Patent Publication Number: US-11036202-B2

Title: Real-time health monitoring of semiconductor manufacturing equipment

Description:
BACKGROUND 
     1. Field of the Invention 
     The present disclosure relates to systems and methods of semiconductor manufacturing, and more particularly to monitoring the health of semiconductor manufacturing equipment for early detection of the tool malfunction. 
     2. Description of the Related Art 
     In the manufacture of semiconductor devices, cluster tools having multiple processing modules (PMs), stations, and a vacuum transfer module (VTM) are used for transporting substrates between the PMs. The cluster tool generally operates at vacuum conditions to match the pressures desired for the various PMs. A substrate is typically introduced into the cluster tool from atmosphere via an equipment front end module (EFEM) (e.g., of a clean room), which may include an atmospheric transfer module (ATM). The EFEM and the VTM of the cluster tool are hermetically separated by a load lock, which maintains the pressure of the EFEM or the VTM during substrate transport into and out of the cluster tool. The load lock is cyclically pumped down to vacuum prior to interfacing with the VTM and vented (e.g., with nitrogen) to atmosphere prior to interfacing with the EFEM. 
     Currently, there are barriers to monitoring tool health in an automated way. First, there is little extraneous space within the cluster tool to place monitoring equipment due to the efficient usage of space within the EFEM, VTM, and the PMs. Additionally, even if there were space, monitoring equipment may not be operable at the vacuum conditions of the cluster tool. Thus, there is a need in the industry to provide real-time and automatic tool health monitoring for early detection of tool performance anomalies, and for timely prompting an operator for maintenance to reduce tool down time. 
     It is in this context that embodiments arise. 
     SUMMARY 
     Methods and systems for real-time tool health monitoring are provided. Embodiments described include methods and system that use in-situ sensors to monitor airborne particles, the measurement data of which is used to monitor the maintenance needs of a semiconductor processing system. Further, door state data relating to timing various doors open/close operation within the semiconductor processing system is used to identify the source of the airborne particles to provide a recommendation as to a maintenance procedure. 
     In one embodiment, a method is provided. The method includes an operation for pumping down a load lock (LL) to a vacuum pressure to match a pressure of a vacuum transfer module (VTM) while both the equipment front end module (EFEM)-facing door and a VTM-facing door are closed. The LL interfaces with the VTM via the VTM-facing door and with an EFEM via the EFEM-facing door, wherein the VTM is interfaces with a plurality of processing modules (PMs) having a respective plurality of PM doors. The method also includes an operation for opening the VTM-facing door such that airborne particles when present in the VTM are allowed to diffuse into the LL, the airborne particles originate from the VTM or the plurality of PMs. The method further includes operations for closing the VTM-facing door and venting the LL to atmosphere and for obtaining, using a sensor in fluid communication with the LL, measurement data of the airborne particles that diffused into the LL during said opening the VTM-facing door. The method moreover includes an operation for determining, using a maintenance detection module, that a maintenance procedure is recommended on one of the LL, the VTM, or the plurality of PMs based on a quantification of the measurement data. 
     In another embodiment, another method is provided. The method includes an operation for opening an EFEM-facing door of an LL while a VTM-facing door is closed, the LL interfaced with an EFEM via the EFEM-facing door and with a VTM via the VTM-facing door. Opening the EFEM-facing door allows airborne particles when present in the EFEM to diffuse into the LL. The method also includes an operation for obtaining, using a sensor in fluid communication with the LL, measurement data of the airborne particles that diffused into the LL from the EFEM. The operation further includes an operation for determining, using a maintenance detection module, that a maintenance procedure is recommended on either the LL or the EFEM based on quantification of the measurement data. 
     In another embodiment, a system is provided. The system includes an LL that interfaces with an EFEM via an EFEM-facing door and with a VTM via a VTM-facing door, wherein the VTM is configured to interface a plurality of PMs having PM doors. The system also includes a sensor in fluid communication with the LL for obtaining measurement data of airborne particles that diffuse into the LL from the EFEM or the VTM. The system further includes a valve disposed along a line between the sensor and the LL, the valve is opened when the pressure of the LL is at atmosphere and closed when the pressure of the LL is at vacuum. The system moreover includes a computer module configured for processing measurement data received from the sensor for determining that a maintenance procedure is recommended on one of the LL, the VTM, the EFEM, or the plurality of PMs. 
     Other aspects will become apparent from the following detailed description, taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments may best be understood by reference to the following description taken in conjunction with the accompanying drawings. 
         FIG. 1  depicts a semiconductor processing system illustrating various chambers of which a sensor provides in-situ health monitoring, according to one embodiment. 
         FIG. 2  depicts a conceptual diagram of particle movement and subsequent detection by sensor at various stages of the load lock (LL) during substrate transfer between the equipment front end module (EFEM) and the vacuum transfer module (VTM) and vice versa, according to one embodiment. 
         FIG. 3  depicts a table showing the various steps associated with in-situ airborne particle monitoring and counting method, according to one embodiment. 
         FIG. 4  shows depicts a semiconductor processing system and various door open times used for correlating particle count to a source of contamination for maintenance detection, according to one embodiment. 
         FIG. 5  shows a maintenance detection module of a computer using various door state data and various sensor data for detecting that a component within a substrate process system needs maintenance, according to one embodiment. 
         FIG. 6  shows a method for identifying the source of airborne particles and for detecting a maintenance need of a semiconductor processing system via in-situ sensors, according to one embodiment. 
         FIG. 7  depicts an interface at which the EFEM and the LL meet and various locations for placement of sensors for tool health monitoring purposes, according to one embodiment. 
         FIG. 8  shows experimental data gathered from various particle sensor locations under various circumstances, according to one embodiment. 
         FIG. 9  shows an overall flow of a method for automatically determining if a maintenance procedure on a tool is recommended based on sensor measurement data, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The following embodiments describe systems, methods, and devices for monitoring the health of components of a cluster tool used for semiconductor manufacturing. Embodiments presented here describe solutions to current difficulties of performing real-time and automated monitoring of tool health. The health of a cluster tool refers generally to the physical state of the cluster tool, including the physical state of any subset of components thereof. Tool health may also refer to whether or not one or more components of the tool requires or would be functionally or structurally benefited by maintenance, repair, cleaning, replacement, refurbishment, or the like. Embodiments described here provide data related to tool health monitoring in an automated way by measuring airborne particles originating from various internal components of a cluster tool. The measurement data of the airborne particles (e.g., particle count, particles size, etc.) are correlated with component usage data (e.g., number of rotations made by a pivot, amount of time a door is open/closed) to identify one or more components that would be benefited from maintenance and the nature and extent of such maintenance. The process of identification of a component that would benefit from maintenance and the approximation of the nature and extent of the maintenance may be referred to as maintenance detection. Embodiments contemplated here use machine-learning to improve the correlation process between the measurement data of airborne particles with required maintenance detection. 
     A typical cluster tool may have tens of thousands of individual parts. A subset of those parts may require regular or occasional maintenance, including manual cleaning, “auto cleaning,” repair, refurbishment, calibration, replacement, modification, upgradation, resurfacing, etc. In some circumstances, these parts tend to be subject to process conditions, such as plasma, electric fields, magnetic fields, high or low temperatures, high or low pressures, exposure to reactants, exposure to ultraviolet (UV) radiation, exposure to high velocity ions, etc. These components may include, for example, a substrate support, electrodes, chamber walls, showerheads, chucks, exclusion rings, wafer contact supports, etc. Additionally, some of these parts are subject mechanical wear, for example from friction due to angular or linear movement and from physical deformation. These components may include bearings, motors, O-rings, gaskets, door seals, pistons, etc. These conditions or physical changes to component properties may generally be referred to degradation, or deterioration, or diminution. 
     One of the primary markers or indications of component degradation, and thus maintenance, is the release or sublimation of particles into a gaseous state. This is especially true under vacuum conditions, where the energy required to sublimate is reduced compared to atmosphere. When a component such as a seal is repeatedly subject to either process conditions or friction or deformation, the component experiences enough “wear and tear” to cause its underlying intermolecular, supramolecular, or even composition to change. These molecular changes typically result in the separation of particles of the underlying monolithic part at the surface at which the part contacts open space. It is from this surface that individual particles sublimate or otherwise become airborne. As a result, airborne particles are indicative of wear in a part as well as its need from maintenance. 
     Tool monitoring such as by detecting these airborne particles have hitherto presented certain challenges. From an architectural standpoint, cluster tools are designed to occupy as small a footprint as practicable because cluster tools are operated in clean rooms, the space of which is valuable. Moreover, because of the dynamic nature of the temperature and pressure of processing modules (PMs), the internal volume of cluster tools is also kept to a minimum to reduce the time and expense it takes to, for example, achieve a certain temperature, pressure, or concentration of a reactant within a chamber. Thus, there very little extraneous space within a cluster tool to place monitoring equipment such as an optics-based or light-based sensor. 
     Furthermore, it is time consuming, inefficient, and expensive to routinely and manually inspect the cluster tool, such as the vacuum transfer module (VTM), the load lock (LL), the atmospheric transfer module (ATM) and the equipment front end module (EFEM) because doing so would require halting any processing. Additionally, manual inspection may require some extent of disassembly of the tool, resulting in even more down time. Moreover, such manual inspections may often unnecessarily halt operations or otherwise fail to detect maintenance issues until they result in defects in substrate processing. A method and system of in-situ monitoring of tool health and detecting maintenance needs is provided, enabling an operator to identify and address the maintenance need prior to failure. 
       FIG. 1  depicts a semiconductor processing system  100  illustrating various chambers of which a sensor  101  provides in-situ health monitoring, according to one embodiment. Semiconductor processing system  100  is shown to include an equipment front end module (EFEM)  102  that an operator  112  can interface with via a computer  103  in a clean room, for example. The EFEM  102  may also interface with other semiconductor processing modules via one or more robots. A cassette  114  carrying a plurality of substrates  116  is shown to be transferred to the EFEM  102 . The ATM  104  operates at atmosphere and is configured to transfer one of the substrates  116  from cassette  114  to the load lock (LL)  106 . The ATM  104  positioned inside of the EFEM  102  and is equipped with at least one robotic arm  118  for performing such transfers between the LL  106  and the EFEM  102 . 
     When the ATM  104  transfers the substrate  116  to the LL  106 , the robotic arm  118  places the substrate  116  on a support  120  while an EFEM-facing door  130  is open. The EFEM-facing door  130  is then closed prior to pumping down the pressure in the LL  106  via pump  128  to match a vacuum of the vacuum transfer module (VTM)  108 . Once the LL  106  is at vacuum, a VTM-facing door  132  is opened and a robotic arm  122  of the VTM  108  transfers the substrate  116  from the support  120  to a support  124  of the processing module (PM)  110 . By way of example, the PM  110  may be implemented to perform plasma etching, chemical vapor deposition (CVD), atomic layer deposition (ALD), ion beam etching (IBE), sputtering, among many others. 
     When the substrate  116  is finished processing, the robotic arm  122  of the VTM  108  retrieves the substrate  116  from the PM  110  and places it back on the support  120 . With both the VTM- and ATM-facing doors  130  and  132  closed, the LL  106  is vented to atmosphere via vent  126 . Once at atmosphere, the EFEM-facing door  130  is opened so that the robotic arm  118  may retrieve the substrate  116  from the LL  106  and place it back onto the cassette  114  or some other storage structure. More than one substrate  116  may reside in LL  106  at a time. For example, as substrate  116  is placed from the VTM  108  to the LL  106 , an unprocessed substrate  116  may be placed inside the LL  106 . Thus, the robotic arm  122  can place a processed substrate  116  into the LL  106  and remove an unprocessed substrate  116  from the LL  106  for processing sequentially. 
     A sensor  101  is shown to be associated with the semiconductor processing system  100  in proximity to EFEM-facing door  130  such that it is within the LL  106  and/or the ATM  104 , or is otherwise in fluid communication with the same. Other placements of the sensor  101  are contemplated and described with reference to  FIG. 7 . The sensor  101  is configured to measure properties of airborne particles that originate from the EFEM  102 , the LL  106 , the VTM  108 , and the PM  110 . The sensor  101  communicates with computer  103  for sending measurement data related to the airborne particle measurement. In some embodiments, the sensor  101  is configured to provide particle counts, particle size, and particle composition as measurement data to the computer  103 . The computer  103  likewise communicates with the sensor  101  to instruct the sensor  101  of when to perform measurements and when not to. In some embodiments, measurement will be taken by sensor  101  at atmosphere due to the impracticality of performing certain measurements at vacuum. In some embodiments, the sensor  101  may be a sensor array having a plurality of sensors that provide measurement data for particle count, particle size, particle composition, relative humidity (RH), temperature, pressure, oxygen levels, etc. This measurement data is further usable to narrow down, for example, which compartment or components within the compartment are in need of maintenance and the nature and extent of such maintenance. 
       FIG. 2  depicts a conceptual diagram of particle movement and subsequent detection by sensor  106  at stages  204 - 210  of the LL  106  during substrate transfer between the EFEM  102  and the VTM  108 , according to one embodiment. At stage  204 , particles  200  originating from the EFEM  102  are represented as relatively larger dots, while particles  202  of the VTM  108  are represented by smaller dots. This has been done for illustrative purposes only and the relative sizes of particles  200  and  202  are not intended to reflect a size of those particles in practice. Nor are the apparent densities of particles  200  and  202  representative of the concentration or particle count of said particles within their respective chambers. 
     The particle sensor  101  is shown to be in fluid communication with the LL  106 , for example, via a tube connected to a foreline associated with a pump. At stage  204 , the EFEM-facing door (or the ATM-facing door) is shown to be open so that, for example, a substrate may be placed into the LL  106 . Additionally, during stage  204 , particles  200  originating from the EFEM  102  may diffuse into the LL  106 . The change in concentration of particles  200 , φ, in the LL  106  as a result of such diffusion may be approximated by the following equations: 
     
       
         
           
             
               
                 
                   
                     
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     The change in concentration of particles  200  in the LL  106  as a result of diffusion is governed by the flux, J, of particles  200  into the LL  106 . Moreover, in equation (2), D is the diffusivity of particles  200 , which may itself depend upon multiple factors including the temperature and the size of the particles  200 . Thus, the concentration of particles  200  in the LL  106  at stage  204  is time-dependent. By knowing, for example, the amount of time the EFEM-facing door is open along with the particle count obtained by the sensor  101 , the concentration of particles originating from the EFEM  102  may be obtained. 
     Next, the LL  106  is evacuated by a pump to vacuum pressure similar to the vacuum of VTM  108 . At stage  206 , the particles  200  are shown to have been pumped out of LL  106  while both the VTM-facing door and the EFEM-facing door are closed. Because the LL  106  is at vacuum in stage  206 , the sensor  101  may be instructed to be off and may be sealed-off from the vacuum of LL  106  via a valve (not shown). 
     Next, the VTM-facing door opens in stage  208  such that the unprocessed substrate may be transferred from the LL  106  to the VTM  108 , while a processed substrate may be transferred from the VTM  108  to the LL  106 . Additionally, while the VTM-facing door is open, particles  202  originating from the VTM  108  may diffuse into the LL  106 . Particles  202  as shown in the VTM  108  may have a number of sources. For example, particles  202  may originate from the VTM  108  itself due to sublimation of particles from seals, bearings, motors, bushings, contact surfaces, etc. of various components associated with the VTM  108  such as the robotic arm, the VTM-facing door, or the PM doors. Additionally, particles  202  may originate from the PMs themselves as residual airborne particles that diffuse from the PMs after process when the PM doors are open. 
     Again, the concentration of particles  202  in LL  106  will depend upon the time that flux is allowed to occur, e.g., the VTM-facing door open time. Between stages  208  and  210 , the VTM-facing door is closed, and the LL  106  is vented to achieve atmospheric pressure. In some embodiments, nitrogen gas is used for venting. At stage  210 , content of the LL  106  is flown to the particle sensor  101  for detection of airborne particles originating from the VTM  108 . The concentration of the particles  202  from the VTM  108  may be extrapolated from the particle count data. The concentration of particle  202  in LL  106  may stay the same during venting because the number of particles within the volume of LL  106  should not change as a result of venting. 
     Between stages  210  and  204 , another cycle of pumping and venting may occur to clear out the particles  202  from LL  106 . However, in other embodiments, this step may not be necessary. For example, the EFEM-facing door may be opened to allow particles  200  to diffuse into LL  106  while also allowing particles  202  to diffuse out of LL  106 . During this period of gas exchange, the sensor  101  may be instructed to provide real-time particle count measurement data to determine if there is a net increase or decrease in particle count. If there is a net increase, this may indicate that EFEM  102  has a higher concentration of particles  200  than that of the VTM  108  of particles  202 . On the other hand, if there is a net decrease in particle count, this may indicate that the VTM  108  has a higher concentration of particles  202  than that of the EFEM  102  for particles  200 . 
       FIG. 3  depicts a table showing the various steps associated with an in-situ airborne particle monitoring and counting method, according to one embodiment. As noted above, at step 1, the wafer (unprocessed) is moved from the EFEM to the LL while the EFEM-facing door is open, and the VTM-facing door is closed. In some embodiments, step 1 may also include moving a processed wafer sitting in the LL to the EFEM prior or subsequent to the movement of the unprocessed wafer into the LL. In step 1, the sensor is on to count the particles traveling from the EFEM. In step 2, the wafer is in the LL with both the EFEM- and the VTM-doors closed while the LL pressure is pumped to vacuum. The sensor is either off or disconnected from fluid communication with the LL via a valve. This is because particle sensors do not generally perform accurately under high vacuum pressures due to lack of gas flow through the sensor channels. 
     At step 3, another processed wafer may be in the VTM for transfer into the LL while the unprocessed wafer moves from the LL to the VTM. Further, at step 3, there will be gas exchange between the LL and the VTM because some particles that diffused into the LL during step 1 may still be present in smaller amounts in step 3. Meanwhile, particles present in the VTM diffuse into the LL. As noted above, these particles may be of many origins, including from the VTM itself, or one of a plurality of PMs. Between steps 3 and 4, various processing procedures may be performed on the substrate at the PMs. The sequence of processing steps may generate particles that accumulate in the VTM during various transferring steps between the PMs. During processing, the VTM-facing door may be closed, and the LL may have undergone a venting and pumping cycle to transfer the processed wafer from the LL to the EFEM. 
     In any case, step 4 serves to transfer the now processed substrate from the VTM to the LL. In doing so, the airborne particles from the VTM that have accumulated over the span of one or more processing steps at the PMs will diffuse into the LL. The rate of diffusion into the LL will depend upon the diffusivity of the particles, which will depend upon the temperature, pressure, the mass of the molecules, the size of the molecules, the size of the opening of the VTM-facing door, and the volume of the LL. The time that the VTM-facing door is open will either be recorded, or, if it is predetermined, obtained from sequence data. Moreover, door open times and frequencies of opening will likewise be recorded for extrapolating particle origination from particle count and other measurement data. Additionally, the speed at which the doors open and close may be accounted for, as the area through which particles flux will change while the door is being opened or closed. 
     Steps 5 and 6 show that the airborne particles that migrated into the LL remain in the LL as the LL is vented to atmosphere. The concentration of those particles will likely not change due to venting because the volume stays constant. The airborne particles originating from the VTM accumulating during processing steps are then measured by the sensor. The cycle beings anew with step 7, where the processed wafer is transported from the LL to the EFEM in exchange for an unprocessed wafer. In step 7, the sensor may be on to determine the particle count change when the EFEM-facing door is open. At this stage, airborne particles originating from the VTM diffuse into the EFEM while those originating from the EFEM diffuse into the LL. As a result, there may be a net increase or decrease in particle count due to the gas exchange at step 7. In other embodiments, the LL may be pumped and vented for a cycle to remove the particles measured at step 6. 
       FIG. 4  shows depicts a semiconductor processing system  100  and various door open times used for correlating particle count  101 - 1  to a source of contamination for maintenance detection, according one embodiment. A substrate  116  is shown to be transferred between an EFEM  102  and a LL  106  via a robotic arm  118  of ATM  104  with end effector  118   a . The substrate  116  is further transported between the LL  106  and the PMs  400 ,  402 ,  404 , and  406  via robotic arm  122  with end effector  122   a . The semiconductor processing system  100  shows various doors that open and close during wafer transfer and processing, which may be referred to here as “door state.” That is, door state data denotes the timing of when various doors are opened or closed and for what duration. For example, an EFEM-facing door  130  is shown to be associated with an EFEM-facing door state  130   a , which is a function of time. In the period shown, the EFEM-facing door state  130   a  is shown to have been in an open state two times. Generally, door state data may be obtained through empirical measurements, or through sequencer programming data, which instructs when each of the doors should be opened or closed according to programming. Additionally, the speed at which a door opens or closes may be obtained from manufacturer data or through empirical measurement. 
     In any case, the EFEM-facing door state  130   a  may be used to calculate the flux of particles into the LL  106  from the EFEM  102 , for example, by solving equation (1) and equation (2). The same is true of VTM-facing door state  132   a . Also shown as part of the semiconductor processing system  100  are four PMs  400 ,  402 ,  404 , and  406 , each associated with PM doors  401 ,  403 ,  405 , and  407  for interfacing with the VTM  108 . Each of the PM doors  401 ,  403 ,  405 , and  407  are associated with PM door states  401   a ,  403   a ,  405   a , and  407   a . For example, PM 1 door state  401   a  shows that PM 1 door  401  opened and closed four times in the arbitrary span used. The amount of time the PM 1 door  401  was opened for each iteration is relatively shorter than, for example, the door open time of the VTM-facing door  132 . PM 2 door state  403   a  shows that PM 2 door  403  opened and close two times in the span used, while PM 3 door state  405   a  shows that PM 3 door  405  opened and closed three times. Further PM 4 door state  407   a  shows the PM 4 door  407  remained closed during the span described. 
     The door states for PM doors  401 ,  403 ,  405 , and  407  may be used to calculate the relative amount of gas exchange between each of the PMs  400 ,  402 ,  404 , and  406  and the VTM  108 . Thus, if particle count  101 - 1  reading is taken from airborne particles originating from the VTM  108  (e.g., at step 6 of  FIG. 3 ), the particle count may be proportionally attributed to the PMs  400 ,  402 ,  404 , and  406  depending upon the PM door states  401   a ,  403   a ,  405   a , and  407   a.    
     The flux will depend upon the amount of time that any of PM doors  401 ,  403 ,  405 , and  407  is open as well as the way or frequency in which they are open. For example, assume that PM 1 door  401  and PM 2 door  403  are open for the same total amount of time as shown in the PM doors states  401   a  and  403   a . Further assume that the concentration of a given particle within both of PMs  400  and  402  are substantially similar and that the relative concentrations of the particle in the PMs  400  and  402  and VTM  108  are such that equilibrium is not achieved for the door open times shown. There will likely be a greater extent of diffusion between PM  400  and the VTM  108  than that between PM  402  and VTM  108 , even though both have the same total amount of door open time. This phenomenon may be described Fick&#39;s first law equation (2). 
     As applied to the present situation, when diffusion begins after PM doors  401  and  403  open, the magnitude of flux depends upon the gradient of concentration between the VTM  108  and PMs  400  and  402 . Because the gradient will tend to decrease as diffusion occurs, the flux will also decrease over the period the doors are open. When a PM door closes, it allows the gradient to be reestablished through intra-compartment diffusion. As a result, the net diffusion into the VTM  108  from PM  400  may be greater than that of PM  402  under the conditions described, even when both PM door  401  and PM door  403  have the same total open time. It is thus contemplated that PM door states  401   a ,  403   a ,  405   a , and  407   a  are to be used to in addition to total door open time for correlating the origin of given particles when particle measurements are made via sensor  101 . 
       FIG. 5  shows a maintenance detection module  500  of computer  103  using various door state data  503  and various sensor data  505  for detecting that a component within a substrate process system needs maintenance, according to one embodiment. The computer  103  is shown to have door state data for each of the VTM-facing door, the EFEM-facing door, as well as each of the PM doors. The door state data  503  may be converted to flux data  501 , which describes the extent of diffusion between two compartments given the nature and extent of the door state data  503 . The flux data  501  is inputted into the maintenance detection module  500 . Additionally, sensor data  505  may be gathered in real time for a number of properties. For example, one or more of the particle counts, volatile organic compound (VOC) measurements, relative humidity (RH) measurements, temperature and pressure measurements, and the oxygen level measurements may be obtained at from sensor  101 . 
     This data is inputted into the maintenance detection module  500  for determining, for example, a profile of the particles detected, including the particle count and size, whether the particles are organic or inorganic, etc. From this data, the maintenance detection module  500  may be able to classify the source of contamination as an oil leak, a seal degradation, or a process chemical residue, etc. Additionally, from the door state data  503 , the maintenance detection module  500  determines the proportion of the particle profile that is attributable to each of the various compartments, e.g., the VTM, the EFEM, and PMs 1-4. Thus, the maintenance detection module  500  is able to determine the source of the airborne particles senses as well as the type of maintenance that is most beneficial. For example, in  FIG. 5 , the maintenance detection module  500  generates a prompt  502  that part x of PM 2 requires replacement within 10 days. 
     In conjunction with the machine detection module  500 , a machine-learning module  504  is also contemplated to be used to predict with greater accuracy and confidence the component needing maintenance and that nature and extent thereof. The machine-learning module  504  may be implemented by computer  103  or it may be implemented at a remote server that communicates with computer  103 . In any case, the machine-learning module  504  may use a supervised learning algorithm that detects features associated with the various door state data  503  and the sensor data  505  for classification by a maintenance detection model. The machine-learning module  504  may be provided with training data that provides ground truth relative to what features a set of sensor data and door state data will have when various components are in need of replacement, for example, due to wear and tear, degradation, contamination, deterioration, etc. 
     Any suitable machine-learning algorithm may be implemented to meet the needs of the operator and the machine-learning module  504 . Some may include, but are not limited to a Bayesian network, linear regression, decision trees, neural networks, k-means clustering, and the like. The machine-learning is contemplated to be “supervised” because when the operator is prompted for some maintenance, the operator may then evaluate the accuracy of the machine-learned prompt while performing the maintenance. If the prompt is accurate, the operator may label the prompt “true,” and if the prompt is not accurate, the operator may label the prompt as “false,” along with any notations indicating his or her findings as to the actual need for maintenance. In this fashion, the machine-learning algorithm may learn from the operator of a particular machine, and if the machine-learning algorithm is distributed across many computers  103 , the machine-learning algorithm may learn from thousands of semiconductor processing machines and operators. In certain embodiments, the maintenance detection module  500  and or the machine-learning module  504  may be provided as a service to operators of semiconductor processing system. 
       FIG. 6  shows a method for identifying the source of airborne particles and for detecting a maintenance need of a semiconductor processing system via in-situ sensors, according to one embodiment. In operation  600 , the method provides for pumping down the load lock containing a substrate to a first pressure to match a pressure of a VTM, the first pressure being a high vacuum in many circumstances. Once the load lock is at vacuum, operation  602  serves to open the VTM-facing door of the load lock such that the substrate may be transferred to the VTM. The VTM-facing door may then be closed. A robot arm of the VTM proceeds to place the substrate into one of a plurality of PMs for processing, including, but not limited to, deposition via physical vapor deposition (PVD), chemical vapor deposition (CVD), atomic layer deposition (ALD), electrochemical deposition (ECD), plasma etching by transformer-coupled plasma (TCP) or inductively coupled plasma (ICP), ion beam etching (IBE), lithography, and sputtering. During these processes, doors for the PMs will open and close and allow any airborne particles originating from the PMs to accumulate into the VTM. The composition of the airborne particles within the VTM will depend upon the door states of the PMs. PMs that have a higher average of open time will contribute more to the composition than PMs with lower averages of open times. 
     When the substrate is finished processing, operation  606  serves to open the VTM-facing door such that the substrate may be retrieved from the VTM to the LL. During the period while the VTM-facing door is open, the airborne particles that have accumulate in the VTM will have an opportunity to diffuse into the LL. In some embodiments, the contents of the VTM may have time to homogenize prior to the opening of the VTM-facing door. In any case, the VTM-facing door state data is captured either empirically or from a sequencer of the computer. The sequencer will contain data related the number of times each of the doors is open and closed. The door open/closed time may be approximated by prior calibration or through manufacturer data. 
     Operation  608  serves to capture those airborne particles that have diffused into the LL by closing the VTM-facing door. Additionally, operation  608  serves to vent, or re-pressurize the LL to a second pressure, the second pressure being atmosphere in many embodiments. Further, operation  608  serves to obtain measurement data of the contents of the LL, which should include any airborne particles that accumulated in the VTM during processing and that diffused into the LL during transfer of the wafer. At operation  610 , the method provides for processing the measurement data obtained from the sensor and the door state data for identifying one or more of the PMs, or the LL, or the VTM for which maintenance would be desired. In some embodiments, the LL may be separately measured by, for example, pumping out any airborne particles originating from the EFEM or the VTM and subsequently venting the LL. Any airborne particles within the LL is likely to be from contamination of the LL itself. Those airborne particles may be measured with the sensor in a similar manner as in operation  608 . The method is thus able to generate a prompt in operation  612  that apprises the operator that one or more of the PMs, the LL, or the VTM need attention. In some embodiments, the prompt generated at operation  612  may be for auto-cleaning on one of the VTM, the LL, or the PMs. Additionally, the prompt in operation  612  may specify the type of maintenance predicted to be desirable. For example, the prompt may specify that there is O-ring degradation, an oil leak, lip seal leak due to mechanical or chemical wear. 
       FIG. 7  depicts an interface  700  where the EFEM  102  and the LL  106  meet and various locations for placement of sensors  101   a - f  for real-time tool health monitoring, according to one embodiment. Within the load lock  106 , an outlet  708  is shown at the lower horizontal surface  106   a  to connect the LL  106  to a pump  128  for pumping down the LL  106  down to vacuum. A foreline  710  and a valve  712  are disposed between the outlet  708  and the pump  128 . One of the particle sensors  101   a  is shown to be connected to the foreline  710  via line  714  having a valve  716 . A pressure transducer  708  is physically connected to the LL  106  and sends a signal to the particle sensor  101   a . During operation, when the pump  128  is on and valve  712  is open, valve  716  is closed and particle sensor  101   a  is off. In this manner, only contents from the LL  106  are evacuated by pump  128 . Subsequently, when the LL  106  achieves atmosphere by venting, a pressure transducer  718  may detect the pressure in the LL  106  or the foreline  710  via line  714  and open valve  716 . This allows contents from the LL  106 , which is at atmosphere, to travel into the particle sensor  101   a . The particle sensor  101   a  may be equipped with a fan to induce flow of the contents of the LL  106  into the particle sensor  101   a . In this configuration, airborne particles present in the LL  106  may be measured fairly directly because the airborne particles are flown into the particle sensor  101   a . However, airborne particles originating from the EFEM  102  may require time to diffuse into the LL  106  for measurement by sensor  101   a.    
     Particle sensor  101   b  is shown to be located inside of LL  106  at an upper horizontal surface  106   b , although the particle sensor  101   b  may also be placed on the lower horizontal surface  106   a , or a vertical surface  106   c . In this configuration, the particle sensor  101   b  may be configured to operate when the pressure inside the LL  106  is above a certain threshold. In some embodiments, measurement data from the particle sensor  101   b  may not be reliable under a certain threshold pressure. In some embodiments, the particle sensor  101   b  will include a wireless transmission module to communicate measurement data to the computer. Any suitable wireless transmission bandwidth or protocol may be used for this purpose. 
     Particle sensor  101   c  is shown to be mounted to an upper surface  130   a  of EFEM-facing door  130  such that it is kept at atmosphere and obtains measurement data of the EFEM  102  when the EFEM-facing door  130  is closed and obtains measurement data of the LL  106  when the EFEM-facing door  130  is open. In some embodiments, the particle sensor  101   c  is directional in the sense that it has an opening facing some direction. In certain embodiments, the opening of the particle sensor  101   c  may face toward the LL  106 , or the EFEM  102 , or may have an opening that faces both of the aforementioned. In another embodiment that is not shown, the particle sensor may be mounted on a vertical surface  130   b  of the EFEM-facing door  130 . As with particle sensor  101   b , particle sensor  101   c  may include a wireless communication module to transmit and receive data to and from the computer. 
     Particle sensor  101   d  is shown to be mounted to the vertical outer surface  106   d  immediately above a door opening  703  of the LL  106 . The positioning of the particle sensor  101   d  is such that airborne particles present in the LL  106  need not diffuse very far before being picked up by the sensor  101   d . Particle sensor  101   e  is in fluid communication with the EFEM  102  via an outlet  701  and a valve  705 . The outlet  701  may be defined from an upper horizontal surface  102   a  of the EFEM  102  or may be defined from a lower horizontal surface  102   b . Particle sensor  101   e  may be equipped with a fan to flow the airborne particles from the EFEM  102  and the LL  106  into the particle sensor  101   e.    
     Particle sensor  101   f  may be placed inside the EFEM  102  at the mouth of an equalization port  707  of an equalization channel  702 . The equalization channel  702  ensures that the pressure between the LL  106  and the EFEM  102  are similar before opening EFEM-facing door  130 . The particle sensor  101   f  may be connected to the equalization port  707  for ease of use. 
       FIG. 8  shows experimental data gathered from various particle sensor locations under various circumstances, according to one embodiment. Data points are for particles of 300 nm or greater in diameter of a laser-based particle counter. Measurement period  800  is performed at a location within a clean room and results in relatively high particle counts. Subsequent measurement periods  802  and  804  show that particle counts within the EFEM and in front of the LL (e.g., sensors  101   c  and  101   d ) are significantly lower that within the clean room in general. When the EFEM-facing door of a clean LL is cycled open and closed, the measurement period  806  shows an increase in particle count as compared to that of measurement period  804  when the LL with the EFEM-facing door is closed. Some of the increase in particle count may be due to increase air flow around the particle sensor resulting from the door opening and closing, as well as gas exchange between the LL and the EFEM. In measurement period  808 , the particle sensor is kept in front of a LL that is contaminated while the EFEM-facing door is cycled opened and closed. Particle counts are measured to be at least twice that of the prior measurement period  806 , indicating that a contaminated LL is detectable using experimental methods. 
       FIG. 9  shows an overall flow of a method for automatically determining if a maintenance procedure is recommended based on sensor measurement data, according to one embodiment. At operation  900 , the LL is pumped down to a vacuum pressure to match a pressure of a VTM while both an EFEM-facing door and a VTM-facing door are closed. The LL interfaces with an EFEM via the EFEM-facing door and with the VTM via the VTM-facing door. In operation  902 , the VTM-facing door is opened such that airborne particles in the VTM are allowed to diffuse into the LL. The airborne particles generally originate from the VTM or one or more of the plurality of PMs, although some, in theory, could originate from the LL and the EFEM. In operation  904 , the VTM-facing door is closed so that the LL may be vented to atmosphere. This is typically done with nitrogen, although other gases are possible. Without first opening the EFEM-facing door, the airborne particles are subject to measurement by a sensor that is in fluid communication with the LL. This is so that the airborne particles measured will be known to be from the VTM and not from the EFEM. In other embodiments, the EFEM-facing door may be opened prior to making the measurements using the sensor. In operation  908 , a maintenance detection module is used to determine that a maintenance procedure is recommended for one of the LL, the VTM, or one of the plurality of PMs or some combination thereof. This maintenance procedure recommendation is based on the measurement data may also be based on door state data. The maintenance detection module may also identify which of the LL, the VTM, or which of the plurality of PMs are to be recommended for the maintenance procedure. The method shown in  FIG. 9  is contemplated to be run for each cycle of pumping and venting such that real-time monitoring of tool health is achieved. In other embodiments, the pumping and venting may be performed intermittently, for example, after every 5, 10, 20, 50 or 100 or more cycles of pumping and venting. 
     Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications can be practiced within the scope of the appended claims. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the embodiments are not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.