspond to at least a part of the "current first inspection".
[0040] When the derived feature quantity is in the first feature quantity distribution area FR1, it is determined to be unacceptable (step S112). For example, data (flag) indicating "unacceptable" is output. For example, data indicating "unacceptable" may be recorded.
[0041] By such processing, the first inspection can be performed efficiently. According to the embodiment, an inspection processing device capable of improving efficiency can be provided. The first inspection can be performed with high accuracy.
[0042] like Figure 1As shown, in step S111, a first process is performed on the target device (e.g., a semiconductor device) (step S130). After that, a second inspection (e.g., an electrical characteristic inspection) is performed, and data of the second inspection is obtained (step S119). The data of the second inspection is compared with the determined reference, and the result of the second inspection is determined (step S120). If the result of the second inspection is unqualified, it is determined to be unqualified (step S122). If the result of the second inspection is qualified, it is determined to be qualified (step S123).
[0043] Figure 5 and Figure 6 This is a schematic diagram illustrating the results of the operation of the inspection processing device according to the first embodiment.
[0044] Figure 5 The result of the “current first inspection” on the first sample SPL1 is illustrated. Figure 6 The results of the "current first inspection" on the second sample SPL2 are illustrated. In this example, Figure 5 As shown, in the first sample SPL1, a plurality of feature quantities V1 are derived. The plurality of feature quantities V1 are not included in the first feature quantity distribution region FR1. The first sample SPL1 is determined to be qualified. Figure 6 As shown, in the second sample SPL2, a plurality of feature quantities V1 are derived. In the second sample SPL2, at least one of the plurality of feature quantities V1 is included in the first feature quantity distribution region FR1. The second sample SPL2 is determined to be unacceptable.
[0045] In the embodiment, for example, the processing unit 76p can output a failed determination result when the result of the "current first inspection" corresponds to the first feature quantity distribution area FR1. The processing unit 76p can not output a failed determination result when the result of the "current first inspection" corresponds to the second feature quantity distribution area FR2.
[0046] The processing unit 76p may store the above-mentioned determination result in the storage unit 76m.
[0047] For example, the "current first inspection" and the "past first inspection" may include an image inspection of the target device. For example, the "past second inspection" may include an electrical characteristic inspection of the target device.
[0048] In one example, the target device is a semiconductor device including a semiconductor. The "current first inspection" and the "past first inspection" include inspection of crystal defects of the semiconductor. The "past second inspection" may include at least one of an electrical property inspection of the target device (e.g., semiconductor device) and an appearance inspection of the target device (e.g., semiconductor device).
[0049] In one example, the semiconductor includes SiC. In a semiconductor device including SiC, crystal defects of SiC are likely to affect electrical characteristics. When the embodiment is applied to a semiconductor device including SiC, higher efficiency is likely to be obtained.
[0050] For example, the inspection of crystal defects may include deriving a crystal defect feature quantity representing a feature of an image of a crystal defect included in a semiconductor. For example, the crystal defect feature quantity represents a feature of a shape of the image of the crystal defect.
[0051] The semiconductor device to which the embodiment is applied may include a plurality of semiconductor chips provided on a wafer including a semiconductor. Inspection is performed in a wafer state. Inspection of crystal defects is performed on each of the plurality of semiconductor chips.
[0052] For example, the inspection of crystal defects may include deriving a crystal defect feature quantity that represents a feature of an image of a crystal defect included in a semiconductor using a DNN (Deep Neural Network).
[0053] For example, the processing unit 76p can also derive the feature quantity distribution FD based on the first past inspection result and the second past inspection result.
[0054] The processing unit 76p may store the derived feature quantity distribution FD in the storage unit 76m.
[0055] The derivation of the feature quantity distribution FD may include compressing the dimension of at least one of the first past inspection result and the second past inspection result. The derivation of the feature quantity distribution FD may include, for example, deriving the first feature quantity distribution area FR1 and the second feature quantity distribution area FR2 by processing the first past inspection result and the second past inspection result based on at least one selected from the group consisting of Euclidean distance, standard Euclidean distance, Mahalanobis distance, Manhattan distance, Chebyshev distance, and Minkowski distance.
[0056] Figure 7 This is a flowchart illustrating the operation of the inspection processing device according to the first embodiment.
[0057] exist Figure 7 In the example of , the target device is a semiconductor device including SiC. For example, in the first inspection, a wafer including SiC is inspected. Figure 7 As shown, an alignment mark is formed on a wafer (step S101). For example, the position of a semiconductor chip within the surface of the wafer is set. For example, a crystal defect inspection of the wafer is performed (step S102). For example, the crystal defect is associated with the position of the semiconductor chip.
[0058] For example, a feature quantity is derived from the inspection image of the crystal defect of each chip (for example, step S110). The derived feature quantity is compared with the feature quantity distribution area (first feature quantity distribution area FR1) (step S103). It is determined whether the derived feature quantity is a feature quantity distribution area (second feature quantity distribution area FR2) corresponding to a pass (step S111). If the derived feature quantity is not a feature quantity distribution area (second feature quantity distribution area FR2) corresponding to a pass, it is determined to be unqualified (step S112).
[0059] In step S111, when the derived feature quantity is a feature quantity distribution area (second feature quantity distribution area FR2) corresponding to a qualified chip, the remaining process (first process) is performed (step S130). The inspection process (second inspection) is performed on the object device (semiconductor device) after the remaining process (first process) is performed (step S119). The result of the inspection process (second inspection) is determined to be qualified / failed (step S120). In step S120, when the result is qualified, it is determined to be a qualified chip (step S123). In step S120, when the result is qualified, it is determined to be an unqualified chip (step S122).
[0060] In the above-described steps S130 and S111 , the feature quantity distribution FD (the first feature quantity distribution region FR1 and the second feature quantity distribution region FR2 ) stored in the storage unit 76 m is used.
[0061] like Figure 7 As shown, for example, in step S230, a first feature quantity distribution area FR1 and a second feature quantity distribution area FR2 are determined. The first feature quantity distribution area FR1 is an area of characteristic quantities corresponding to the unqualified result of the second inspection. For example, the first feature quantity distribution area FR1 is an area containing a cluster with a high frequency of unqualified in the electrical test.
[0062] For example, the second feature quantity distribution region FR2 is a region of feature quantities corresponding to a pass result of the second inspection. For example, the second feature quantity distribution region FR2 is a region including clusters that are not related to the electrical test result.
[0063] like Figure 7 As shown, for example, a database is stored in the storage unit 76m. In the database, for example, the crystal defect inspection results and the electrical test results are associated and stored for each chip. For example, the feature quantity of the crystal defect inspection image group is derived (step S210). For example, the derived feature quantity distribution FD is divided into each cluster (step S220). The feature quantity distribution FD is divided into multiple areas. Thus, the first feature quantity distribution area FR1 and the second feature quantity distribution area FR2 are determined.
[0064] like Figure 7 As shown, the inspection result of the crystal defect obtained in step S102 can be stored in the storage unit 76m. For example, step S210 can be performed regularly. The inspection result (the result of the second inspection) obtained in step S119 can be stored in the storage unit 76m.
[0065] (Second Embodiment)
[0066] The second embodiment relates to a method for manufacturing a semiconductor device. The method for manufacturing a semiconductor device of the embodiment uses, for example, a storage unit 76m and a processing unit 76p to perform a first inspection. The storage unit 76m can store a feature quantity distribution FD. The feature quantity distribution FD includes a first feature quantity distribution area FR1 related to the semiconductor device and a second feature quantity distribution area FR2 related to the semiconductor device. The processing unit 76p can perform a "current first inspection" related to the semiconductor device based on the first feature quantity distribution area FR1 and the second feature quantity distribution area FR2 stored in the storage unit 76m.
[0067] The feature quantity distribution FD relates to a first past inspection result and a second past inspection result of a past first inspection related to the semiconductor device. The second past inspection result is obtained by a "past second inspection" of the semiconductor device after a "past first process" performed after the "past first inspection". The second past inspection result is associated with the first past inspection result. The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area FR1 is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area FR2. In an embodiment, the semiconductor device can be inspected with high efficiency.
[0068] In the second embodiment, the semiconductor device includes a semiconductor. The "current first inspection" and the "past first inspection" include inspection of crystal defects of the semiconductor. The "past second inspection" includes at least one of an electrical property inspection of the semiconductor device and an appearance inspection of the semiconductor device. The semiconductor includes SiC, for example.
[0069] The method for manufacturing a semiconductor device of the embodiment may include further performing a "current first process" on the semiconductor device after the "current first inspection". The method for manufacturing a semiconductor device of the embodiment may also include performing a "current second inspection" on the semiconductor device after the "current first process" has been performed.
[0070] The implementation method may include the following technical solutions.
[0071] (Technical Solution 1)
[0072] An inspection and processing device, comprising:
[0073] a storage unit capable of storing a feature quantity distribution including a first feature quantity distribution area related to a target device and a second feature quantity distribution area related to the target device; and
[0074] a processing unit capable of performing a current first inspection related to the target device based on the first feature quantity distribution area and the second feature quantity distribution area stored in the storage unit,
[0075] the feature quantity distribution relates to a first past inspection result and a second past inspection result of the first inspection in the past related to the target device,
[0076] the second past inspection result is obtained by a past second inspection of the target device after the past first inspection,
[0077] The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area.
[0078] (Technical Solution 2)
[0079] According to the inspection and processing device described in technical solution 1,
[0080] When the current result of the first inspection corresponds to the first feature quantity distribution area, the processing unit can output a determination result of failure.
[0081] When the result of the current first inspection corresponds to the second feature quantity distribution area, the processing unit may not output the determination result of failure.
[0082] (Technical Solution 3)
[0083] According to the inspection and processing device of technical solution 1 or 2,
[0084] A past first process is performed between the past first inspection and the past second inspection.
[0085] (Technical Solution 4)
[0086] According to any one of technical solutions 1 to 3, the inspection and processing device,
[0087] The current first inspection and the past first inspection include an image inspection of the target device,
[0088] The second inspection in the past includes an electrical characteristics inspection of the target device.
[0089] (Technical Solution 5)
[0090] According to any one of technical solutions 1 to 3, the inspection and processing device,
[0091] The target device is a semiconductor device including a semiconductor,
[0092] The current first inspection and the past first inspection include inspection of crystal defects of the semiconductor,
[0093] The second inspection in the past includes at least any one of an electrical characteristic inspection of the target device and an appearance inspection of the target device.
[0094] (Technical Solution 6)
[0095] According to the inspection and processing device described in technical solution 5,
[0096] The semiconductor includes SiC.
[0097] (Technical Solution 7)
[0098] According to the inspection and processing device described in technical solution 5,
[0099] The inspection of the crystal defect includes deriving a crystal defect feature amount indicating a feature of an image of the crystal defect included in the semiconductor.
[0100] (Technical Solution 8)
[0101] According to the inspection and processing device described in technical solution 7,
[0102] The crystal defect feature amount represents a feature of a shape of the image of the crystal defect.
[0103] (Technical Solution 9)
[0104] An inspection and processing device according to any one of technical solutions 5 to 8, wherein:
[0105] The semiconductor device includes a plurality of semiconductor chips disposed on a wafer including the semiconductor.
[0106] The inspection of the crystal defects is performed for each of the plurality of semiconductor chips.
[0107] (Technical Solution 10)
[0108] According to the inspection and processing device described in technical solution 5,
[0109] The inspection of the crystal defects includes deriving a crystal defect feature quantity representing a feature of an image of the crystal defects included in the semiconductor using a DNN (Deep Neural Network).
[0110] (Technical Solution 11)
[0111] An inspection and processing device according to any one of technical solutions 7 to 10, wherein:
[0112] The feature quantity distribution is two-dimensional.
[0113] (Technical Solution 12)
[0114] An inspection and processing device according to any one of technical solutions 7 to 10, wherein:
[0115] The feature quantity distribution is two-dimensional including the first feature quantity and the second feature quantity.
[0116] The first feature quantity distribution area is an area in the two dimensions,
[0117] The second feature quantity distribution region is another region in the two dimensions.
[0118] (Technical Solution 13)
[0119] An inspection and processing device according to any one of technical solutions 1 to 12, wherein:
[0120] The processing unit may further derive the feature quantity distribution based on the first past inspection result and the second past inspection result.
[0121] (Technical Solution 14)
[0122] According to the inspection and processing device described in technical solution 13,
[0123] The processing unit may cause the storage unit to store the derived feature quantity distribution.
[0124] (Technical Solution 15)
[0125] An inspection processing device according to technical solution 13 or 14, wherein the derivation of the feature quantity distribution includes compressing the dimension of at least any one of the first past inspection result and the second past inspection result.
[0126] (Technical Solution 16)
[0127] An inspection and processing device according to any one of technical solutions 13 to 15, wherein:
[0128] The derivation of the feature quantity distribution includes: deriving the first feature quantity distribution area and the second feature quantity distribution area by processing the first past inspection result and the second past inspection result based on at least one selected from the group consisting of Euclidean distance, standard Euclidean distance, Mahalanobis distance, Manhattan distance, Chebyshev distance and Minkowski distance.
[0129] (Technical Solution 17)
[0130] A method for manufacturing a semiconductor device, comprising: performing the first inspection using a storage unit and a processing unit,
[0131] The storage unit is capable of storing a feature quantity distribution including a first feature quantity distribution region related to the semiconductor device and a second feature quantity distribution region related to the semiconductor device.
[0132] The processing unit is capable of performing a current first inspection related to the semiconductor device based on the first feature quantity distribution area and the second feature quantity distribution area stored in the storage unit,
[0133] the feature quantity distribution relates to a first past inspection result and a second past inspection result of the first inspection in the past related to the semiconductor device,
[0134] the second past inspection result is obtained by a past second inspection of the semiconductor device after the past first inspection,
[0135] The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area.
[0136] (Technical Solution 18)
[0137] The method for manufacturing a semiconductor device according to technical solution 17, wherein:
[0138] The semiconductor device comprises a semiconductor,
[0139] The current first inspection and the past first inspection include inspection of crystal defects of the semiconductor,
[0140] The second inspection in the past includes at least one of an electrical characteristic inspection of the semiconductor device and an appearance inspection of the semiconductor device.
[0141] (Technical Solution 19)
[0142] The method for manufacturing a semiconductor device according to technical solution 18, wherein:
[0143] The semiconductor includes SiC.
[0144] (Technical Solution 20)
[0145] The method for manufacturing a semiconductor device according to technical solution 17 or 18, wherein:
[0146] further performing a current first process on the semiconductor device after the current first inspection,
[0147] A current second inspection is further performed on the semiconductor device after the current first process is performed.
[0148] According to the embodiments, it is possible to provide an inspection processing apparatus and a method for manufacturing a semiconductor device that can improve efficiency.
[0149] The embodiments of the present invention are described above with reference to specific examples. However, the present invention is not limited to these specific examples. For example, as long as a person skilled in the art can implement the present invention in the same manner and obtain the same effect by appropriately selecting from the known range, the specific configuration of each element such as a storage unit and a processing unit included in the inspection and processing device is included in the scope of the present invention.
[0150] Furthermore, embodiments in which two or more elements of the specific examples are combined within a technically possible range are also included in the scope of the present invention as long as they include the gist of the present invention.
[0151] In addition, as an embodiment of the present invention, based on the above-mentioned inspection and processing device and semiconductor device manufacturing method, all inspection and processing devices and semiconductor device manufacturing methods that can be implemented by technicians in this field with appropriate design changes also fall within the scope of the present invention as long as they include the main purpose of the present invention.
[0152] Furthermore, within the scope of the concept of the present invention, a person skilled in the art can conceive of various changes and modifications, and these changes and modifications also belong to the scope of the present invention.
[0153] Several embodiments of the present invention have been described, but these embodiments are presented as examples and are not intended to limit the scope of the invention. These new embodiments can be implemented in various other ways, and various omissions, substitutions, and changes can be made without departing from the scope of the subject matter of the invention. These embodiments and their variations are included in the scope or subject matter of the invention, and are included in the invention described in the claims and their equivalents.
[0154] Description of Reference Numerals
[0155] 76d: display; 76f: interface; 76i: input device; 76m: storage unit; 76p: processing unit; 110: inspection processing device; F1, F2: first and second characteristic quantities; FD: characteristic quantity distribution; FR1, FR2: first and second characteristic quantity distribution areas; RS1: black circle; RS2: white circle; SPL1, SPL2: first and second samples; V1: characteristic quantity.
Claims
1. An inspection and processing device, comprising: a storage unit capable of storing a feature quantity distribution including a first feature quantity distribution area related to a target device and a second feature quantity distribution area related to the target device; and a processing unit capable of performing a current first inspection related to the target device based on the first feature quantity distribution area and the second feature quantity distribution area stored in the storage unit, the feature quantity distribution relates to a first past inspection result and a second past inspection result of the first inspection in the past related to the target device, the second past inspection result is obtained by a past second inspection of the target device after the past first inspection, The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area.
2. The inspection processing device according to claim 1, wherein: When the current result of the first inspection corresponds to the first feature quantity distribution area, the processing unit can output a determination result of failure. When the result of the current first inspection corresponds to the second feature quantity distribution area, the processing unit may not output the determination result of failure.
3. The inspection processing device according to claim 1, wherein: A past first process is performed between the past first inspection and the past second inspection.
4. The inspection processing device according to claim 1, wherein: The current first inspection and the past first inspection include an image inspection of the target device, The second inspection in the past includes an electrical characteristics inspection of the target device.
5. The inspection and processing device according to any one of claims 1 to 3, wherein: The target device is a semiconductor device including a semiconductor, The current first inspection and the past first inspection include inspection of crystal defects of the semiconductor, The second inspection in the past includes at least one of an electrical characteristic inspection of the target device and an appearance inspection of the target device.
6. The inspection processing device according to claim 5, wherein: The inspection of the crystal defect includes deriving a crystal defect feature amount indicating a feature of an image of the crystal defect included in the semiconductor.
7. The inspection processing device according to claim 5, wherein: The semiconductor device includes a plurality of semiconductor chips disposed on a wafer including the semiconductor. The inspection of the crystal defects is performed on each of the plurality of semiconductor chips.
8. The inspection processing device according to claim 6, wherein: The feature quantity distribution is two-dimensional including the first feature quantity and the second feature quantity, The first feature quantity distribution area is an area in the two dimensions, The second feature quantity distribution region is another region in the two dimensions.
9. A method for manufacturing a semiconductor device, comprising: performing a first inspection using a storage unit and a processing unit, The storage unit is capable of storing a feature quantity distribution including a first feature quantity distribution region related to the semiconductor device and a second feature quantity distribution region related to the semiconductor device. The processing unit is capable of performing the current first inspection related to the semiconductor device based on the first feature quantity distribution area and the second feature quantity distribution area stored in the storage unit, the feature quantity distribution relates to a first past inspection result and a second past inspection result of the first inspection in the past related to the semiconductor device, the second past inspection result is obtained by a past second inspection of the semiconductor device after the past first inspection, The first failure rate of the second past inspection result corresponding to the first feature quantity distribution area is higher than the second failure rate of the second past inspection result corresponding to the second feature quantity distribution area.
10. The method for manufacturing a semiconductor device according to claim 9, wherein: The semiconductor device comprises a semiconductor, The current first inspection and the past first inspection include inspection of crystal defects of the semiconductor, The second inspection in the past includes at least one of an electrical characteristic inspection of the semiconductor device and an appearance inspection of the semiconductor device.
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Systems and methods incorporating neural networks and forward physical models in semiconductor applications
JP2019525450A