KR20120037421A - Arrangement for identifying uncontrolled events at the process module level and methods thereof - Google Patents

Abstract

A method is provided for detecting an in situ fast transient event in a processing chamber during substrate processing. The method includes a set of sensors comparing the data set with a reference set (in-situ fast transient event) to determine whether the first data set includes a potential in-situ fast transient event. If the first data set includes a potential in situ fast transient event, the method also includes saving an electrical signature that occurs during the period of the potential in situ fast transient event. The method further includes comparing the electrical signature against a set of stored arc signatures. The method also classifies the electrical signature as a first incidence fast transient event, if a match is determined, and determines a severity level for the first incidence fast transient event based on a predefined set of threshold ranges. More to do.

Description

An apparatus and method for identifying uncontrolled events at the process module level {ARRANGEMENT FOR IDENTIFYING UNCONTROLLED EVENTS AT THE PROCESS Modules

Improvements in plasma processing have provided growth for the semiconductor industry. To compete, manufacturers need to be able to process substrates with quality semiconductor devices. In general, precise control of process parameters is required to achieve satisfactory results during substrate processing. If processing parameters (eg, RF power, pressure, bias voltage, ion flux, plasma density, etc.) fall outside of a predefined window, unwanted processing results (eg, inferior etch profile, low Selectivity, damage to the substrate, damage to the processing chamber, etc.) may occur. Thus, the ability to identify states when processing parameters are outside a predefined window is important for the fabrication of semiconductor devices.

During substrate processing, certain uncontrolled events may occur that may damage the substrate and / or damage processing chamber components. To identify uncontrolled events, data can be collected during substrate processing. A monitoring device such as a sensor may be employed to collect data for various process parameters (such as bias voltage, reflected power, pressure, etc.) during substrate processing. As described herein, a sensor refers to a device that can be employed to detect the states and / or signals of a plasma processing component. For ease of explanation, the term "component" can be used to mean an assembly of atomic or multiple components within a processing chamber.

The type and amount of data being controlled by sensors has recently increased. By analyzing the data controlled by the sensors with respect to process module data and process context data (chamber event data), parameters outside the predefined window can be identified. Thus, a corrective action (such as recipe adjustment) can be provided to stop uncontrolled event (s), thereby preventing further damage to the substrate and / or processing chamber component. Can be.

The invention is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like reference numerals refer to like elements.
1 illustrates an overall logic diagram of a prior art of an interconnect tool environment with a host level analysis server.
2 shows a simple block diagram of an interconnect tool environment with a cluster tool level solution for correlating data between a sensor and a process model controller.
3 shows a simple logic schematic of a process-level troubleshooting architecture, in one embodiment of the invention.
4 shows a simple functional diagram of a process module level analysis server, in one embodiment of the invention.
5 shows a simplified diagram of a microarking event.
6A and 6B show a simple block diagram of a processing environment in embodiments of the present invention.
FIG. 7 illustrates a simple flow chart in one embodiment of the present invention illustrating how a fast sampling transient detection algorithm detects a real time fast transient event in a manufacturing environment that is not part of an analysis module.

The invention will now be described in detail with reference to several embodiments of the invention shown in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without some or all of these specific details. In other instances, well known process steps and / or structures have not been described in order to avoid unnecessarily obscuring the present invention.

Hereinafter, various embodiments are described, including methods and techniques. It should be noted that the present invention may also include an article of manufacture including a computer readable medium having computer readable instructions stored thereon for carrying out embodiments of the present technology. Computer readable media may include, for example, semiconductor, magnetic, magneto-optical, optical or other forms of computer readable media for storing computer readable code. In addition, the present invention may also include apparatuses for practicing embodiments of the present invention. Such an apparatus may include dedicated and / or programmable circuits for performing tasks relating to embodiments of the present invention. Examples of such apparatus include a general purpose computer and / or a dedicated computing device, if properly programmed, and include a combination of a computer / computing device and / or a dedicated / programmable circuit configured for various tasks in accordance with embodiments of the present invention. It may also include.

As mentioned above, to be competitive, manufacturers must be able to efficiently and effectively mediate problems that may arise during substrate processing. Arbitration generally involves analyzing the excess data collected during processing. For ease of explanation, FIG. 1 shows an overall logic diagram of the prior art of an interconnect tool environment with a host level analysis server.

For example, consider the situation where manufacturers may have one or more cluster tools (such as etch tools, cleaning tools, strip tools, etc.). Each cluster tool may have a plurality of processing modules, each processing module configured for one or more specific processes. Each cluster tool may be controlled by a CTC such as a cluster tool controller (CTC) 104, a CTC 106, and a CTC 108. Each cluster tool controller may interact with one or more PMCs, such as process module controllers (PMCs) 110, 112, 114, and 116. For ease of explanation, examples in connection with the PMC 110 are provided.

In order to identify states that may require intervention, sensors may be employed to collect data about the processing parameters (sensed data) during substrate processing. In one example, during substrate processing, a plurality of sensors (such as sensors 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, and 140) are configured for one or more processing parameters. Interact with the process module controller to collect data. The type of sensors that may be available may depend on the type of data that can be collected. For example, sensor 118 can be configured to collect voltage data. In another example, sensor 120 can be configured to collect pressure data. In general, sensors that can be employed to collect data from a process module can be of different brands, make and / or modules. As a result, the sensor may have little or no interaction with other sensors.

In general, the sensor is configured to collect measurement data for one or more specific parameters. Since most sensors are not configured to perform processing, each sensor can be connected to a computing module (such as a computer, user interface, etc.). The computing module is generally configured to process analog data to convert raw analog data into a digital format.

In one embodiment, sensor 118 collects voltage data from sensor PMC 110 via sensor cable 144. Analog voltage data received by sensor 118 is processed by computing module 118b. Data collected by the sensors is sent to a host level analysis server (such as data box 142). Before transferring data towards data box 142 via a network connection, the data is first converted from analog format to digital format by the computing module. In one embodiment, the computing module 118b converts the analog data collected by the sensor 118 into a digital format before transmitting data to the data box 142 via the network path 146.

Data box 142 may be a centralized analysis server configured to collect, process, and analyze data from a plurality of sources, including process modules and sensors. In general, one data box may be available to process data collected during substrate processing by all of the cluster tools of a single manufacturer.

The actual amount of data that can be delivered to the data box 142 can be significantly less than the amount collected by the sensors. In general, sensors can collect large amounts of data. In one embodiment, the sensor may collect data at rates up to 1 megabyte per second. However, only a portion of the data collected by the sensors is sent to data box 142.

One reason for not sending the entire data streams collected by the sensors to the data box 142 is due to network bandwidth limitations when using cost-effective and commercially available communication protocols. The network pipeline to data box 142 is sent to a single receiver (such as data box 142) (sensors 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, Unable to process large volumes of data from multiple sources (such as 138 and 140). In other words, the network path between the sensor devices (sensor and computing module) and the data box 142 may experience more traffic congestion when the data box 142 attempts to receive a large amount of data originating from all sensor devices. Can be. As can be appreciated from the above description, if the data box 142 cannot handle incoming traffic, the data packet in transit may be missing and may need to be retransmitted, thereby congesting the already heavily congested network pipe. Additional burden may be placed on the line.

In addition, data box 142 may not be able to process the high volume of data coming from multiple sources, while also performing other important functions such as processing and analyzing the data. As described above, the data box 142 is not only configured to receive incoming data packets, but the data box 142 is also configured to process or analyze all of the incoming data streams, for example. Since data box 142 is an analysis server for the different data streams being collected, data box 142 needs sufficient processing power to perform analysis on excess data streams.

Because data box 142 has limited processing resources, only a portion of the data collected from each sensor is sent to data box 142. In one embodiment, of the thousands of data items that may be collected by a single sensor, only 10-15 data items at 1-5 hertz may be delivered to the data box 142. In one embodiment, only a summary of the data collected by the sensor 118 may be sent to the data box 142.

In addition to receiving data from the plurality of sensors, data box 142 may also receive data from a process module controller. In one embodiment, process module data and process context data (chamber event data) may be collected by each process module controller and delivered to data box 142. For easy explanation, process module data and process context data may also be referred to as process module and chamber data. For example, process module data and process context data may be collected by PMC 110 and sent to CTC 104 via path 148. The CTC 104 not only manages data from the PMC 110 but also processes data from other processing module controllers within the cluster tool (such as the PMC 112, the PMC 114, and the PMC 116). It may be.

The data collected by the cluster tool controller is then sent to the semiconductor equipment fab host 102 via the SEMS / GEM (semiconductor equipment communication standard / generic equipment module) interface. In one embodiment, the CTC 104 sends data collected from the PMCs 110, 112, 114, and / or 116 via the path 150 via the SECS / GEM 156 to the fab host 102. do. Fab host 102 may not only receive data from CTC 104, but may also receive data from other cluster tool controllers such as, for example, CTCs 106 and 108. The data collected by the fab host 102 is then delivered to the data box 142 via the path 158. Due to the huge amount of data collected, not all data sent to the fab host 102 is delivered to the data box 142. In many cases, only a summary of the data may be sent to the data box 142.

Data box 142 can process, analyze, and / or correlate data collected by sensors and process module controllers. Once anomaly is identified, data box 120 may determine the source of the problem, such as, for example, a parameter that does not conform to the recipe step being performed at PMC 110. Once the source of the problem has been identified, the data box 142 can send an interdiction to the fab host 102 in the format of an Ethernet message. Upon receipt of the message, the fab host 102 can deliver the message to the CTC 104 via the SECS / GEM 156. The cluster tool controller may then relay the message to the intended process module controller, which in this embodiment is the PMC 110.

However, the prohibition signal is not normally provided in real time. Instead, a forbidden signal is typically received by the intended process module after the actuated substrate has been processed or even after the entire substrate lot has exited the process module. Thus, not only the substrate / substrate lot is damaged but also one or more processing chamber components can be adversely affected, thereby increasing waste and increasing cost of ownership.

One reason for the delay is that huge amounts of data have been received from excessive sources. Although data box 142 may be configured with a high speed processor and may have enough memory to process large volumes of data streams, data box 142 may still process, correlate, and / or collect all data collected. Or you may need time to analyze.

Another delay reason for receiving the inhibit signal by the process module is due to the unsafe data streams being received by the data box 142. Since data box 142 receives data from excessive sources, the actual data being transmitted to data box 142 may be significantly smaller than the data being collected. In one embodiment, instead of transmitting 1 gigahertz data collected by the sensor 118, only a portion of the data (about 1-5 hertz) is actually delivered. As a result, even if data box 142 is receiving high volume of data from all its sources, the data being received is usually unsafe. Thus, if data box 142 does not have access to the complete data set from all sources, determining uncontrolled events may take time.

In addition, the path that data is being sent to data box 142 can vary. For example, after analog data is converted into digital data, the data is transmitted directly from the sensor device (which is the sensor and its computing module). In contrast, data collected by the process module is transmitted over a longer network path (at least through the cluster tool controller and the fab host). Thus, data box 142 cannot complete the analysis until all relevant data streams have been received.

Not only is the network path between the process module and the data box 142 longer, but the data streams transmitted through this path typically face at least two bottlenecks. The first bottleneck is in the cluster tool controller. Since the data collected by the process modules in the cluster tool are sent to a single cluster tool controller, the first bottleneck occurs because data streams from several process modules must be processed through a single cluster tool controller. Given the enormous volume of data that can be transmitted from each process module, the network path to the cluster tool controller typically experiences heavy traffic congestion.

If the data was received by the cluster tool controller, the data is sent to the fab host 102. The second bottleneck may occur at the fab host 102. If fab host 102 can receive data from several cluster tool controllers, traffic to fab host 102 may also experience congestion due to the high volume of data being received.

Since data box 142 requires data from different sources to determine uncontrolled events, the traffic state between the process module and data box 142 prevents the delivery of data streams to data box 142. Prevent timely. As a result, previous time is lost before data box 142 combines all the data needed to perform the analysis. In addition, once the prohibition signal is ready, the prohibition signal must go back through the same length of path to the activated process module before the prohibition signal can be applied to perform the corrective action.

Another factor contributing to delay is the challenge of correlating data from multiple data sources. Since the data stream received by the data box 142 is typically a summary of the data collected from each sensor, and / or process module, the available data streams can be made at different time intervals to correlate the data. It can be a challenge. In one embodiment, the selected data streams sent from the sensor 118 to the data box 142 may be at one second intervals, while the data streams from the PMC 110 may be at two second intervals. As a result, correlating data streams may require time before an uncontrolled event can be determined decisively.

A further challenge of correlating data is due to the different paths that data is being sent to data box 142. When data is being transmitted through different computers, servers, etc., the data may be exposed to drift, network latency, network loading, and the like. As a result, data box 142 may be difficult to correlate data from several data sources. In order to quickly identify uncontrolled events, if precise correlation is needed, more analysis may be required to be performed before the uncontrolled events can be correctly identified.

Another disadvantage of the solution provided in FIG. 1 is the cost of ownership. In addition to the cost of maintaining the cluster tool system, there is an additional cost associated with the sensor device. Each sensor may be of a different brand / make / model, and each sensor device typically includes a sensor and a computing module. Physical space is usually required to house each of the sensor devices. Thus, the cost of housing the sensor device can be particularly expensive, particularly in areas where real estate costs can be high.

In order to reduce the actual time delay between the actual occurrence of uncontrolled events in the process module and the receipt of the inhibit signal by the process module, a cluster level analysis server is provided. 2 shows a simple block diagram of an interconnect tool environment with a cluster tool level solution for correlating data between a sensor and a process model controller.

Similar to FIG. 1, the cluster tool may include a plurality of process modules (such as PMCs 210, 212, 214, and 216). In order to collect data for analysis, each process module includes a plurality of sensors (such as sensors 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, and 240). Can be coupled to. Each sensor may interact with its corresponding process module controller via a sensor cable (such as sensor cable 244) to collect processing parameter data. The data collected by the sensor may be in analog format. Before delivering data to the cluster level analysis server (such as remote controller 242) via path 246, the computing module (such as computing module 218b) may process the data and convert it to a digital format.

Similar to FIG. 1, each process module controller may also send data (such as process module data and process context data) to a cluster tool controller (such as CTCs 204 and 206). In one embodiment, data collected by the PMC 210 may be sent to the CTC 204 via the path 248. The CTC 204 may also receive data from the PMC 210 as well as receive data from other processing module controllers (such as the PMCs 212, 214, and 216). The data received by the cluster tool controller is then communicated to the fab host 202 via the path 250.

Between the fab host 202 and the CTC 204, a serial tap can be connected to the network path 250 to copy the data being transferred to the fab host 202. In one embodiment, the serial tap 208 may intercept data being transmitted to the fab host 202 by the CTC 204. The data is copied and a copy of the data stream is sent to the remote controller 242 via the path 254. If a fab host is connected to more than one cluster tool controller, a dedicated remote controller is associated with the cluster tool controller for each cluster tool controller. In one embodiment, data being transmitted from CTC 206 to fab host 202 via path 252 may be intercepted by another serial tap 256. The data is copied and sent via the path 258 to a remote controller 260 that is different from the remote controller 242 associated with the CTC 204.

Thus, instead of a single data box processing all data from several cluster tools, multiple remote controllers may be available to process data from several cluster tools. That is, each cluster tool is associated with its own remote controller. Since each remote controller is processing data from a small number of data sources (such as process module controllers and sensors associated with a single cluster tool), each remote controller has a high volume of data from each source. Can be processed. In one embodiment, instead of 30 to 100 data items being transmitted, 40 kB to 100 kB data items at 10 hertz can now be received by each remote controller.

Data received from sensors and process module controllers is analyzed by a remote controller. If the problem is identified, the remote controller can send a prohibition signal to the cluster tool controller. In one embodiment, the remote controller 242 identifies the problem within the PMC 210. The inhibit signal is sent to the CTC 204 via the serial tap 208 via the paths 254 and 250. In this embodiment, upon receiving the inhibit signal, the CTC 204 forwards the inhibit signal to the intended process module controller, which is the PMC 210.

Since the remote controller is only responsible for processing data from one cluster tool instead of multiple cluster tools (as done by data box 142), more data can be analyzed and better correlation It can exist between different data sets. As a result, the remote controller can perform better and faster analysis, thereby providing a more timely prohibition signal to correct uncontrolled events in the processing module. In this embodiment, instead of receiving the inhibit signal to prevent the identified uncontrolled event from occurring on the next substrate lot (such as the inhibit signal provided by data box 142), for example, a remote controller The inhibit signal sent by 242 allows process engineers to obtain at least a portion of a substrate lot scheduled to be processed.

Although the remote controller solution is a better solution than the data box solution, the remote controller solution still relies on summary data to perform data analysis. As a result, problems that may arise during substrate processing may still be in an unidentified state. In addition, the path between the process module and the remote controller is still not a direct path. As a result, computer drift, network latency, and / or network loading can cause time discrepancy that can make it difficult for a remote controller to correlate data from a process module with data from a sensor.

Thus, even if the remote controller solution has increased the forbidden timeline, the remote controller solution is still inadequate. At best, it is only possible to prevent the problems experienced by the worked substrate from occurring during the processing of the next substrate. In the harsh competitive markets where cost is required to be minimized, downtime due to waste due to damaged substrates and / or damaged processing chamber components can be interpreted as market loss. Thus, there is a need for a real-time solution for identifying uncontrolled events.

In accordance with embodiments of the present invention, a process-level troubleshooting architecture (PLTA) is provided that performs arbitration at the process module level. Embodiments of the present invention include a PLTA that provides real-time analysis for real-time intervention. Embodiments of the present invention also include devices for load balancing and fault tolerance between sensors.

In one embodiment of the invention, the PLTA is a network system in which an analysis server communicates with a single processing module and its corresponding sensors. In one embodiment, the information exchanged within the network is bidirectional. In one embodiment, the analysis server may continuously receive process data from processing modules and sensors. Conversely, sensors can receive data from the processing module and the processing module can receive instructions from the analysis server.

For example, consider the situation in which the substrate is being processed. During the substrate process, a plurality of data can be collected. In one embodiment, data on pressure is collected every 100 milliseconds. If processing occurs in one hour, 36000 data items are collected for the pressure parameter. However, in addition to the pressure data, a plurality of other process data (eg, voltage bias, temperature, etc.) may also be collected. Thus, a significant amount of data is collected until the substrate process is complete.

In the prior art, an analysis server that can be configured to service data collected from a plurality of processing modules (such as remote controller 242 of FIG. 2), if not from a plurality of cluster tools (such as data box 142). Send data to Since data streams come from multiple sources, time is required to analyze and / or correlate the data. In addition, since prior art analysis servers cannot process and analyze all collected data, only a portion of the data collected from each source is sent to the analysis server. As a result, complex tasks of coordinating, processing, correlating, and / or analyzing data streams require time that may not always be readily available.

In one aspect of the present invention, the inventors have found that more accurate and faster analysis can be performed if more granular data is available for analysis. To analyze more data from a single source, the analysis server must analyze data from a few sources. In one embodiment, an apparatus for processing and / or analyzing data at a process module level is provided. That is, a process module level analysis server is provided for performing analysis on each process module and its corresponding sensors.

In one embodiment, the process module level analysis server includes a shared memory backbone that may include one or more processors. Each processor may be configured to interact with one or more sensors. In one embodiment, the data collected by sensor 1 may be processed by processor 1 and the data collected by sensor 2 may be processed by processor 2.

Unlike the prior art, processors may share processing power with each other to perform load balancing and fault tolerance. In the prior art, the computing module is configured to process data collected by the sensor. Since each computing module is a separate unit and does not always interact with each other, load balancing is not always performed. Unlike the prior art, a set of processors in a process module level analysis server may perform load balancing. In one embodiment, if processor 1 is experiencing data overload while processor 2 is receiving little or no data, processor 2 may be supplemented to support processor 1 in processing data from sensor 1. have.

In addition, in the prior art, if a computing module is malfunctioning because the computing modules tend to be of different brands / makes / models, other computing modules could not perform the processing performed by the malfunctioning computing module instead. . Unlike the prior art, the workload may be redistributed among the processors as needed. For example, if processor 2 is unable to perform its function, the workload may be redistributed to another processor until processor 2 is repaired. As can be appreciated from the above description, the processors eliminate the need for individual computing modules, thereby reducing the physical space required to house the computing modules.

In one embodiment of the invention, the processors may be divided into two types of processors, a primary processor and a secondary processor. Both the primary processor and the secondary processor are configured to process data from the sensors. In one embodiment, if secondary processor 1 is associated with sensor 1, secondary processor 1 typically processes only data from sensor 1. Similarly, if Secondary Processor 2 is associated with Sensors 2 and 3, Secondary Processor 2 typically only processes data from these two sensors 2 and 3.

In one embodiment, the shared memory backbone can include one or more primary processors. The set of primary processors may be configured to process data coming from the sensor as well as may be configured to process data coming from the processing module. In addition, the set of primary processors is configured to correlate data and perform analysis between various sources (such as sensors and processing module). If a prohibition is needed, the set of primary processors is configured to send a prohibition signal to the process module controller.

The features and advantages of the present invention can be better understood with reference to the following figures and description.

3 shows a simple logic schematic of a process-level troubleshooting architecture, in one embodiment of the invention. The manufacturer may have more than one cluster tool, but a single cluster tool is used as an example of one embodiment of the present invention. The cluster tool may have any number of processing modules, but the embodiment illustrated in FIG. 3 includes a single cluster tool with four processing modules.

The data collected by each processing module is collected by its corresponding processing module controllers (PMC 306, PMC 308, PMC 310, and PMC 312) to collect the cluster tool controller (CTC 304). Is transmitted to the fab host 302 via)). The data that can be transmitted by the PMCs can be the same type of data (process module data and process context data) previously transmitted in the prior art. Unlike the prior art, the data being sent to the fab host 302 does not depend on the processing module performing the arbitration. Instead, the data may be archived and available for future analysis.

In one embodiment, a process module level analysis server (APECS 314) is provided to perform the analysis required for mediation. Consider the situation where the substrate is being etched in the PMC 308. During substrate processing, sensors 316, 318, and 320 collect data from PMC 308. In one embodiment, sensor 316 is configured to collect voltage bias data from PMC 308. Analog data collected from the PMC 308 is transmitted to the sensor 316 via the sensor cable 328. Similarly, sensors 318 and 320 may collect data via sensor cables 330 and 332, respectively. The data collected by the sensors can then be sent to the APECS 314 via one of the paths 322, 324, and 326 for processing and / or analysis.

Unlike the prior art, data collected by the sensors need not be preprocessed (eg, as summarized) before being sent to the analysis server (APECS 314). In one embodiment, instead of causing the computing module to process the data, each sensor may include a simple data converter that may be employed to convert analog data into digital data before passing the data to the APECS 314. have. Alternatively, in one embodiment, a data converter, such as a field programmable gate array (FPGA), may be embedded within the APECS 314. In one embodiment, each processor may include a data converter algorithm for converting data into a digital format as part of the processing. As can be appreciated from the above description, by eliminating the need for computing modules, less physical space is required to house the cluster tool and its hardware. As a result, the cost of ownership can be reduced.

Since APECS 314 is dedicated to processing data only from one processing module and its corresponding sensors, APECS 314 can process higher volumes of data from a single source. That is, instead of gradually decreasing the volume of data transmitted from each sensor, APECS 314 is configured to process most of the data collected by each sensor, although not all. In one embodiment, at this time, instead of only 10-15 data items being sent for analysis, more than 2,000 data items from each sensor may be available for analysis by APECS 314. As a result, the data stream available for APECS 314 to process and analyze is a more complete data set.

In one embodiment, APECS 314 is also configured to process data coming from the processing module. Unlike the prior art, where data streams are transmitted over long lengths of data paths through multiple servers (e.g., cluster tool controllers, fab hosts, etc.) before being received by analysis servers (such as data boxes or remote controllers), The data collected in the process module is sent directly to the APECS 314 without having to go through another server. In one embodiment, process module data may be sent from PMC 308 to APECS 314 via path 334. If an uncontrolled event is identified, a forbidden signal may be sent directly to PMC 308 via path 336 without having to first proceed to other servers.

Additional details for the process module level analysis server are provided in FIG. 4. 4 shows a simple functional diagram of a process module level analysis server, in one embodiment of the invention. A process model level analysis server (such as APECS 400) may be assigned to each process module. APECS 400 is a bi-directional server and is configured to process incoming data and to send a forbidden signal when uncontrolled events are identified.

The data source can flow data collected by the sensors and data collected by the process module from two main sources. In one embodiment, APECS 400 is configured to receive incoming data from a plurality of sensors (sensors 410, 412, 414, 416, 420, 422, 424, and 426. If an amount of funds can already be invested in a conventional sensor device (sensor with a computing module), the APECS 400 draws data from both the conventional sensor device and change sensors (sensors that do not require a computing module). Configured to accept.

In one embodiment, APECS 400 may include an interface such as Ethernet switch 418 to interface with a conventional sensor device (such as sensors 410, 412, 414, and 416). In one embodiment, data collected by sensor 410 before digital data is sent to APECS 400 (via path 430, 432, 434, or 436) is first generated by computing module 410b. Converted from analog format to digital format. Ethernet switch 418 is configured to interact with conventional sensor devices to receive data streams. The data streams are then passed to one of the processors 402, 404, 406, and 408 in the APECS 400 (via path 446, 448, 450, or 452) for processing.

Instead of using a conventional sensor device to measure process parameters, a change sensor (a sensor without a computing module) can be employed. Since the collected data does not need to be summarized, the computing module no longer requires processing. Instead, in one embodiment, the change sensor may include a data converter (not shown), such as a low cost FPGA, to convert the data into an analog format to a digital format. Alternatively, instead of installing a data converter in the sensors, a data converter (not shown) may be installed in the APECS 400. Regardless of whether the data converter is installed externally or internally to the APECS 400, removal of the computing module provides cost savings in owning the cluster tool. In one embodiment, the cost of purchasing, housing, and managing the computing module is substantially eliminated.

In one embodiment of the present invention, APECS 400 includes a set of processors 402, 404, 406, and 408 to process incoming data. The set of processors may be a physical processing unit, a virtual processor, or a combination thereof. Each processor is responsible for processing data streams from sources associated with the processor. In one embodiment, data streams flowing from the sensor 422 through the path 440 are processed by the processor 404. In another embodiment, the data streams collected by the sensor 424 are sent to the processor 406 via the path 442 for processing.

The number of sensors and the relationship between the processor and the sensors may depend on the configuration of the user. In one embodiment, although FIG. 4 illustrates only a one-to-one relationship between processors and sensors, other relationships may exist. In one embodiment, the processor may be configured to process data from more than one source. In other embodiments, more than one processor may be configured to process data streams from one sensor.

In one embodiment, each processor shares a shared memory backbone 428. As a result, load balancing can be performed when one or more processors are overloaded. In one embodiment, if data streams flowing from the sensor 426 through the path 444 overwhelm the processor 408 processing capability, other processors may be assisted to reduce the load on the processor 408.

In addition to load balancing, the shared memory backbone also provides an environment for fault tolerance. In other words, if one of the processors fails to operate properly, the processing previously supported by the malfunctioning processor is redistributed to the other processors. In one embodiment, if processor 406 is not functioning properly and cannot process data streams from sensor 424, processor 404 may be instructed to process data streams from sensor 424. . Thus, the ability to redistribute the workload allows inadequately functioning processors to be replaced without incurring an outage for the entire server.

In one embodiment, two types of processors may exist within the APECS 400. The first type of processors is a secondary processor (such as processor 404, 406, or 408). Each secondary processor is configured to process data streams received from corresponding sensors. In addition, each processor is configured to, in one embodiment, analyze the data to identify any possible problems that may exist with the corresponding sensor (s).

The second type of processor is known as the primary processor 402. 4 illustrates only one primary processor, the number of primary processors may depend on the configuration of the user. In another embodiment, the primary processor can be configured to process data streams from one or more sensors. In one embodiment, data streams collected by sensor 420 are sent to primary processor 402 via path 438 for processing.

Another source of data for the primary processor is a process module. In other words, process module data and process context data collected by the process module may be processed by the primary processor. In one embodiment, the data collected by the process module is sent to the APECS 400 via the process control bus via the path 454. Thereafter, the data first traverses through the Ethernet switch 418 before flowing through the path 446 to the primary processor 402.

In addition to processing the data, the primary processor is also configured to analyze the data from multiple sources. In one embodiment, data correlation between data streams from sensors 422 and 424 is performed by primary processor 402. In another embodiment, data correlation between data streams from the process module and data streams from one or more sensors is also performed by the primary processor 402.

Since the data paths for each of the data sources are of approximately similar lengths, correlating the data presents significantly less challenge than experienced in the prior art. In one embodiment, because data flows from the process module to the APECS 400 without having to proceed through other servers (such as cluster tool controllers and / or fab hosts), the data stream from the process module is shown in FIGS. Computer and / or network conditions (such as computer drift, network latency, network loading, etc.) that could occur when data streams had to be transmitted through other servers (such as cluster tool controllers, fab hosts, etc.) as described in FIG. I don't experience the change caused by In addition, the waiting time for receiving all of the relevant data streams required to perform correlation and analysis is then significantly reduced. Thus, when external conditions (such as computer drift, network latency, network loading, etc.) have been significantly removed, the correlation of data from different sources is greatly simplified.

In addition to the data path, higher data volumes with higher granularity from a single source provide more data points to perform correlation, allowing for faster and more accurate analysis. In the prior art, since the analysis server of the prior art cannot process a high volume of data from an excess of data sources, the data used for analysis is usually incomplete and the correlation between the data sources is usually difficult. Unlike the prior art, the number of data sources is significantly reduced because each analysis server is then responsible for analyzing only data from a limited number of data sources (sensors associated with the process module and processor module). Since the number of data sources is significantly reduced, the analytics server has the ability to process higher volumes of data from a single source. If more finer details are provided, better correlation can be achieved between data streams from different sources.

If a problem is identified (such as an uncontrolled event), the primary processor is configured to send a prohibition signal to the process module. In one embodiment, a direct digital output line 456 is employed to send a forbidden signal from the APECS 400 to the process module. With a direct digital output line between the two devices, the inhibit signal does not have to be converted first to an Ethernet message before the inhibit signal can be sent. Thus, the time required to properly format the inhibit signal and convert the inhibit signal back is significantly eliminated. Thus, the APECS 400 may provide a real time prohibition signal or a near real time prohibition signal to the processor module to handle an uncontrolled event.

In another embodiment, the primary processor may also be configured to interact with other devices via path 458. In one embodiment, when the cluster tool controller sends a request to APECS 400, the request can be sent via path 458 and processed by primary processor 402. In another embodiment, notification to the fab host may be sent via path 458 and the cluster tool controller.

As can be seen from one or more embodiments of the present invention, a process level arbitration architecture is provided. By localizing the analysis server at the process module level, data granularity is provided for analysis, resulting in faster and more accurate analysis. Using similar data paths for different data sensors, better correlation exists between the different data streams. By faster and more accurate analysis, the prohibition signal provided in a timely manner provides corrective action to correct the uncontrolled event affecting the actuated substrate as well as corrective action to prevent the next substrate from being damaged. Intervention can be performed in a timely manner, thereby protecting the worked substrate from damage. Thus, only a small number of substrates can be discarded and damage to processing chamber components can be substantially reduced.

In another aspect of the present invention, the inventors detect real-time in situ detection of fast transient events (such as micro arcing events, dechucking events, spiking events, etc.) by a process level mediation architecture that can perform timely, fast and accurate analysis. We have found that we can identify and manage them. As described herein, fast transient events refer to events (such as micro arcing events, dechucking events, spiking events, etc.) that can occur conventionally and quickly during a short hold period during substrate processing. Due to the short time lengths and speeds at which each event can persist, identifying fast transient events has typically been done off-line after the entire substrate lot has been processed as much as possible.

In one embodiment, one or more substrates can be inspected using, for example, an optical metrology tool. However, this test does not provide real time detection. Instead, the substrate can be damaged as well as the rest of the substrate lot, for example, until a microarking event has been identified as occurring in the substrate. In addition, damage to hardware components within the processing chamber may also occur.

Recently, fast transient sensors have been developed that can allow fast transient electrical signatures (which are the result of fast transient events) to be captured. However, most high speed transient sensors do not have the ability to classify electrical signatures. That is, although fast transient sensors can collect data, fast transient sensors typically do not have the ability to classify data into important electrical signatures that can be employed to identify potentially harmful events.

For example, consider a situation in which electrical charge may accumulate during the etch process, causing micro arcing to occur. As described herein, micro arcing refers to an event that occurs when power dissipates quickly, which causes damage to the pattern on the substrate (such as breaking the layer, breaking the pattern, melted layer, etc.). . The VI probe can also be employed to collect data on microarking. However, most high-speed transient sensors, such as VI probes, lack the ability to interpret data and identify when high-speed transient events, such as microarcing events, occur.

Instead, the data collected by the fast transient sensor may have to be analyzed by a third party, such as a human, or by a software program. In one embodiment, a human user may have to analyze an excessive amount of data and make a determination (based on his or her experience) whether a fast transient event has occurred during substrate processing. Analyzing data can be done hourly or weekly. Even if data analysis is performed by a software program, analyzing millions of data samples can take time. By the time the problem is identified, damages to one or more substrates and / or hardware components of the processing chamber may have already occurred.

Detecting fast transient events, such as micro arcing events, can be a difficult task because micro arcing events are not typically predictable. That is, for example, microarking does not always occur for all substrates. In one aspect of the invention, the inventors have found that the electrical signature of the microarking event is predictable even when the timing of the microarking event is unpredictable. That is, each micro arcing event can be represented by a unique signature.

5 shows a simplified diagram of a microarking event (curve 502). As can be seen from curve 502, when an on wafer microarking event occurs, the voltage and current signals experience a sharp drop 504 simultaneously. Thereafter, the voltage and current signals undergo reverse decay because the voltage current signals gradually rise to a flat region 506, which may be at a different level than the point at which both drop.

In accordance with embodiments of the present invention, a method and apparatus are provided for processing a fast transient event, such as a microarking event, in a processing chamber of a plasma processing system. Embodiments of the present invention include a method for detecting a fast transient event (eg, microarking). Embodiments of the invention also include a method of classifying a fast transient electrical signature by performing a signature comparison with a known fast transient signature (such as an arc signature). Embodiments of the present invention further include a method of classifying the severity of a fast transient event. Embodiments of the present invention also further include a method of managing fast transient events to minimize damage during a real-time manufacturing environment.

In this publication, various embodiments may be described using microarking as an example. However, the present invention is not limited to micro arcing and may include any fast transient events that may occur during substrate processing. Instead, the description is meant as an example, and the invention is not limited to the described embodiments.

In an embodiment of the present invention, a method and apparatus for detecting a potential micro arcing event is provided. As described above, high speed transient sensors (such as VI probes) can be employed to perform high sampling rates (eg, collecting millions or billions of data points per second) to collect data during substrate processing. Can be. In one embodiment, for example, a fast sampling transient detection algorithm may be running while the VI probe collects data during substrate processing. In one embodiment, the fast sampling transient detection algorithm may include criteria to define a potential fast transient electrical signal. In one embodiment, to identify potential on wafer microarking events, the fast sampling transient detection algorithm may search for events in which both voltage and current signals drop simultaneously. In one embodiment, to identify potential chamber microarking events, a fast sampling transient detection algorithm may be employed to search for events spiked by both voltage and current signals.

In one embodiment, the fast sampling transient algorithm is coupled to a sensor controller (such as a VI probe controller), and a sensor (eg, VI probe) and provides an interface to the sensor (eg, VI probe) and provides a sensor (eg, a VI probe). For example, by a computing module configured to receive data from a VI probe). In another embodiment, the fast sampling transient algorithm is executed by a computing module that interacts with a sensor controller (eg, VI probe controller). In another embodiment, the fast sampling transient algorithm is executed by an analysis module that directly interacts with the sensor (eg, VI probe).

If a potential micro arc event is identified by a sensor (eg, a VI probe) or a computing module that interacts with the sensor (eg, a VI probe), in one embodiment, the voltage and current signals that occur near the event occurrence. Waveforms (eg, electrical signatures) may be stored and communicated to an analysis module, such as a process module level analysis server (eg, APECS 314) for analysis. In other words, by performing detection at the sensor level, only information about potential high-speed transient electrical signatures (such as microarking) is passed towards the analysis module for further analysis. Thus, instead of sending all the data to the analysis module for analysis, filtering may be performed to reduce the amount of data traffic transmitted along the data path, thereby reducing bandwidth requirements and reducing the processor capability of the analysis module. Decrease.

However, if a potential micro arcing event is identified by an analysis module that interacts directly with a sensor (eg, a VI probe), in one embodiment, data filtering is not required. Instead, an analysis module (such as APECS 314) that is part of the process level arbitration architecture may have a high speed processor capable of processing large volumes of data. Given its own advanced process level arbitration architecture, the general data traffic congestion that can occur in other types of analytic architectures can be substantially eliminated. As a result, the analysis module can analyze millions of data samples quickly and efficiently.

In one embodiment of the present invention, classification of potential fast transient electrical signatures may be performed. In one embodiment, once a waveform of a potential fast transient event is received by the analysis module, the analysis module may compare the potential fast transient electrical signature against a set of fast transient signatures (such as a set of arc signatures). In one embodiment, various known waveforms may be stored in the library, which may be examples of fast transient events, such as micro arcing.

In one embodiment, if the potential fast transient electrical signature matches one of the set of fast transient signatures stored in the library, the severity of the fast transient event can be determined. In one embodiment, fast transient events may be events that may have little or no impact on the substrate being processed. Thus, an event can be classified as an event with a low severity level. In another embodiment, fast transient events may be events that can damage on the current substrate being processed. Thus, a fast transient event can be classified as an event with a high severity level.

By identifying the severity of the fast transient event, one can make a decision about the best way to handle the fast transient event. In one embodiment of the present invention, a predefined course of action may be provided depending on the severity of the fast transient event. In one embodiment, a fast transient event with a low severity level may trigger an alert, while a fast transient event with a high severity level may result in the termination of an etching process, for example.

For ease of explanation, FIG. 6A shows a simple block diagram of a processing environment in one embodiment of the present invention. The processing system 600 can include a processing chamber 602 in which the substrate 604 is being processed. During substrate processing, a gas (not shown) may interact with the power (provided via the set of RF generators 606 through the set of match boxes 608) to generate a plasma for etching the substrate.

During substrate processing, if the accumulated electrical charge causes a fast transient event, the data may be collected by the VI probe 610 and identified by the fast sampling transient detection algorithm module 616. In one embodiment, the fast sampling transient detection algorithm module 616 may include criteria for defining a fast transient event. In one embodiment, the fast sampling transient detection algorithm module may be configured to execute during substrate processing.

In one embodiment, the collected data may be passed to the VI probe controller 612 along the set of paths 614. The VI probe controller 612 is configured to manage at least the VI probe 610. In one embodiment, the VI probe controller 612 can also include a fast sampling transient detection algorithm module 616.

In another embodiment, the fast sampling transient detection algorithm module 616 may be an independent computing module in communication with the VI probe controller 612. That is, data collected by the VI probe 610 may be sent to the fast sampling transient detection algorithm module 616 via the VI probe controller 612. By forming the fast sampling transient detection algorithm module 616 as an independent module, the VI probe controller 612 need not be changed even if the VI probe controller 612 cannot handle further processing.

In another embodiment, instead of sending data to VI probe controller 612, the data is routed from VI probe 610 via path 650 (as shown in FIG. 6B) to a fast sampling transient detection algorithm module (as shown in FIG. 6B). 616 can be sent directly to an analysis module 618 that can house it. The data collected by the VI probe 610 need not be preprocessed by sending the data directly to analyze the module 618. In addition, computing modules (such as VI probe controller 612) may be eliminated to reduce indirect costs of real estate. Instead, analysis module 618 may be employed to identify potential high speed transient electrical signatures.

If a potential fast transient electrical signature was detected based on a predefined criterion, the potential fast transient electrical signature can be classified by an analysis module 618 such as a process module level analysis server (eg, APECS 314). . In one embodiment, analysis module 618 may perform signature comparison by comparing potential fast transient signatures against a set of fast transient signatures stored within a library, such as an arc signature. If a match is identified, it is assumed that a fast transient event has occurred.

In one embodiment, the analysis module 618 is configured to determine the severity of the fast transient event. One skilled in the art knows that fast transient events can have different severity (eg, intensity) levels. Thus, an algorithm is provided for determining the severity of each fast transient event. In one embodiment, the severity level / critical range may be predefined or user configurable. As an example, a drop greater than 4 dB in the current or voltage signal and a hold period longer than 15 microseconds (defined as the period from drop to recovery) may be considered as appropriate thresholds for detection of damage on the wafer. .

If the severity level for the fast transient event has been classified, an action procedure may be applied. In one embodiment, action procedures may be predetermined and associated with a severity level / critical range. In one embodiment, the action procedure may be user configurable. In one embodiment, fast transient electrical signatures (such as microarking) with small voltage and current drops may be considered harmless and only a notification may be required to be sent to the operator. In another example, a fast transient electrical signature with a large voltage and current drop can be considered as an event with a high severity level and termination of the substrate process can be triggered.

FIG. 7 shows a simple flow chart illustrating how in one embodiment of the present invention a fast sampling transient detection algorithm detects a real time fast transient event in a manufacturing environment that is not part of an analysis module.

In a first step 702, substrate processing begins. For example, consider the situation where the substrate 604 is being processed within the processing chamber 602.

In a next step 704, substrate processing in the processing chamber is monitored. In step 704a, fast transient sensors, such as VI probes, can monitor electrical parameters (eg, voltage and current signals at different phases, fundamental frequencies, and high frequencies). At about the same time, in step 704b, a fast sampling transient detection algorithm may be executed.

In a next step 706, a determination is made of potential fast transient events. In other words, the fast sampling transient detection algorithm may include criteria to define potential fast transient events, such as, for example, microarking. If the data collected by the VI probe does not meet the criteria defined by the fast sampling transient detection algorithm, no potential fast transient event has occurred and the VI probe continues to monitor the substrate process (step 704).

However, once a potential fast transient event is identified, at step 708, the voltage and current waveforms may be stored near the occurrence of the potential fast transient event.

In a next step 710, the stored waveform is sent to the analysis module. In one embodiment, only data related to the occurrence of a potential fast transient event may be stored and transmitted. By sending only potential high-speed transient electrical signatures, resource leakage can be minimized. In addition, since preliminary processing was performed only by a sensor controller (such as a VI probe controller), the analysis module includes a high speed processor to analyze the data and to quickly classify and determine action procedures for potential fast transient events. It may not be necessary.

In a next step 712, signature comparison is performed by the analysis module. In one embodiment, the analysis module can compare the potential fast transient electrical signatures against the set of fast transient signatures. In one embodiment, a set of fast transient signatures can be stored in a library. In one embodiment, the library may also include non-fast transient signatures that allow correlation to be performed.

At a next step 714, a determination is made of the classification of the potential fast transient electrical signature. If the signature comparison results in no match being identified, then the potential fast transient electrical signature is not classified as the targeted fast transient electrical signature (step 716). In one embodiment, the potential high speed transient electrical signature may be discarded. In another embodiment, a potential high speed transient electrical signature may be added to the library as a new high speed transient electrical signature (step 718).

However, if the signature comparison results in that a fast transient electrical signature is identified, then at step 720, the severity of the fast transient event is determined. In one embodiment, the severity can range from a low value to a high value. In one embodiment, the severity may be based on a predefined set of threshold ranges. In one embodiment, a fast transient electrical signature may be added to the library (step 718). Step 718 is an optional step and is not required to detect real time fast transient events.

In a next step 722, an action procedure is determined. Once the severity level has been determined, an action procedure can be executed. In one embodiment, the action procedure may be predefined. In one embodiment, a fast transient electrical signature with a low severity level may trigger a notification to the operator. In another embodiment, a fast transient electrical signature with a medium severity level can trigger an alarm. In yet another embodiment, a fast transient electrical signature with a high severity level can trigger termination of the substrate process. As can be seen from the above description, the severity level and the action procedure associated with the severity level can be user configurable.

7 shows only an embodiment implementing a method for detecting real time fast transient events within a manufacturing environment. In another embodiment, the method may also be applied to detect a real time fast transient event in one embodiment where a fast sampling transient detection algorithm is part of the analysis module. In this type of environment, the execution of the fast sampling transient detection algorithm may be performed by an analysis module (such as APECS 314) instead of the VI probe controller. In one embodiment, the analysis module is a high speed processing computing module capable of processing high volumes of data. In one embodiment, the analysis module is directly connected with the sensor. The data is thus collected by the sensor and sent directly to the analysis module.

As can be seen from the above description, an apparatus and method are provided for detecting an in-situ fast transient event. In the prior art, detection of fast transient events is typically performed after substrate processing is complete for a substrate lot. In addition, complex metrology tools may be required to determine the presence of fast transient events. Since the presence of a fast transient event is unpredictable, each substrate in the substrate lot may need to be measured to determine the potential damage that can occur.

In contrast to the prior art, embodiments of the present invention provide for detection of fast transient events during substrate processing in real time, thereby minimizing damage to the remaining substrate lot and / or processing chamber. In addition, unlike the prior art, the detection process is an automated process that requires little or no human interference. Instead, the system is configured to automatically detect fast transient events if user configurable conditions / criteria / thresholds have been defined.

If a fast transient event (such as a microarking event) can be identified in real time within the manufacturing environment, the latency between the actual occurrence and the action procedure taken to manage the occurrence can be reduced. In the prior art, latency may take hours or even weeks. However, with the methods and / or apparatus described herein, the latency can be reduced to only milliseconds, thereby reducing the overall cost of ownership.

Although the present invention has been described in terms of several preferred embodiments, there are variations, modifications, substitutions and equivalents thereof that fall within the scope of the present invention. While various embodiments are provided herein, these embodiments are illustrative only and are not intended to limit the present invention.

Further, headings and summaries are provided herein for convenience and should not be used as binding the claims herein. In addition, the summary is written in a very short form and is provided here for convenience only and should not be taken as a limitation or limitation on the overall invention described in the claims. Although the term “set” is described herein, such term is intended to be generally understood in a mathematical sense including members greater than 0, 1, 1. It should also be noted that there are many variations of implementations of the apparatus and methods of the present invention. Accordingly, the following appended claims are intended to be interpreted as all such alterations, substitutions, and equivalents as falling within the scope and spirit of the present invention.

Claims (20)

A method of detecting in-situ fast transient events in a processing chamber of a plasma processing system during substrate processing, the method comprising:
Analyzing the first data set collected by the set of sensors, wherein the analyzing comprises comparing the first data set to a reference set that determines whether the first data set includes a potential in-situ fast transient event. And analyzing, the set of criteria defining a set of in-situ fast transient events;
If the first data set includes the potential in situ fast transient event, storing an electrical signature that occurs during a period in which the potential in situ fast transient event occurs;
Comparing the electrical signature against a set of stored arc signatures;
If a match is determined, classifying the electrical signature as a first incidence fast transient event; And
Determining a severity level for the first in situ fast transient event based on a predefined set of threshold ranges. The method of claim 1,
Analyzing the first data set comprises performing a fast sampling transient algorithm. The method of claim 2,
And the fast sampling transient algorithm is executed by a sensor controller. The method of claim 2,
The fast sampling transient algorithm is executed by a computing module, the computing module configured to be coupled to at least one of a sensor and a sensor controller. The method of claim 2,
And the fast sampling transient algorithm is executed by an analysis module configured to directly interact with one of the set of sensors. The method of claim 5, wherein
The analysis module is a process module level analysis server configured to perform analysis on each process module and a set of sensors associated with each process module. The method of claim 1,
And determining a course of action based on the severity level of the first in-situ fast transient event. The method of claim 1,
And the first in-situ fast transient event is a micro-arcing event. The method of claim 1,
And wherein said first data set is being collected by a fast transient sensor capable of performing a high sampling rate. The method of claim 1,
If the electrical signature does not match one of the set of stored arc signatures, the electrical signature is added to the library as a non-fast transient event signature. An apparatus for detecting an in-situ fast transient event in a processing chamber of a plasma processing system,
The processing chamber includes a plurality of sensors configured to collect data during substrate processing,
The device,
A fast sampling transient algorithm module configured to compare the data against a reference set and extract an electrical signature from the data, wherein the reference set defines a set of predefined in-situ fast transient events. module; And
An analysis module in direct communication with the fast sampling transient algorithm module,
The analysis module at least,
Receiving the electrical signature,
Comparing the electrical signature against a set of stored arc signatures,
Classifying the electrical signature as a fast transient event if a match occurs, and
And determine to determine a severity level for the fast transient event based on a predefined set of threshold ranges. The method of claim 11,
And a library configured to store the set of stored arc signatures. The method of claim 12,
And the library is configured to store a non-fast transient signature. The method of claim 11,
And the analyzing module is configured to send an action procedure directly to the process module controller when the fast transient event is identified during the substrate processing. The method of claim 11,
And the analysis module is further configured to determine an action procedure based on the severity level of the fast transient event. The method of claim 11,
And the fast transient event is a micro arcing event. The method of claim 11,
And the fast sampling transient algorithm module is controlled by an analysis module configured to directly interact with a plurality of sensors. The method of claim 11,
And the analysis module is a process module level analysis server configured to perform an analysis on each process module and a set of sensors associated with each process module. The method of claim 11,
And the fast sampling transient algorithm module is controlled by a sensor controller. The method of claim 11,
And the fast sampling transient algorithm module is controlled by a computing module, the computing module configured to be coupled to at least one of a sensor and a sensor controller.

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