Method and apparatus for detecting cracks in lamp housings

Abstract

This specification provides a method and apparatus for detecting a lamp housing crack. For example, a method related to a process chamber having a lamp housing includes sequentially processing a plurality of substrates within the process chamber, collecting lamp housing data indicating fluid leakage within the lamp housing from a sensor operably connected to the lamp housing of the process chamber during processing of the plurality of substrates, determining from the lamp housing data whether a lamp housing crack exists, and performing at least one of issuing an alarm or stopping the processing of the process chamber in response to determining that a lamp housing crack exists.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to methods and apparatus for processing a substrate, for example to methods and apparatus for detecting lamp housing cracking. Background Art

[0002] The use of radiant heat sources in substrate processing is known. For example, reduced pressure chambers and atmospheric chambers may comprise a lamp housing including one or more lamps configured to heat one or more substrates to a relatively high temperature (e.g., for an annealing process, oxidation removal, etc.). However, after long-term use, the lamp housing (and / or one or more lamps) may develop cracks (see FIG. 1 below). A cracked lamp housing may leak cooling water into the lamp tube of the lamp and / or onto the quartz window through which light is emitted. As a result, the process chamber requires relatively long unplanned downtime and corrective maintenance to replace the lamp housing, remove water leaks, and address additional failure of the lamp and / or related components due to water intrusion. Summary of the Invention Problem to be Solved by the Invention

[0003] Accordingly, the inventors of the present invention describe herein improved methods and apparatus for detecting lamp housing cracking. Means for Solving the Problem

[0004] This specification provides methods and apparatus for detecting lamp housing cracks. In some embodiments, a method relating to a process chamber having a lamp housing includes sequentially processing a plurality of substrates in the process chamber; collecting lamp housing data indicating fluid leakage in the lamp housing from sensors operably connected to the lamp housing of the process chamber during the processing of the plurality of substrates; determining from the lamp housing data whether a lamp housing crack is present; and, in response to the determination that a lamp housing crack is present, issuing an alarm or stopping processing in the process chamber.

[0005] In at least some embodiments, a non-transient computer-readable storage medium has instructions stored thereon that, when executed by a process, perform a method using a ramp housing to process substrates. This method includes sequentially processing multiple substrates in a process chamber, collecting ramp housing data indicating fluid leakage within the ramp housing from sensors operably connected to the ramp housing of the process chamber while processing the multiple substrates, determining from the ramp housing data whether a crack in the ramp housing is present, and, in response to determining the presence of a crack in the ramp housing, issuing an alarm or stopping the process chamber.

[0006] In at least some embodiments, the apparatus for processing substrates comprises a process chamber configured to process substrates using radiant heat provided by a lamp housing, a sensor operably connected to the lamp housing, and a controller, the controller being configured to sequentially process a plurality of substrates in the process chamber, and during the processing of the plurality of substrates, to collect lamp housing data indicating fluid leakage in the lamp housing from the sensor operably connected to the lamp housing of the process chamber, to determine from the lamp housing data whether a lamp housing crack is present, and in response to the determination that a lamp housing crack is present, to issue an alarm or to stop processing in the process chamber.

[0007] Other embodiments and additional embodiments of this disclosure are described below.

[0008] The embodiments of this disclosure, briefly outlined above and discussed in more detail later, can be understood by referring to the exemplary embodiments of this disclosure shown in the accompanying drawings. However, the accompanying drawings only show typical embodiments of this disclosure and should therefore not be considered limiting, as this disclosure may accept other equally valid embodiments. [Brief explanation of the drawing]

[0009] [Figure 1] This is a flowchart of a method relating to a process chamber having a lamp housing, according to at least some embodiments of the present disclosure. [Figure 2] This figure shows a tool configured for use in carrying out the method of Figure 1, according to at least some embodiments of the present disclosure. [Figure 3] This figure shows a lamp housing configured for use with the tool shown in Figure 2, according to at least some embodiments of the present disclosure. [Modes for carrying out the invention]

[0010] For ease of understanding, the same reference numerals have been used to indicate identical elements common to these figures where possible. These figures are not drawn at a constant scale and may be simplified for clarity. Elements and features of one embodiment may be incorporated into other embodiments for the benefit of their own without further description.

[0011] The methods and apparatus described herein are configured for use with a process chamber configured to process a substrate. For example, the process chamber may include one or more lamp housing sensors that can be configured to detect lamp housing pressure, e.g., one or more lamp housing sensors that can be configured to detect lamp housing pressure when used with a depressurized process chamber. In at least some embodiments, the methods and apparatus described herein use one or more humidity sensors that can be configured to detect relative humidity in the lamp housing, e.g., one or more humidity sensors that can be configured to detect relative humidity in the lamp housing when used with an atmospheric pressure process chamber. For example, in either embodiment, data collected from one or more lamp housing sensors or humidity sensors can be used to predict when cracks will occur / form on the lamp housing. For example, with respect to one or more lamp housing sensors, this collected data allows the user to determine lamp housing pressure changes that may correlate with premature cracking. Similarly, with respect to humidity sensors, this collected data allows the user to determine lamp housing humidity changes that may correlate with premature cracking. It is also possible to set limits for the collected data (e.g., failure vs. normal) to warn the user of early break detection by using an automated time series modeling that uses a window based on at least one of the moving average of the range of collected data values, which is the difference between the maximum and minimum values, or the standard deviation of the range of collected data values. The methods and apparatus described herein can reduce, and in some cases eliminate, the need for unscheduled downtime and corrective maintenance to replace lamp housings, eliminate water leaks, and address additional failures of the lamp housing (and / or lamp head) and / or related components due to water ingress.

[0012] Figure 1 is a flowchart of a method 100 for processing a substrate according to at least some embodiments of the present disclosure, and Figure 2 is a tool 200 (or apparatus) that can be used to carry out the method 100 according to at least some embodiments of the present disclosure.

[0013] Method 100 may be performed within a tool 200, which may include any suitable process chamber configured for one or more physical vapor deposition (PVD), chemical vapor deposition (CVD), and / or atomic layer deposition (ALD), such as plasma ALD or thermal ALD (e.g., without plasma formation). Examples of processing systems that may be used to perform the methods of the present invention disclosed herein include, but are not limited to, one or more process chambers commercially available from Applied Materials, Inc. in Santa Clara, California, USA. Other process chambers, including process chambers from other manufacturers, may be used as appropriate in connection with the teachings provided herein.

[0014] Tool 200 can be implemented in individual process chambers, which may be provided in a standalone configuration, or as part of a cluster tool, for example, as an integrated tool (Tool 200) as described below with respect to Figure 2. Examples of integrated tools include, but are not limited to, one or more process chambers commercially available from Applied Materials, Inc. in Santa Clara, California, USA. The methods described herein may also be implemented using other cluster tools to which suitable process chambers are coupled, or in other suitable process chambers. For example, in some embodiments, the methods of the present invention discussed above may be implemented within an integrated tool such that vacuum breaks between processing steps are limited or absent. For example, reduced vacuum breaks may limit or prevent contamination (e.g., oxidation) of one or more metal layers or other parts of the substrate.

[0015] The integrated tool includes a processing platform 201 (vacuum-tight processing platform), a factory interface 204, and a controller 202. The processing platform 201 comprises several process chambers, including 214A, 214B, 214C, and 214D operably coupled to a transfer chamber 203 (vacuum substrate transfer chamber), and process chambers 214E and 214F operably coupled to a buffer chamber 208 (vacuum substrate buffer chamber).

[0016] The factory interface 204 is operably coupled to the buffer chamber 208 by one or more load lock chambers (two load lock chambers, such as 206A and 206B shown in Figure 2). In at least some embodiments, either the buffer chamber 208 or the transfer chamber 203 of the tool 200 can be omitted. One or more modules or channels can be provided between the buffer chamber 208 and the transfer chamber 203, and these modules or channels can be configured to receive one or more substrates from the buffer chamber 208 and / or the transfer chamber 203. In at least some embodiments, modules 218A and 218B are provided between the buffer chamber 208 and the transfer chamber 203, and modules 218A and 218B are configured to receive one or more substrates from the buffer chamber 208 and / or the transfer chamber 203 during operation, as will be described in more detail later. As described above, modules 218A and 218B may have transparent covers.

[0017] In some embodiments, to facilitate the transfer of one or more semiconductor substrates (wafers), the factory interface 204 includes a docking station 207 and a factory interface robot 238. The docking station 207 is configured to receive one or more front-opening unified pods (FOUPs). An embodiment in Figure 2 shows four FOUPs, including 205A, 205B, 205C, and 205D. The factory interface robot 238 is configured to transfer substrates from the factory interface 204 to the processing platform 201 through load-lock chambers such as 206A and 206B. Each of the load-lock chambers 206A and 206B has a first port coupled to the factory interface 204 and a second port coupled to the transfer chamber 203. The load lock chambers 206A and 206B are coupled to a pressure control system (not shown) that pumps depressurization and ventilation of the load lock chambers 206A and 206B to facilitate the transfer of substrates between the vacuum environment of the buffer chamber 208 and the substantial ambient (e.g., atmospheric) environment of the factory interface 204. In at least some embodiments, the buffer chamber 208 can be maintained in the substantial ambient environment. In embodiments where the buffer chamber 208 is not used, the load lock chambers 206A and 206B facilitate the transfer of substrates between the transfer chamber 203 and the factory interface 204. The buffer chamber 208 and the transfer chamber 203 each have a vacuum robot 242 positioned to transfer / receive one or more substrates. For example, the vacuum robot 242 of the buffer chamber 208 can receive / transfer a substrate 221 between the load lock chambers 206A and 206B, the process chambers 214E and 214F, and modules 218A and 218F. Similarly, the vacuum robot 242 in the transfer chamber 203 can receive / transfer the substrate 221 between the process chambers 214A, 214B, 214C, and 214D, as well as modules 218A and 218F.

[0018] In some embodiments, process chambers 214A, 214B, 214C, 214D, 214E, and 214F may include at least an ALD chamber, a CVD chamber, a PVD chamber, an e-beam deposition chamber, and / or an electroless electroplating (EEP) deposition chamber. Similarly, in some embodiments, one or more optional service chambers (shown as 216A and 216B) may be coupled to the buffer chamber 208. Service chambers 216A and 216B may be configured to perform other substrate processes such as degassing, bonding, chemical mechanical polishing (CMP), annealing, substrate cleaning (e.g., pre-cleaning to remove oxidation), wafer cutting, etching, plasma dicing, orientation, substrate measurement, and cooling.

[0019] The controller 202 controls the operation of the tool 200 by directly controlling the process chambers 214A, 214B, 214C, 214D, 214E, and 214F and the apparatus 212, or, instead, by controlling a computer (or controller) associated with the process chambers 214A, 214B, 214C, 214D, 214E, and 214F, the apparatus 212, and the tool 200. During operation, the controller 202 enables data collection and feedback from each chamber and system to optimize the performance of the tool 200. For example, the controller 202 can receive data from one or more sensors operably coupled to one or more of the process chambers 214A, 214B, 214C, 214D, 214E, and 214F, service chambers 216A and 216B, and / or modules 218A and 218F. The controller 202 generally includes a central processing unit 230, memory 234, and support circuits 232. The central processing unit 230 may be any form of general-purpose computer processor that can be used in an industrial environment. The support circuits 232 are conventionally coupled to the central processing unit 230 and may include a cache, clock circuit, input / output subsystem, power supply, etc. Software routines, such as the processing methods described above, may be stored in memory 234 (e.g., a non-transient computer-readable storage medium) and, when executed by the central processing unit 230, may transform the central processing unit 230 into a purpose-specific computer (e.g., controller 202). These software routines may be stored and / or executed by a second controller (not shown) located far away from the tool 200.

[0020] Refer to Figure 1 next. First, one or more substrates, thermal module assemblies, etc., may be loaded into one or more of the four FOUPs, for example, 205A, 205B, 205C, and 205D (Figure 2). For example, in at least some embodiments, a substrate 221 (wafer) can be loaded into FOUP 205B. The substrate 221 can have a diameter of 150 mm, 200 mm, 300 mm, etc. The substrate 221 can be formed from germanium, silicon, silicon carbide, silicon oxide, etc. In at least some embodiments, the substrate 221 can have a diameter of 300 mm and can be formed from silicon. In at least some embodiments, one or more metal layers can be deposited on the substrate 221. For example, these one or more metal layers can include aluminum, cobalt, copper, nitride, titanium, tantalum, etc. In at least some embodiments, the substrate 221 can include metal layers containing cobalt and tungsten.

[0021] After loading, the factory interface robot 238 can transfer the substrate 221 from the factory interface 204 to the processing platform 201, for example, through the load lock chamber 206A. The vacuum robot 242 of the buffer chamber 208 can transfer the substrate 221 from the load lock chamber 206A to one or more of the process chambers 214A, 214B, 214C, 214D, 214E, and 214F, service chambers 216A and 216B, and / or modules 218A and 218B, and can transfer the substrate 221 from one or more of the process chambers 214A, 214B, 214C, 214D, 214E, and 214F, service chambers 216A and 216B, and / or modules 218A and 218B.

[0022] For example, in at least some embodiments, a vacuum robot 242 in the buffer chamber 208 can transfer the substrate 221 from the load lock chamber 206A to the service chambers 216A and 216B and / or modules 218A and 218B, where one or more of the following can be performed: degassing, bonding, chemical mechanical polishing (CMP), annealing, substrate cleaning (oxidation removal process), wafer cutting, etching, plasma dicing, orientation, substrate measurement and / or cooling.

[0023] For example, as described above, the substrate 221 can be processed in one or more process chambers configured to process the substrate using radiant heat provided by a lamp housing. For example, service chambers 216A and 216B (e.g., radiant chambers) can be configured to perform an annealing process (annealing chamber) or an oxidation removal process (oxidation chamber), each of which uses relatively high heat. When performing such a process, service chambers 216A and 216B may include one or more sensors 300 (e.g., a lamp housing sensor and / or a relative humidity sensor) operably coupled to the lamp housing via one or more suitable coupling devices (e.g., bolts, clamps, etc.). For example, when service chambers 216A and 216B are configured for use in a reduced pressure environment (e.g., an annealing process), the one or more sensors 300 can be configured to detect lamp housing pressure (e.g., a pressure sensor). Similarly, when service chambers 216A and 216B are configured for use in an atmospheric pressure environment (e.g., an oxidation removal process), the one or more sensors 300 can be configured to detect relative humidity within the lamp housing (e.g., a relative humidity sensor). In either embodiment, collected data from the one or more sensors 300 can be used to predict when lamp housing cracking will occur / form. Lamp housing cracking allows coolant 304 (e.g., cooled or uncooled water from an unillustrated cooling water line) to leak from the lamp housing 302 into one or more lamp modules 306. The one or more lamp modules 306 are configured to emit / radiate high heat through a lamp window 308 to process the substrate 221 (e.g., as indicated by arrow 310). The leaked coolant 304 can damage, and in some cases destroy, the one or more lamp modules 306. This can cause unplanned downtime, labor hours, and possibly failure of other related components.

[0024] Accordingly, the inventors of the present invention have found that by using empirical data on pressure and / or relative humidity obtained during one or more annealing or oxidation removal processes, lamp housing cracks can be determined / predicted, and damage caused by lamp housing cracks can be reduced, and in some cases eliminated. The empirical data can be obtained over long periods of time (e.g., one week, two weeks, one month, two months, one year, two years, etc.). For example, with respect to a reduced pressure environment, the inventors of the present invention have found that this collected data allows the user to determine lamp housing pressure changes that may correlate with early lamp housing cracks. Similarly, with respect to an atmospheric pressure environment, the inventors of the present invention have found that this collected data allows the user to determine lamp housing humidity changes that may correlate with early housing cracks. In at least some embodiments, an automated time-series modeling can also be used to set limits for the collected data (e.g., fault vs. normal) to warn the user of the detection of early cracks, using a window based on either a moving average of the range of the collected data, which is the difference between the maximum and minimum values, or the standard deviation of the range of the collected data.

[0025] In response thereto, at 102, the method 100 includes sequentially processing a plurality of substrates within a process chamber, and at 104, the method 100 includes, during processing of the plurality of substrates, collecting lamp housing data indicating a fluid leak within a lamp housing (which leak can cause the lamp housing to crack) from a sensor operatively connected to the lamp housing of the process chamber. For example, automatic time series modeling can include one or more of: a) collecting data from a plurality of substrate (wafer) runs (annealing and / or oxidation removal processes), b) for each substrate run, calculating the range of that substrate run (e.g., the range can be defined as the value obtained by subtracting the minimum sensor value from the maximum sensor value obtained from one or more sensors 300, c) calculating a moving average of the ranges obtained by b), which moving average can be based on, for example, the last X runs, d) calculating a standard deviation of the ranges obtained by step b), which standard deviation can be based on, for example, the last X runs, e) calculating a threshold limit value by adding the moving average obtained by step c) to the standard deviation obtained by step d), and f) calculating the range of the next substrate run, and comparing the calculated range with the threshold limit value obtained in e).

[0026] Next, at 106, the method 100 includes determining from the lamp housing data whether a cracked lamp housing is present. For example, as described above, under the control of the controller 202, a substrate 221 can be transferred to one or both of service chambers 216A and 216B. For example, in at least some embodiments, the substrate 221 can be transferred to the service chamber 216A, where an oxidation removal process can be performed on the substrate 221. Alternatively or additionally, the substrate 221 can be transferred to the service chamber 216B, where an annealing process can be performed on the substrate 221.

[0027] During step 106, method 100 may include comparing a first subset of lamp housing data (e.g., empirical data collected between steps 102 and 104 a) to e) collected while processing a first substrate of a plurality of substrates with a second subset of lamp housing data collected while processing a second substrate of a plurality of substrates (e.g., step 104 f). In at least some embodiments, during step 106, method 100 may include determining a threshold value based on the first subset of lamp housing data (obtained between steps 104 e), generating a metric based on the second subset of lamp housing data, and comparing the metric with the threshold value (obtained between steps 104 f).

[0028] For example, while the oxidation removal process is being performed, one or more sensors 300 (e.g., relative humidity sensors) collect relative humidity data of the lamp housing 302 and transfer the relative humidity data to the controller 202. Similarly, while the annealing process is being performed, one or more sensors 300 (e.g., pressure sensors) collect pressure data of the lamp housing 302 (e.g., pressure changes along the surface of the lamp housing 302) and transfer the lamp housing pressure data to the controller 202.

[0029] For example, the controller 202 can compare the collected lamp housing data with previously stored lamp housing data (e.g., empirical data obtained by previously performed annealing and / or deoxidation processes during a) to e) of 102 and 104). This empirical data can be obtained using time series modeling over a fixed period of about one to two weeks, or in some embodiments less than one week or more than two weeks (e.g., one day to about two years). In at least some embodiments, this period does not have to be fixed, and data can be collected using continuous time series modeling. In at least some embodiments, the controller 202 can be configured to perform comparisons at predetermined time intervals (e.g., predetermined seconds, minutes, hours, etc.) or continuously during processing.

[0030] Next, in 108, method 100 includes, in response to determining the presence of a crack in the lamp housing, performing at least one of the following: issuing an alarm or stopping processing in the process chamber. For example, when performing an oxidation removal process, if the relative humidity of the lamp housing 302 exceeds a predetermined threshold (e.g., humidity about 50% to humidity about 60%), the controller 202 may issue an alarm and / or automatically stop processing the substrate. In at least some embodiments, the controller 202 may issue an alarm and the user may manually stop processing the substrate. In at least some embodiments, the controller 202 may be further configured to detect a time frame in which the relative humidity remains at a predetermined threshold (e.g., for processing at least one substrate). Similarly, when performing an annealing process, if the pressure of the lamp housing 302 exceeds a predetermined threshold (e.g., about 20 Torr to about 30 Torr), the controller 202 may issue an alarm and / or stop processing the substrate. In at least some embodiments, the controller 202 may be further configured to detect a time frame in which the pressure remains at a predetermined threshold (e.g., for processing at least one substrate). In at least some embodiments, for a particular substrate, predetermined threshold values ​​for relative humidity and pressure can be based on at least one of the moving average of the range of collected data obtained during a period (e.g., about one week to about two weeks, or in some embodiments, less than one week or more than two weeks) during each time-series modeling, which is the difference between the maximum and minimum values, or the standard deviation of the range of collected data.

[0031] If process chambers 214A, 214B, 214C, 214D, 214E, and 214F are configured to perform one or more annealing or deoxidation processes, then method 100, including operations 102-108, may also be used with process chambers 214A, 214B, 214C, 214D, 214E, and 214F.

[0032] Furthermore, in some embodiments, method 100 may be based on predictions from a machine learning / artificial intelligence process that can predict when a lamp housing crack might occur based on trends in previous and current pressure / humidity measurements. For example, the collected lamp data can be input into a trained machine learning model (which may be stored in memory 234, e.g.) previously trained on older data, and the output of the trained machine learning model can be used to issue an alarm as described above. For example, in at least some embodiments, during 106, method 100 may include extracting one or more features from the time-series lamp housing data and providing the one or more features to a trained neural network configured to provide an output indicating whether a lamp housing crack is present. In at least some embodiments, the one or more features may include a range of values ​​obtained during substrate processing, from the maximum to the minimum value of the sensor values ​​obtained during a) to f) in 102 and 104 as described above. For example, the collected data obtained can be input into a trained neural network, which can use a moving average and / or standard deviation to determine a predetermined threshold (e.g., pressure / humidity) and issue an alarm when the predetermined threshold is reached.

[0033] While the above description applies to embodiments of the present disclosure, other embodiments and additional embodiments of the present disclosure may be devised without departing from the basic scope of the present disclosure.

Claims

1. A method relating to a process chamber having a lamp housing, wherein the method is Processing multiple substrates sequentially within the process chamber, During the processing of the plurality of substrates, lamp housing data indicating fluid leakage within the lamp housing is collected from a sensor operably connected to the lamp housing of the process chamber. To determine from the lamp housing data whether or not a crack exists in the lamp housing, and In response to the determination that a crack exists in the lamp housing, at least one of the following is performed: issue an alarm or stop processing in the process chamber. A method that includes this.

2. Determining whether or not the aforementioned lamp housing crack exists is A first subset of the lamp housing data collected while processing the first substrate of the plurality of substrates is compared with a second subset of the lamp housing data collected while processing the second substrate of the plurality of substrates. The method according to claim 1, including the method described in claim 1.

3. The above comparison is, Determining a threshold value based on the first subset of the lamp housing data, To generate a metric based on the second subset of the lamp housing data, and The metric is compared with the threshold value. The method according to claim 2, including the method described in claim 2.

4. Determining whether or not the aforementioned lamp housing crack exists is Extracting one or more features from the lamp housing data, and The method includes providing one or more of the aforementioned features to a trained neural network, wherein the trained neural network provides an output indicating whether or not the lamp housing crack exists. The method according to claim 1.

5. The method according to claim 4, wherein one or more of the features include a range obtained by subtracting the minimum value from the maximum value of the sensor value obtained during substrate processing.

6. The method according to any one of claims 1, 2, 4, or 5, wherein the sensor is a pressure sensor configured to detect pressure along the surface of the lamp housing, or a humidity sensor configured to detect relative humidity associated with the lamp housing.

7. The method according to claim 1, wherein the process chamber is a reduced pressure chamber.

8. The method according to claim 1, wherein the process chamber is an atmospheric chamber.

9. The method according to any one of claims 1, 2, 4, 5, 7, or 8, wherein the process chamber is at least one of an annealing chamber or an oxidation chamber.

10. A non-transient computer-readable storage medium on which instructions are stored causing a method relating to a process chamber having a ramp housing to be executed when executed by a processor, wherein the method is Processing multiple substrates sequentially within the process chamber, During the processing of the plurality of substrates, lamp housing data indicating fluid leakage within the lamp housing is collected from a sensor operably connected to the lamp housing of the process chamber. To determine from the lamp housing data whether or not a crack exists in the lamp housing, and In response to the detection of a crack in the lamp housing, at least one of the following is performed: issue an alarm or stop processing in the process chamber. Non-transient, computer-readable storage media, including [specific data / information].

11. Determining whether or not there is a crack in the lamp housing is A first subset of the lamp housing data collected while processing the first substrate of the plurality of substrates is compared with a second subset of the lamp housing data collected while processing the second substrate of the plurality of substrates. A non-transient computer-readable storage medium according to claim 10, comprising:

12. A threshold value is determined based on the first subset of the lamp housing data. A metric is generated based on the second subset of the lamp housing data. The metric is compared with the threshold limit value. The non-transient computer-readable storage medium according to claim 11.

13. Determining whether or not there is a crack in the lamp housing is Extracting one or more features from time-series lamp housing data, and The method includes providing one or more of the aforementioned features to a trained neural network, wherein the trained neural network provides an output indicating whether or not a crack in the lamp housing exists. The non-transient computer-readable storage medium according to claim 10.

14. The non-transient computer-readable storage medium according to claim 13, wherein one or more of the features include a range of values ​​obtained by subtracting the minimum value from the maximum value of the sensor value obtained during substrate processing.

15. The non-transient computer-readable storage medium according to claim 10, wherein the sensor is a pressure sensor configured to detect pressure along the surface of the lamp housing, or a humidity sensor configured to detect relative humidity associated with the lamp housing.

16. The non-transient computer-readable storage medium according to claim 10, wherein the process chamber is a reduced pressure chamber.

17. The non-transient computer-readable storage medium according to claim 10, wherein the process chamber is an atmospheric chamber.

18. The non-transient computer-readable storage medium according to any one of claims 10, 11, 13, 15, 16, or 17, wherein the process chamber is at least one of an annealing chamber or an oxidation chamber.

19. A device for processing substrates, A process chamber configured to process a substrate using radiant heat provided by a lamp housing, A sensor operably connected to the lamp housing, Controller and The controller is equipped with, Multiple substrates are processed sequentially within the process chamber. During the processing of the plurality of substrates, lamp housing data indicating fluid leakage within the lamp housing is collected from the sensor operably connected to the lamp housing of the process chamber. The lamp housing data is used to determine whether a crack exists in the lamp housing. In response to the detection of a crack in the lamp housing, the system performs at least one of the following actions: issue an alarm or stop processing in the process chamber. A device configured in such a way.

20. The process chamber according to claim 19, wherein the controller is further configured to compare a first subset of the lamp housing data collected while processing a first substrate of the plurality of substrates with a second subset of the lamp housing data collected while processing a second substrate of the plurality of substrates.

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