WO2022017080A1 - 光罩缺陷检测方法及系统 - Google Patents

光罩缺陷检测方法及系统 Download PDF

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Publication number
WO2022017080A1
WO2022017080A1 PCT/CN2021/100235 CN2021100235W WO2022017080A1 WO 2022017080 A1 WO2022017080 A1 WO 2022017080A1 CN 2021100235 W CN2021100235 W CN 2021100235W WO 2022017080 A1 WO2022017080 A1 WO 2022017080A1
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defect
threshold
defects
information
static
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French (fr)
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侯力华
许文豪
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Changxin Memory Technologies Inc
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Changxin Memory Technologies Inc
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Priority to US17/460,404 priority Critical patent/US11988575B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/94—Investigating contamination, e.g. dust
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
    • G03F1/68—Preparation processes not covered by groups G03F1/20 - G03F1/50
    • G03F1/82—Auxiliary processes, e.g. cleaning or inspecting
    • G03F1/84—Inspecting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/94—Investigating contamination, e.g. dust
    • G01N2021/945—Liquid or solid deposits of macroscopic size on surfaces, e.g. drops, films, or clustered contaminants

Definitions

  • the present application relates to a photomask defect detection method and system.
  • lithography In the manufacturing process of semiconductor devices such as Dynamic Random Access Memory (DRAM), lithography is a crucial step. In the lithography process, the photomask plays a very important role. The quality of the photomask directly affects the yield of semiconductor products. Therefore, testing the quality of the photomask has also become a necessary process.
  • DRAM Dynamic Random Access Memory
  • a photomask defect detection method including:
  • a reticle defect detection system which continuously performs defect detection on the reticle when the reticle is loaded or unloaded, and obtains defect information of each defect
  • a photomask defect detection system including:
  • an acquisition module used to continuously perform defect detection on the photomask when the photomask is loaded or deactivated, and obtain defect information of each defect
  • a data processing module electrically connected to the acquisition module, for acquiring the dynamic threshold of each of the defects from the defect information of each of the defects;
  • an alarm module electrically connected to the data processing module, for judging whether the dynamic thresholds of the defects belong to the unacceptable thresholds of the detection system, and determining whether the dynamic thresholds of the defects belong to the unacceptable thresholds of the detection system When the threshold is reached, alarm processing is performed.
  • FIG. 1 is a schematic flowchart of a mask defect detection method provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of an electrical structure of a mask defect detection system provided by an embodiment of the present application.
  • the present application provides a method for detecting defects in a photomask, which mainly includes the following steps:
  • Step S110 providing a photomask
  • Step S120 providing a reticle defect detection system, which continuously performs defect detection on the reticle when the reticle is loaded or deactivated, and obtains defect information of each defect;
  • Step S130 obtaining the dynamic threshold of each of the defects from the defect information of each of the defects.
  • Step S140 judging whether the dynamic threshold of each defect belongs to an unacceptable threshold of the detection system, and if so, perform alarm processing.
  • the defects of reticle generally include fog defects, particle dust, scratches, etc.
  • an intelligent reticle inspection station Intelligent Reticle Inspection Station, IRIS
  • IRIS Intelligent Reticle Inspection Station
  • the industry usually uses a fixed illuminance spot specification and a relatively common exposure lighting condition to calculate the size of particles falling on the mask, so as to obtain a fixed particle size specification requirement to determine the particles falling on the mask or Whether the defect affects the product.
  • mask defects are detected based on IRIS inspection data. For example, when particles are detected and analyzed for early warning, NG will be reported when the particle size is 40 ⁇ m, and relevant personnel will be notified to process the mask.
  • the measurement error is taken into account when performing mask inspection, and the dynamic threshold value of each defect is obtained according to the defect information of each detected defect, and the dynamic threshold value of each defect is determined.
  • it also includes:
  • a static threshold of the detection system is set, and when the defect information of the defect reaches the static threshold, the historical defect information of the defect is acquired;
  • a dynamic threshold detection system unacceptable threshold for the defect is calculated based on the static threshold and the measurement error range.
  • the static threshold is a conventionally set parameter in the current IRIS detection system, such as 40 ⁇ m. It is understandable that due to the error in the measurement of the machine, the detection size of some particles with an actual size greater than or equal to 40 ⁇ m is smaller than 40 ⁇ m, and the detection size may be 32 ⁇ m, 33 ⁇ m or 37 ⁇ m, so it is impossible to accurately and timely alarm.
  • the measurement error range of the defect is calculated based on the historical defect information of the defects of different photomasks, and then the static threshold is reasonably narrowed by using the measurement error range to obtain the unacceptable threshold of the detection system. The dynamic threshold of each defect is compared with the unacceptable threshold of the detection system to realize early warning.
  • the measurement error range is (V min , V max ), and the dynamic threshold range of each defect is (V t +V min , V t +V max ); wherein, V max is The maximum value of the mean absolute error of the historical defect information of the defect that reaches the static threshold, V min is the minimum value of the mean absolute error of the historical defect information of the defect that reaches the static threshold, and V t is the defect information.
  • the average absolute error is the average error calculated by taking the absolute value of the prediction error.
  • the average absolute error can avoid the problem of mutual cancellation of errors, so it can more accurately reflect the size of the actual prediction error.
  • the dynamic threshold of the defect is based on the actual measurement data plus the error range; then, according to the average
  • the minimum absolute error, the maximum mean absolute error, and the defect information for the defect determine a dynamic threshold range for the defect, and compare the dynamic threshold range for the defect to an unacceptable threshold for the inspection system, if the defect
  • the dynamic threshold range of the defect has an intersection with the unacceptable threshold of the detection system, that is, there is at least one defect information in the dynamic threshold range of the defect that belongs to the unacceptable threshold of the detection system.
  • the unacceptable threshold of the detection system used may be a static threshold, such as a static threshold set in the detection system; the detection system is unacceptable
  • the threshold can also be a dynamic threshold, such as using the measurement error to limit the static range to obtain an unacceptable dynamic threshold range of the detection system, and use the unacceptable dynamic threshold range of the detection system and the dynamic threshold of the defect to achieve early warning.
  • the unacceptable threshold for the detection system is the static threshold.
  • the static threshold of the inspection system set before the inspection can be used as the unacceptable threshold of the inspection system, and the dynamic threshold range of the defect and the unacceptable threshold of the inspection system can be compared with each other. By comparison, it is judged whether there is an intersection between the dynamic threshold range of the defect and the unacceptable threshold of the detection system; if there is an intersection, alarm processing is performed.
  • the static threshold set in the detection system can be narrowed by using the measurement error, and a value can be selected from the narrowed range according to experience or randomly as the unacceptable threshold of the detection system; for example, According to the measurement error, the static threshold (assumed to be 40 ⁇ m) set in the detection system is narrowed, and the narrowed range is (38, 39). If the operator believes that selecting 38.5 ⁇ m can be carried out in time according to experience When an alarm is issued and it is beneficial to reduce the number of mask cleanings, the unacceptable threshold of the detection system can be set to 38.5 ⁇ m.
  • the unacceptable threshold of the detection system is a dynamic threshold, and its range is (V target - V max , Vtarget - V min ); wherein, V target is the static threshold.
  • the minimum value of the average absolute error and the maximum value of the average absolute error are determined from the plurality of average absolute errors, and according to the average absolute error
  • the minimum value and the maximum value of the average absolute error limit the set static threshold reasonably, and take the measurement error into consideration, so as to accurately determine the defects whose actual size may be larger than the static threshold, so as to realize early warning and reduce the rework rate.
  • the critical range may also be (V target - kV max , V target - kV min ), where k is a coefficient that can be set according to actual detection needs and/or manual experience. By adjusting The value of k adjusts the narrowing range of the static threshold.
  • the defect information of the defect includes coordinate information and size of the defect.
  • V MAD is the mean absolute error of the defect, is the average size of the defects
  • x 1 , x 2 , . . . , x n is the size of the detected defects
  • Cnt is the number of the defects with the same coordinate information but with deviations in size.
  • the size of the defect is obtained during the detection process, and the coordinate information of the defect is also obtained, so that in the subsequent process of obtaining the measurement error range according to the historical defect information of the defect, the position information corresponding to the same defect can be determined.
  • the detected defect size and then calculate the average absolute error value corresponding to the defect according to all the dimensions corresponding to the defect, and further determine the measurement error range according to the average absolute error value of each defect.
  • the average absolute error value corresponding to the defect with a deviation in the size of the defect is calculated only according to the determination of all the defects in the historical defect information that are located at the same coordinates and have deviations in size, which can improve the measurement error range. accuracy, while reducing the amount of calculation and speeding up the judgment process.
  • the historical defect information of the defect is defect information of the defect collected within a selected time period.
  • the historical defect information of the defect is the defect information of the defect collected in the continuous 6-8 hours .
  • data is collected within the last 6-8 hours when the defect information of the defect detected reaches the static threshold.
  • the duration can also be reasonably adjusted according to the frequency of use of the reticle. For example, if a certain reticle is used very frequently, the defect information of the defect collected in the last 4 to 5 hours can be used as the defect historical defect information. For another example, if the frequency of use of a certain photomask is extremely low, the defect information of the defect previously collected within half a year or three months may be used as the historical defect information of the defect.
  • the historical defect information of the defects is data detected when no cleaning and/or maintenance treatment is performed.
  • the data platform stores all the collected inspection data using the photomask, and the data is too large. Therefore, it is necessary to adopt the method of feature sampling, that is, to analyze the inspection data of the photomask in a certain period of time and in the feature machine. for benchmark analysis.
  • the data obtained by detection without cleaning and/or maintenance processing is used, and the size of the defect is relatively large at this time, which is beneficial to reduce the detection error.
  • the method for detecting a photomask further includes: sending defect information of each defect to a data platform for storage.
  • the inspection data is to collect all the inspection data when the photomask is used, and the data is too large to be stored locally. Therefore, storing in the data platform can reduce the requirements for the size of the IRIS storage space, and at the same time, it is also convenient for other equipment to obtain the historical defect information of the corresponding defects from the data platform in the future, so as to realize the unified management of the inspection data.
  • the alarm information is sent in the form of an email, a short message or an alarm management system notification, so as to realize the alarm.
  • the alarm information may include the code of the reticle, the size of the defect reaching the static threshold, the machine corresponding to the reticle, and the like. Therefore, the alarm information is sent in the form of an email, a short message or an alarm management system notification, so that the staff at the monitoring platform can obtain the alarm information and the specific content contained in the alarm information, and quickly troubleshoot the fault.
  • the method for detecting reticle defects further includes: when the defect information of the detected defects reaches a static threshold, notifying staff to perform cleaning and/or maintenance on the reticle.
  • the negatively charged gas ions move to the anode plate under the action of the electric field, and collide with the particles on the surface of the mask during the movement, so that the particles are negatively charged, and the charged particles are affected by the electric field force. down, moving toward the anode plate to deposit the particles on the anode plate, thereby completely removing the particles on the surface of the reticle.
  • a new static threshold is correspondingly set according to the changed exposure conditions, and a new unacceptable threshold for the detection system is obtained by recalculation. threshold.
  • the static threshold is data related to exposure conditions. Under different exposure conditions, the allowable size of defects is different. Therefore, during the process of photolithography, when the exposure conditions change, it is necessary to change the exposure conditions according to the changed exposure conditions. A new static threshold is set corresponding to the condition, and a new threshold that is unacceptable to the detection system is recalculated.
  • the mask defect detection system includes an acquisition module 210 , a data processing module 220 and an alarm module 230 .
  • the obtaining module 210 is configured to continuously perform defect detection on the reticle when the reticle is loaded or deactivated, and obtain defect information of each defect.
  • the data processing module 220 is electrically connected to the obtaining module 210, and is configured to obtain the dynamic threshold of each of the defects from the defect information of each of the defects.
  • the alarm module 230 is electrically connected to the data processing module 220, and is used for judging whether the dynamic threshold of each defect belongs to an unacceptable threshold of the detection system, and when it is determined that the dynamic threshold of the defect belongs to the detection system Alarm processing is performed when the threshold is unacceptable.
  • reticle defect inspection is currently performed based on IRIS inspection data
  • the fixed particle size judgment mode for example, an alarm will be issued when the particle size exceeds 40 ⁇ m
  • a warning message that the particle size exceeds the specification is generated.
  • the specification of the particles prior to this may also have exceeded this specification, so the analysis of defects that do not reach 40 ⁇ m in particle size is also important.
  • the measurement error is taken into account when performing mask inspection, the dynamic threshold of each defect is obtained according to the defect information of each detected defect, and the dynamic threshold of each defect is judged.
  • the threshold belongs to the unacceptable threshold of the detection system, and when the dynamic threshold of each defect belongs to the unacceptable threshold of the detection system, alarm processing is performed to realize early warning of mask defects, so that the mask can be cleaned in time , thereby reducing rework rates and production costs.
  • the alarm module 230 is further configured to determine whether the defect information of the defect reaches a set static threshold before the detection is performed;
  • the obtaining module 210 is further configured to obtain historical defect information of the defect that reaches the static threshold when the defect information of the defect reaches the static threshold;
  • the data processing module 220 is further configured to calculate the measurement error range of the defect according to the historical defect information of the defect reaching the static threshold, and calculate the measurement error range according to the static threshold and the measurement error range. Thresholds that are unacceptable to the detection system.
  • the measurement error range of the defect is calculated by the historical defect information of the defect, and then the static threshold is reasonably narrowed by using the measurement error range to obtain the unacceptable threshold of the detection system.
  • the dynamic threshold of the defect is compared with the unacceptable threshold of the detection system to realize early warning.
  • the measurement error range is (V min , V max ), and the dynamic threshold range of each defect is (V t +V min , V t +V max ); wherein, V max is The maximum value of the mean absolute error of the historical defect information of the defect that reaches the static threshold, V min is the minimum value of the mean absolute error of the historical defect information of the defect that reaches the static threshold, and V t is the defect information.
  • the average absolute error is the average error calculated by taking the absolute value of the prediction error.
  • the average absolute error can avoid the problem of mutual cancellation of errors, so it can more accurately reflect the size of the actual prediction error.
  • the defect information of the defect exceeds the static threshold, an alarm will be issued, so the actual measurement data of the default defect will be small, so the dynamic threshold of the defect is based on the actual measurement data plus the error range.
  • the unacceptable threshold of the detection system used may be a static threshold, such as the static threshold set in the detection system; the detection system cannot
  • the accepted threshold can also be a dynamic threshold.
  • the static range is limited by the measurement error to obtain the unacceptable dynamic threshold range of the detection system, and the unacceptable dynamic threshold range of the detection system and the dynamic threshold of the defect are used to realize early warning. .
  • the unacceptable threshold for the detection system is the static threshold.
  • the static threshold of the inspection system set before the inspection can be used as the unacceptable threshold of the inspection system, and the dynamic threshold range of the defect and the unacceptable threshold of the inspection system can be compared with each other. By comparison, it is judged whether there is an intersection between the dynamic threshold range of the defect and the unacceptable threshold of the detection system; if there is an intersection, alarm processing is performed.
  • the static threshold set in the detection system may be narrowed by using the measurement error, and a value may be selected from the narrowed range according to experience or randomly as an unacceptable threshold of the detection system.
  • the measurement error range is (V min , V max ), the unacceptable threshold of the detection system is a dynamic threshold, and the range is (V target - V max , V target - V min );
  • V max is a defect history reaches the threshold value of the static mean absolute error maximum defect information
  • the defect history information of the minimum mean absolute error V min to reach the threshold value of the static defects, V target is the static threshold.
  • the minimum mean absolute error and the maximum mean absolute error are determined from a plurality of mean absolute errors, and the set static threshold is reasonably limited according to the minimum mean absolute error and the maximum mean absolute error, The measurement error is taken into account to accurately identify defects whose actual size may be larger than the static threshold, enabling early warning and reducing rework rates.
  • the critical range may also be (V target - kV max , V target - kV min ), where k is a coefficient that can be set according to actual detection needs and/or manual experience. By adjusting The value of k adjusts the narrowing range of the static threshold.
  • the historical defect information of the defect is the defect information collected in a selected time period, for example, the defect information of the defect is collected in a continuous 6-8 hours.
  • the duration corresponding to the historical defect information of the defect can also be adjusted according to the frequency of use of the mask. The higher the frequency of use of the mask, the shorter the duration corresponding to the historical defect information of the defect.
  • the alarm module 230 is further configured to notify the staff to perform cleaning and/or maintenance processing on the mask when the defect information of the detected defect reaches a static threshold.
  • the embodiments of the present application provide a method and system for detecting defects of a photomask.
  • the detection method includes: providing a photomask; providing a photomask defect detection system, when the photomask is loaded or removed from the machine, continuously performs defect detection on the photomask, and obtains defect information of each defect; Obtain the dynamic threshold of each defect from the defect historical defect information; determine whether the dynamic threshold of each defect belongs to the threshold that is unacceptable to the detection system, and if so, perform alarm processing.
  • the dynamic threshold of each defect is obtained according to the detected defect information of each defect, and it is judged whether the dynamic threshold of each defect belongs to the unacceptable threshold of the detection system, and whether the dynamic threshold of each defect belongs to an unacceptable threshold of the detection system, and When the dynamic threshold range falls within the unacceptable threshold of the detection system, alarm processing is performed to realize early warning of mask defects, so that the mask can be cleaned in time, thereby reducing the rework rate and production cost.

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Abstract

一种光罩缺陷检测方法及系统。其中光罩缺陷检测方法包括：提供光罩（S110）；提供光罩缺陷检测系统，在光罩上机或下机时，持续对光罩进行缺陷检测，获取各缺陷的缺陷信息（S120）；从各缺陷的缺陷信息中获取各缺陷的动态阈值（S130）；以及判断各缺陷的动态阈值是否属于检测系统不可接受的阈值，若是，进行告警处理（S140）。

Description

光罩缺陷检测方法及系统
相关申请交叉引用
本申请要求2020年07月23日递交的、标题为“光罩缺陷检测方法及系统”、申请号为2020107174921的中国申请，其公开内容通过引用全部结合在本申请中。
技术领域
本申请涉及一种光罩缺陷检测方法及系统。
背景技术
动态随机存储器(Dynamic Random Access Memory，DRAM)等半导体器件的制造工艺中，光刻是至关重要的步骤。在光刻工艺中，光罩起着十分重要的作用，光罩的好坏直接影响着半导体产品的良率，因而检测光罩的质量也成了必要的工序。
发明内容
根据多个实施例，本申请第一方面提供一种光罩缺陷检测方法，包括：
提供光罩；
提供光罩缺陷检测系统，在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；
从各所述缺陷的缺陷信息中获取各所缺陷的动态阈值；以及
判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，若 是，进行告警处理。
根据多个实施例，本申请第二方面提供一种光罩缺陷检测系统，包括：
获取模块，用于在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；
数据处理模块，与所述获取模块电连接，用于从各所述缺陷的缺陷信息中获取各所述缺陷的动态阈值；以及
告警模块，与所述数据处理模块电连接，用于判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，并在判定所述缺陷的动态阈值属于所述检测系统不可接受的阈值时进行告警处理。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例或传统技术中的技术方案，下面将对实施例或传统技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的一种光罩缺陷检测方法的流程示意图。
图2为本申请实施例提供的一种光罩缺陷检测系统的电气结构示意图。
具体实施方式
由于光罩在未经检测到需要清洗前是持续使用的，因此光罩表面的颗粒或者其他的缺陷(光罩缺陷主要包括雾状缺陷、颗粒灰尘、擦伤等)会在使 用过程中持续对制程产生影响，后续如果发现光罩上存在超出规格的粉尘颗粒或其他缺陷，则之前已进行光刻制程的晶圆将会全部被返工，导致生产成本增大。为了避免这种现象，制定有效的光罩缺陷检验模式尤为重要。
为了便于理解本申请，下面将参照相关附图对本申请进行更全面地描述。附图中给出了本申请的实施例。但是，本申请可以以许多不同的形式来实现，并不限于本文所描述的实施例。相反地，提供这些实施例的目的是使本申请的公开内容更加透彻全面。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中在本申请的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请。
在此使用时，单数形式的“一”、“一个”和“所述/该”也可以包括复数形式，除非上下文清楚指出另外的方式。还应当理解的是，术语“包括/包含”或“具有”等指定所陈述的特征、整体、步骤、操作、组件、部分或它们的组合的存在，但是不排除存在或添加一个或更多个其他特征、整体、步骤、操作、组件、部分或它们的组合的可能性。同时，在本说明书中，术语“和/或”包括相关所列项目的任何及所有组合。
请参阅图1，本申请提供一种光罩缺陷检测方法，主要包括如下步骤：
步骤S110，提供光罩；
步骤S120，提供光罩缺陷检测系统，在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；
步骤S130，从各所述缺陷的缺陷信息中获取各所述缺陷的动态阈值；以及
步骤S140，判断各所述缺陷的动态阈值是否属于所述检测系统不可接受 的阈值，若是，进行告警处理。
可以理解，光罩缺陷总体来说有雾状缺陷、颗粒灰尘、擦伤等，一般利用智能光罩检测机台(Intelligent Reticle Inspection Station，IRIS)对光罩缺陷进行检测。业界通常是以一个固定的照度斑规格以及一个比较通用的曝光照明条件，来计算出光罩上掉落的颗粒尺寸，从而得出一个固定的颗粒尺寸规格要求来判断光罩上掉落的颗粒或缺陷是否对产品产生影响。
当前基于IRIS检测数据进行光罩缺陷检测，例如对颗粒进行检测分析预警时，当颗粒尺寸大小为40μm就报NG，并通知相关人员对光罩进行处理，在固定颗粒尺寸判定模式下，由于检测装置检测误差的影响，在产生颗粒尺寸超规格的警告信息时，在此之前的颗粒的规格也可能已经超出该规格，因此对颗粒尺寸没有达到40μm的缺陷的分析也很重要。为解决该问题，本申请中，在进行光罩检测时将量测误差考虑进去，通过根据检测到的各缺陷的缺陷信息获取各所述缺陷的动态阈值，并判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，以及在各所述缺陷的动态阈值属于所述检测系统不可接受的阈值内时进行告警处理，实现对光罩缺陷进行提前预警，以便及时对光罩进行清洗，从而降低返工率和生产成本。
在其中一个实施例中，还包括：
在进行检测之前，设定所述检测系统的静态阈值，当所述缺陷的缺陷信息达到所述静态阈值时，获取所述缺陷的历史缺陷信息；
根据所述缺陷的历史缺陷信息计算所述缺陷的量测误差范围；以及
根据所述静态阈值和所述量测误差范围计算所述缺陷的动态阈值检测系统不可接受的阈值。
本实施例中，静态阈值为目前IRIS检测系统中常规设定参数，如40μm。 可以理解，由于机台量测存在误差，有些实际尺寸大于等于40μm的颗粒的检测尺寸比40μm小，检测尺寸有可能是32μm、33μm或37μm，因此无法准确及时进行告警。本实施例中通过不同光罩的缺陷的历史缺陷信息计算所述缺陷的量测误差范围，进而利用量测误差范围对静态阈值进行合理限缩，得到所述检测系统不可接受的阈值，并通过将各所述缺陷的动态阈值与所述检测系统不可接受的阈值进行比较，实现提前预警。
在其中一个实施例中，所述量测误差范围为(V min，V max)，各所述缺陷的动态阈值范围为(V t+V min，V t+V max)；其中，V max为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最大值，V min为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最小值，V t为所述缺陷的缺陷信息。
可以理解，平均绝对误差是将预测误差取绝对值后计算的平均误差，平均绝对误差可以避免误差相互抵消的问题，因而可以更准确反映实际预测误差的大小。此外，由于缺陷的缺陷信息超过静态阈值就会报警，所以默认的缺陷的实际量测数据会偏小，所以缺陷的动态阈值都是实际量测数据的基础上加上误差范围；然后，根据平均绝对误差最小值、平均绝对误差最大值以及所述缺陷的缺陷信息确定所述缺陷的动态阈值范围，并将所述缺陷的动态阈值范围与所述检测系统不可接受的阈值进行比较，如果所述缺陷的动态阈值范围与所述检测系统不可接受的阈值有交集，即存在所述缺陷的动态阈值范围存在至少一个缺陷信息属于所述检测系统不可接受的阈值，则会进行告警处理，从而实现提前预警，同时解决因量测误差所导致的无法进行准确预警的问题。
判断缺陷的动态阈值是否属于所述检测系统不可接受的阈值时，使用的所述检测系统不可接受的阈值可以是静态阈值，如所述检测系统中设置定的静态阈值；所述检测系统不可接受的阈值也可以是动态阈值，如利用量测误差对静态范围进行限缩，得到检测系统不可接受的动态阈值范围，并利用检测系统不可接受的动态阈值范围和所述缺陷的动态阈值实现预警。
在其中一个实施例中，所述检测系统不可接受的阈值为所述静态阈值。本实施例中，可利用在进行检测前设定的所述检测系统的静态阈值作为所述检测系统不可接受的阈值，并将所述缺陷的动态阈值范围与所述检测系统不可接受的阈值进行比较，判断所述缺陷的动态阈值范围与所述检测系统不可接受的阈值是否有交集；如有交集，则进行告警处理。另外，还可以利用量测误差对所述检测系统中设定的静态阈值进行限缩，并根据经验或随机从限缩后的范围内选择一个数值作为所述检测系统不可接受的阈值；例如，根据量测误差对所述检测系统中设定的静态阈值(假设为40μm)进行限缩后，得到限缩后的范围为(38,39)，如操作人员根据经验认为选择38.5μm能够及时进行告警且有利于减少光罩清洗次数时，可将所述检测系统不可接受的阈值设置为38.5μm。
在其中一个实施例中，所述检测系统不可接受的阈值为动态阈值，其范围为(V target-V max，Vtarget-V min)；其中，V target为所述静态阈值。
本实施例中，在根据多个缺陷的历史缺陷信息计算出每一缺陷的平均误差后，从多个平均绝对误差中确定出平均绝对误差最小值和平均绝对误差最大值，并根据平均绝对误差最小值和平均绝对误差最大值对设定的静态阈值进行合理限缩，将测量误差考虑进去，从而精准确定其实际尺寸可能大于静 态阈值的缺陷，从而实现预警，降低返工率。此外，在其它一些实施例中，所述临界范围还可以为(V target-kV max,V target-kV min)，其中k为可根据实际检测需要和/或人工经验设定的系数，通过调整k值可调整静态阈值的限缩范围。
在其中一个实施例中，所述缺陷的缺陷信息包括所述缺陷的坐标信息和尺寸。
本实施例中，首先确定所述缺陷的历史缺陷信息中具有相同的坐标信息但尺寸有偏差的所述缺陷；然后，利用平均绝对误差计算公式得到具有相同的坐标信息但尺寸有偏差的所述缺陷的平均绝对误差；
基于计算得到的所述平均绝对误差确定平均绝对误差最大值和平均绝对误差最小值，以得到所述量测误差范围；
其中，平均绝对误差计算公式为：
Figure PCTCN2021100235-appb-000001
其中，V MAD为所述缺陷的平均绝对误差，
Figure PCTCN2021100235-appb-000002
为所述缺陷的尺寸平均值，x 1，x 2，…，x n为检测到的所述缺陷的尺寸，Cnt为具有相同的坐标信息但尺寸有偏差的所述缺陷的个数。
可以理解，在检测过程中获取缺陷的尺寸的同时还获取所述缺陷的坐标信息，使得后续在根据缺陷的历史缺陷信息得到量测误差范围的过程中，可根据位置信息确定同一缺陷对应的所检测到的缺陷尺寸，进而根据所述缺陷对应的所有尺寸计算该缺陷对应的平均绝对误值，并进一步根据每一缺陷的平均绝对误差值确定量测误差范围。此外，本实施例中仅根据确定所述历史 缺陷信息中所有位于相同坐标且尺寸有偏差的所述缺陷，计算缺陷尺寸有偏差的所述缺陷对应的平均绝对误差值，可提高量测误差范围的精确度，同时降低计算量，加快判断过程。
在其中一个实施例中，所述缺陷的历史缺陷信息为在选定时间段内收集到所述缺陷的缺陷信息。
可以理解，有些光罩的利用频率比较高，则其对应的缺陷信息数据量比较大，还一些光罩的利用频率比较低，那么其对应的缺陷信息数据量则会比较小。本实施例中，考虑到数据量的问题，当所述缺陷的缺陷信息达到所述静态阈值时，所述缺陷的历史缺陷信息为在持续的6～8小时内收集到所述缺陷的缺陷信息。例如，在检测到所述缺陷的缺陷信息达到所述静态阈值时最近的6～8小时内收集到数据。此外，还可以根据光罩的使用频率合理调整时长，例如，某一光罩的利用频率极高，则可将之前在持续的4～5小时内收集到所述缺陷的缺陷信息作为所述缺陷的历史缺陷信息。再例如，某一光罩的利用频率极低，则可将之前在半年或三个月内收集到所述缺陷的缺陷信息作为所述缺陷的历史缺陷信息。
在其中一个实施例中，所述缺陷的历史缺陷信息为未做清洗和/或保养处理时检测得到的数据。
可以理解，数据平台中存储有收集到的所有使用所述光罩的检测数据，数据过于庞大，因此需要采用特征取样的方法即对分析光罩在某些时段内、特征机台内的检测数据为基准分析。此外，本实施例中采用未做清洗和/或保养处理时检测得到的数据，此时缺陷的尺寸相对较大，有利于降低检测误差。
在其中一个实施例中，所述光罩的检测方法还包括：将各所述缺陷的缺陷信息发送至数据平台进行存储。
可以理解，检测数据是收集所有使用光罩时的所有检测数据，数据过于庞大，不便于存储在本地。因此，将存储在数据平台中，可降低对IRIS存储空间大小的要求，同时也便于后续其它设备从数据平台中获取相对应的缺陷的历史缺陷信息，实现对检测数据进行统一管理。
在其中一个实施例中，以邮件、简讯或警报管理系统通知的形式发送告警信息，从而实现告警。本实施例中，所述告警信息可以包括所述的光罩的编码、达到静态阈值的缺陷的尺寸以及所述光罩对应的机台等。因此，以邮件、简讯或警报管理系统通知的形式发送所述告警信息，方便监控平台处的工作人员获取告警信息以及告警信息所包含的具体内容，并快速排除故障。
在其中一个实施例中，所述光罩缺陷的检测方法还包括：在检测到缺陷的缺陷信息达到静态阈值时，通知工作人员光罩进行清洗和/或保养处理。
可以理解，当检测到缺陷的缺陷信息达到静态阈值时，说明当前光罩上缺陷的尺寸已影响到了图案质量，无法继续利用其进行光刻，因此需要清洗。目前通常使用风吹方式进行清除；或者，在光罩上下表面分别放置阴极板和阳极板，并且在阴极板和阳极板两端施加一定的电压，使阴极板和阳极板之间形成高压电场，由于阴极发生放电，使带负电的气体离子在电力场的作用下向阳极板运动，在运动中与光罩表面的颗粒相碰，使颗粒荷以负电，荷电后的颗粒在电场力的作用下，向阳极板运动，使颗粒沉积于阳极板上，从而将光罩表面上的颗粒完全移除。
在其中一个实施例中，在进行光刻工艺的过程中，当曝光条件发生变化时，根据改变后的曝光条件对应设定新的静态阈值，并重新计算得到新的所述检测系统不可接受的阈值。
可以理解，静态阈值为与曝光条件相关的数据，不同的曝光条件下，缺 陷的允许尺寸是不同的，因此在进行光刻工艺的过程中，当曝光条件发生变化时，需要根据改变后的曝光条件对应设定新的静态阈值，并重新计算得到新的所述检测系统不可接受的阈值。
基于同一发明构思，本申请实施例还提供了一种光罩缺陷检测系统。请参见图2，所述光罩缺陷检测系统包括获取模块210、数据处理模块220和告警模块230。
所述获取模块210用于在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息。
所述数据处理模块220与所述获取模块210电连接，用于从各所述缺陷的缺陷信息中获取各所述缺陷的动态阈值。
所述告警模块230与所述数据处理模块220电连接，用于判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，并在判定所述缺陷的动态阈值属于所述检测系统不可接受的阈值时进行告警处理。
可以理解，当前基于IRIS检测数据进行光罩缺陷检测时，在固定颗粒尺寸判定模式下(例如颗粒尺寸超过40μm就进行告警)，由于检测装置检测误差的影响，在产生颗粒尺寸超规格的警告信息时，在此之前的颗粒的规格也可能已经超出该规格，因此对颗粒尺寸没有达到40μm的缺陷的分析也很重要。为解决该问题，本实施例中，在进行光罩检测时将量测误差考虑进去，通过根据检测到的各缺陷的缺陷信息获取各所述缺陷的动态阈值，并判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，以及在各所述缺陷的动态阈值属于所述检测系统不可接受的阈值时进行告警处理，对光罩缺陷实现提前预警，以便对光罩进行及时清洗，从而降低返工率和生产成本。
在其中一个实施例中，所述告警模块230，还用于在进行检测之前，判 断所述缺陷的缺陷信息是否达到设定的静态阈值；
所述获取模块210，还用于当所述缺陷的缺陷信息达到所述静态阈值时，获取达到所述静态阈值的所述缺陷的历史缺陷信息；
所述数据处理模块220，还用于根据达到所述静态阈值的所述缺陷的历史缺陷信息计算所述缺陷的量测误差范围，并根据所述静态阈值和所述量测误差范围计算所述检测系统不可接受的阈值。
本实施例中通过缺陷的历史缺陷信息计算所述缺陷的量测误差范围，进而利用量测误差范围对静态阈值进行合理限缩，得到所述检测系统不可接受的阈值，并通过将各所述缺陷的动态阈值与所述检测系统不可接受的阈值进行比较，实现提前预警。
在其中一个实施例中，所述量测误差范围为(V min，V max)，各所述缺陷的动态阈值范围为(V t+V min，V t+V max)；其中，V max为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最大值，V min为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最小值，V t为所述缺陷的缺陷信息。
可以理解，平均绝对误差是将预测误差取绝对值后计算的平均误差，平均绝对误差可以避免误差相互抵消的问题，因而可以更准确反映实际预测误差的大小。此外，由于缺陷的缺陷信息超过静态阈值就会报警，所以默认的缺陷的实际量测数据会偏小，所以缺陷的动态阈值都是实际量测数据的基础上加上误差范围。
在判断缺陷的动态阈值是否属于所述检测系统不可接受的阈值时，使用的所述检测系统不可接受的阈值可以是静态阈值，如所述检测系统中设定的 静态阈值；所述检测系统不可接受的阈值也可以是动态阈值，如利用量测误差对静态范围进行限缩，得到检测系统不可接受的动态阈值范围，并利用检测系统不可接受的动态阈值范围和所述缺陷的动态阈值实现预警。
在其中一个实施例中，所述检测系统不可接受的阈值为所述静态阈值。本实施例中，可利用在进行检测前设定的所述检测系统的静态阈值作为所述检测系统不可接受的阈值，并将所述缺陷的动态阈值范围与所述检测系统不可接受的阈值进行比较，判断所述缺陷的动态阈值范围与所述检测系统不可接受的阈值是否有交集；如有交集，则进行告警处理。另外，还可以利用量测误差对所述检测系统中设定的静态阈值进行限缩，并根据经验或随机从限缩后的范围内选择一个数值作为所述检测系统不可接受的阈值。
在其中一个实施例中，所述量测误差范围为(V min，V max)，所述检测系统不可接受的阈值为动态阈值，其范围为(V target-V max，Vtarget-V min)；
其中，V max为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最大值，V min为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最小值，V target为所述静态阈值。
本实施例中，从多个平均绝对误差中确定出平均绝对误差最小值和平均绝对误差最大值，并根据平均绝对误差最小值和平均绝对误差最大值对设定的静态阈值进行合理限缩，将测量误差考虑进去，从而精准确定其实际尺寸可能大于静态阈值的缺陷，从而实现预警，降低返工率。此外，在其它一些实施例中，所述临界范围还可以为(V target-kV max,V target-kV min)，其 中k为可根据实际检测需要和/或人工经验设定的系数，通过调整k值可调整静态阈值的限缩范围。
在其中一个实施例中，所述缺陷的历史缺陷信息为在选定的时间段内收集的缺陷信息，例如在持续的6～8小时内收集到所述缺陷的缺陷信息。此外，所述缺陷的历史缺陷信息对应的时长还可以根据光罩的使用频率进行调整，光罩使用频率越高，所述缺陷的历史缺陷信息对应的时长越短。
在其中一个实施例中，所述告警模块230还用于在检测到缺陷的缺陷信息达到静态阈值时，通知工作人员光罩进行清洗和/或保养处理。
综上，本申请实施例提供了一种光罩缺陷的检测方法及系统。所述检测方法包括：提供光罩；提供光罩缺陷检测系统，在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；从各所述缺陷的历史缺陷信息中获取各所述缺陷的动态阈值；判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，若是，则进行告警处理。本申请中，通过根据检测到的各缺陷的缺陷信息获取各所述缺陷的动态阈值，并判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，以及在各所述缺陷的动态阈值范围属于所述检测系统不可接受的阈值时进行告警处理，对光罩缺陷实现提前预警，以便及时对光罩进行及时清洗，从而降低返工率和生产成本。
在本说明书的描述中，参考术语“其中一个实施例”、“其他实施例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特征包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性描述不一定指的是相同的实施例或示例。
上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对 上述实施例各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (18)

  1. 一种光罩缺陷检测方法，包括：
    提供光罩；
    提供光罩缺陷检测系统，在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；
    从各所述缺陷的缺陷信息中获取各所述缺陷的动态阈值；以及
    判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，若是，进行告警处理。
  2. 根据权利要求1所述的光罩缺陷检测方法，还包括：
    在进行检测之前，设定所述检测系统的静态阈值，当所述缺陷的缺陷信息达到所述静态阈值时，获取所述缺陷的历史缺陷信息；
    根据所述缺陷的历史缺陷信息计算所述缺陷的量测误差范围；以及
    根据所述静态阈值和所述量测误差范围计算所述缺陷的动态阈值检测系统不可接受的阈值。
  3. 如权利要求2所述的光罩缺陷检测方法，其中所述量测误差范围为(V min，V max)，各所述缺陷的动态阈值范围为(V t+V min，V t+V max)；
    其中，V max为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最大值，V min为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最小值，V t为所述缺陷的缺陷信息。
  4. 根据权利要求3所述的光罩缺陷检测方法，所述检测系统不可接受的阈值为所述静态阈值。
  5. 如权利要求3所述的光罩缺陷检测方法，其中所述检测系统不可接受的阈值为动态阈值，其范围为(V target-V max，V target-V min)；
    其中，V target为所述静态阈值。
  6. 如权利要求2所述的光罩缺陷检测方法，所述缺陷的历史缺陷信息为在选定时间段内收集到所述缺陷的缺陷信息。
  7. 如权利要求1所述的光罩缺陷检测方法，所述缺陷的缺陷信息包括所述缺陷的坐标信息和尺寸。
  8. 如权利要求6所述的光罩缺陷检测方法，所述缺陷的历史缺陷信息为未做清洗和/或保养处理时检测得到的数据。
  9. 如权利要求1所述的光罩缺陷检测方法，还包括：
    将各所述缺陷的缺陷信息发送至数据平台进行存储。
  10. 如权利要求1所述的光罩缺陷检测方法，其中所述进行告警处理包括：
    以邮件、简讯或警报管理系统通知的形式发送告警信息。
  11. 如权利要求1所述的光罩缺陷检测方法，还包括：
    在检测到所述缺陷的缺陷信息达到静态阈值时，通知工作人员光罩进行清洗和/或保养处理。
  12. 一种光罩缺陷检测系统，包括：
    获取模块，用于在所述光罩上机或下机时，持续对所述光罩进行缺陷检测，获取各缺陷的缺陷信息；
    数据处理模块，与所述获取模块电连接，用于从各所述缺陷的缺陷信息中获取各所述缺陷的动态阈值；以及
    告警模块，与所述数据处理模块电连接，用于判断各所述缺陷的动态阈值是否属于所述检测系统不可接受的阈值，并在判定所述缺陷的动态阈值属于所述检测系统不可接受的阈值时进行告警处理。
  13. 如权利要求12所述的光罩缺陷检测系统，其中
    所述告警模块，还用于在进行检测之前，判断所述缺陷的缺陷信息是否达到设定的静态阈值；
    所述获取模块，还用于当所述缺陷的缺陷信息达到所述静态阈值时，获取达到所述静态阈值的所述缺陷的历史缺陷信息；以及
    所述数据处理模块，还用于根据达到所述静态阈值的所述缺陷的历史缺陷信息计算所述缺陷的量测误差范围，并根据所述静态阈值和所述量测误差范围计算所述缺陷的动态阈值检测系统不可接受的阈值。
  14. 如权利要求13所述的光罩缺陷检测系统，其中所述量测误差范围为(V min，V max)，各所述缺陷的动态阈值范围为(V t+V min，V t+V max)；
    其中，V max为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最大值，V min为达到所述静态阈值的所述缺陷的历史缺陷信息的平均绝对误差最小值，V t为所述缺陷的缺陷信息。
  15. 根据权利要求14所述的光罩缺陷检测系统，所述检测系统不可接受的阈值为所述静态阈值。
  16. 如权利要求14所述的光罩缺陷检测系统，其中所述检测系统不可接受的阈值为动态阈值，其范围为(V target-V max，V target-V min)；
    其中，V target为所述静态阈值。
  17. 如权利要求13所述的光罩缺陷检测系统，所述缺陷的历史缺陷信息为在选定时间段内收集到所述缺陷的缺陷信息。
  18. 如权利要求12所述的光罩缺陷检测系统，所述缺陷的缺陷信息包括所述缺陷的坐标信息和尺寸。
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