Method for eliminating early wafer effects in semiconductor process chambers - Patents.com

Abstract

Embodiments disclosed herein include a method for monitoring chamber performance to mitigate first wafer effects. In one embodiment, the method for determining optimal chamber conditions includes monitoring parameters while running a recipe for several substrates after the chamber has been idled. In one embodiment, the method further includes determining when repeatability of the parameters meets a stability specification. In one embodiment, the method may proceed to recording the parameters.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application No. 17 / 534,116, filed November 23, 2021, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] FIELD Embodiments relate to the field of semiconductor manufacturing, and more particularly to a process for mitigating first wafer effect in a semiconductor processing chamber. [Background technology]

[0003] Process chambers in semiconductor manufacturing environments are carefully calibrated to produce accurate results on substrates (e.g., wafers) processed in the chamber. A process chamber that is left idle will deviate from its optimal operating conditions. Substrates processed immediately after idling often exhibit differences in film properties or other processing characteristics. This drift from desired results is often referred to as the first wafer effect (FWE).

[0004] To minimize FWE, the chamber can run periodic conditioning recipes, in which dummy substrates are cycled through the chamber. While such processes can mitigate FWE, they come at a cost. For example, increased operating costs are observed due to the cost of powering the chamber, the cost of consumable materials (e.g., gases), and the cost of the dummy substrates. Summary of the Invention

[0005] Embodiments disclosed herein include a method for monitoring chamber performance to mitigate first-wafer effects. In one embodiment, the method for determining optimal chamber conditions includes monitoring parameters while executing a recipe for several substrates after the chamber has been idle. In one embodiment, the method further includes determining when the repeatability of the parameters meets stability specifications. In one embodiment, the method may proceed with recording the parameters.

[0006] In some embodiments, the method may also include warming up the chamber. In one embodiment, the method includes obtaining stored sensor data indicative of an endpoint of a warm-up routine. In one embodiment, the method may then include initiating the warm-up routine when the sensor data is below the stored sensor data value. In one embodiment, the method may further include stopping the warm-up routine when the sensor data is equal to or greater than the stored sensor data value.

[0007] In one embodiment, a method for warming up a chamber may include obtaining stored virtual sensor data indicative of an endpoint of a warm-up routine. In one embodiment, the stored virtual sensor data is a calculated value based on readings of two or more physical sensors. In one embodiment, the method may further include initiating a warm-up routine when the virtual sensor data is below a value of the stored virtual sensor data. In one embodiment, the warm-up routine includes turning on one or more lamps. In one embodiment, the method may further include stopping the warm-up routine when the virtual sensor data is equal to or greater than the value of the stored virtual sensor data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a graph of recorded sensor values ​​during a process recipe for processing multiple substrates after a chamber idle event, according to one embodiment. [Figure 2A]1 is a line scan across the surface of multiple substrates after a chamber idle event, according to one embodiment. [Figure 2B] 1 is a line scan across the surface of multiple substrates after a chamber idle event and warm-up routine, according to one embodiment. [Figure 3A] 10 is a graph of virtual sensor readings over the course of processing multiple substrates without a warm-up routine after a chamber idle event, according to one embodiment. [Figure 3B] 10 is a graph of virtual sensor readings over the course of processing multiple substrates following a chamber warm-up routine after an idle event, according to one embodiment. [Figure 4] FIG. 1 is a flow diagram of a process for recording parameters corresponding to optimal chamber conditions, according to one embodiment. [Figure 5] FIG. 1 is a flow diagram of a process for warming up a chamber after a chamber idle event, according to one embodiment. [Figure 6] FIG. 1 is a flow diagram of a process for warming up a chamber after a chamber idle event and beginning processing of a lot of substrates. [Figure 7] FIG. 1 illustrates a block diagram of an exemplary computer system that may be used with a processing tool, according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The systems described herein include processes for mitigating early wafer effects in semiconductor processing chambers. In the following description, numerous specific details are set forth to provide a thorough understanding of the embodiments. It will be apparent to those skilled in the art that the embodiments may be practiced without these specific details. In other instances, well-known aspects have not been described in detail so as not to unnecessarily obscure the embodiments. Furthermore, it should be understood that the various embodiments illustrated in the accompanying drawings are illustrative representations and are not necessarily drawn to scale.

[0010] As discussed above, periodic conditioning recipes are currently used to mitigate first wafer effects (FWE). However, such processes result in increased chamber operating costs. In response, embodiments disclosed herein include a chamber warm-up routine designed to mitigate FWEs that occur after a chamber idle event. Thus, the chamber may be kept idle without the need to run periodic conditioning recipes. That is, processing dummy wafers to keep the chamber in optimal operating condition may not be necessary.

[0011] In one embodiment, a chamber warm-up routine returns the chamber to optimal operating conditions before the first substrate is loaded into the chamber. Optimal operating conditions may be identified in a previous iteration of a processing operation. For example, the system may monitor the first few substrates in a lot of substrates. When the sensor data for subsequent substrates is within stability specifications (e.g., a difference between 0.1% and 25%), the sensor data is stored for future use. The sensor data may be acquired from one or more sensors. In certain embodiments, the sensor data for a given time is stored for future use. For example, replicating the chamber conditions at the time a substrate is inserted into or removed from the chamber has been shown to be a good metric for determining when the chamber is properly warmed up.

[0012] In one embodiment, the stored sensor data may be used as an indicator value to determine when the warm-up routine has returned the chamber to optimal operating conditions. In one embodiment, the sensor data may be a temperature value. In some embodiments, the sensor data may be a physical sensor that directly detects a chamber condition. In other embodiments, the sensor data may be sourced from a virtual sensor. A virtual sensor provides a calculated value using source data from one or more physical sensors (e.g., a flow sensor, a pressure sensor, a butterfly valve control angle, etc.). That is, a virtual sensor may provide sensor data for a chamber condition that is difficult or impossible to measure directly.

[0013] Referring now to FIG. 1 , a graph of sensor data for processing multiple substrates is shown, according to one embodiment. In a particular embodiment, the sensor used to provide the data in FIG. 1 is the change in angle feedback of a butterfly valve during the first five substrates. In other embodiments, the sensor used to provide the data in FIG. 1 is a virtual sensor (e.g., a virtual angle feedback sensor). While an angle feedback sensor is provided as an example of a sensor type, it should be understood that other types of sensors (e.g., pressure sensors, gas flow sensors, temperature sensors, etc.), or combinations of different sensors, may be used in embodiments disclosed herein.

[0014] In one embodiment, each line in the graph of FIG. 1 represents the sensor value of a different substrate processed. In one embodiment, the processed substrate may be the first substrate after a chamber idle event. Thus, the FWE is shown in FIG. 1. As shown in the graph of FIG. 1, the first substrate has a lower value than subsequent substrates. Notably, the substrates all exhibit variation from one another. However, as more substrates are processed, the variation between substrates decreases. For example, the difference between the first substrate W1 and the second substrate W2 is greater than the difference between the fifth substrate W5 and the tenth substrate W10.

[0015] Differences in the sensor output values ​​in Figure 1 can result in non-uniform substrate results. For example, film properties (e.g., thickness, composition, uniformity, etc.) can vary between processed substrates. This variation can result in substrates that do not meet desired specifications. Thus, the substrates may be discarded, or the substrate variations may cause device performance or yield issues later in the process flow.

[0016] An example of film non-uniformity caused by FWE is shown in FIG. 2A, which is a line scan (e.g., a 49-point line scan) across the surface of a substrate. As shown, the first substrate 1 (without warm-up) has significantly lower values ​​than the second substrate 2 and the third substrate 3. In some cases, the difference between the first substrate 1 and the second substrate 2 and the second substrate 3 may exceed the allowable tolerance. Therefore, the first substrate 1 may need to be discarded or reworked.

[0017] In response, embodiments disclosed herein include a warm-up routine that mitigates the FWE illustrated in FIG. 2A. For example, FIG. 2B shows a line scan across the surface of substrates 1-3 when the warm-up routine is used. As shown, the first substrate 1, the second substrate 2, and the third substrate 3 each have substantially similar results. That is, there is little variation among substrates 1-3. Therefore, the FWE described previously is avoided.

[0018] Referring now to FIG. 3A, a graph of virtual sensor readings over the course of processing multiple substrates is shown, according to one embodiment. Overlaid on the graph of virtual sensor data is an indication of when substrates were inserted into and / or removed from the chamber. That is, peak 310 corresponds to the insertion and / or removal of a substrate into and / or from the chamber. The initial substrate insertion occurs after an idle period 311. For example, the idle period can be of any duration. In some embodiments, the idle period can be one minute or more, one hour or more, or one day or more. Thus, the first peak 312 of the virtual sensor can be different from subsequent peaks. For example, the first peak 312 is lower than the subsequent peaks. Thus, the first substrate may be suffering from FWE.

[0019] Referring now to Figure 3B, a graph of virtual sensor readings over the course of processing multiple substrates is shown, according to one embodiment. The graph in Figure 3B differs from the graph in Figure 3A in that a warm-up routine 314 was performed before the first substrate swap 310. As shown, the virtual sensor output value rises before the first substrate swap 310. The warm-up routine 314 results in the subsequent peaks 312 in the virtual sensor data substantially leveling off. Thus, there is substantially no FWE.

[0020] In one embodiment, the warm-up routine can be adjusted to provide substantially similar chamber conditions at subsequent instances of substrate swap. For example, the virtual sensor value is substantially similar at each instance of substrate swap. In some cases, a substrate swap occurs at the end of each peak 312 in the virtual sensor reading. In this manner, the warm-up routine 314 raises the virtual sensor output value and allows the virtual sensor output value to drop for a short period of time before increasing to form the next peak. Such an embodiment allows the first substrate after the idle event 311 to be processed as if one or more substrates had been processed prior to the first substrate. Correspondingly, FWE is avoided.

[0021] 4, a flow diagram of a process 480 for learning optimal chamber conditions after an idle event is shown, according to one embodiment. In one embodiment, the process 480 may be initiated after the chamber has been idled. For example, an idle event may be a period of time (e.g., seconds, minutes, hours, days) during which the chamber is not processing substrates. In some embodiments, during an idle event, there may be no substrates (e.g., dummy substrates) cycling through the chamber with a conditioning recipe. Thus, the cost of operating the chamber is reduced.

[0022] In one embodiment, process 480 may begin with step 481, which involves monitoring parameters while executing a recipe for several substrates at the beginning of a lot of substrates. In one embodiment, the recipe may be started without a chamber warm-up, i.e., it is expected that one or more of the substrates may exhibit FWE. In one embodiment, several substrates may be three or more substrates. In some embodiments, several substrates may be up to 25 substrates. In a specific embodiment, several substrates may be five substrates.

[0023] In one embodiment, the parameter being monitored can be any state of the chamber during recipe execution. For example, the parameter can be pressure, temperature, or flow rate of one or more gases. In some embodiments, multiple parameters can be monitored during step 481. In one embodiment, the parameter can be detected by a physical sensor. In other embodiments, the parameter can be measured by a virtual sensor.

[0024] In one embodiment, process 480 can proceed to step 482, which includes determining when parameter repeatability meets a stability specification. Parameter repeatability can refer to how consistent a monitored parameter is at a given point in a recipe. For example, greater repeatability at or around a substrate swap operation has been shown to lead to more consistent substrate results (e.g., uniform film properties). Thus, the point in a recipe used to determine parameter repeatability can occur at or around a substrate swap operation.

[0025] In one embodiment, the stability specification refers to the percentage difference between subsequent iterations of a recipe. In some embodiments, the stability specification may be as tight as about 0.1%. In other embodiments, the stability specification may be up to about 25%. In particular embodiments, the stability specification may be about 3%. That is, at certain times during the recipe (e.g., during or around a substrate swap), the output value of the sensor (or virtual sensor) may be within the stability specification.

[0026] In one embodiment, process 480 can proceed to step 483, which includes recording the parameters. That is, when the parameters meet stability specifications, the output of the sensors is recorded. The recorded parameters can be used as a reference for determining when the chamber is sufficiently warmed up. For example, in the case of a thermal process, an idling chamber can be heated to a temperature consistent with the recorded parameters before starting substrate processing. In this manner, FWE can be limited or avoided entirely.

[0027] 5, a flow diagram of a process 590 for warming up a chamber after an idle event is shown, according to one embodiment. In one embodiment, an idle event may refer to a chamber that has not recently been used to process production substrates. In one embodiment, an idle event may have a duration of a few seconds, minutes, hours, days, or longer. During an idle event, there may be no need to cycle non-production substrates (e.g., dummy substrates) through the conditioning recipe.

[0028] In one embodiment, process 590 may begin with step 591, which includes obtaining stored sensor data indicative of an endpoint of the warm-up routine. In one embodiment, the stored sensor data may be from one or more physical sensors and / or one or more virtual sensors. In certain embodiments, the stored sensor data may be generated using a process similar to process 480, described in more detail above. That is, the stored data may be data indicative of the chamber being adequately warmed up to mitigate or eliminate FWE. In some cases, the stored data may be referred to as learned values ​​or setpoints. It should be understood that the endpoint of the warm-up routine may be recipe-specific; that is, the endpoint may be different for different process recipes. In some embodiments, the endpoint may be a process time, a process temperature, or any other process parameter.

[0029] In embodiments, process 590 can proceed to step 592, which includes initiating a warm-up routine when the sensor data is below a stored sensor data value. For example, in some embodiments, the value can be a temperature. When the measured temperature (or the temperature calculated using the virtual sensor) is below the stored sensor data value, the chamber can initiate one or more processes to raise the temperature to the stored sensor data value. For example, in a chamber for thermal processing, one or more lamps can be turned on to raise the temperature. In other embodiments, various processing steps, such as activating a plasma source, rotating a susceptor, raising and lowering pins, changing gas flow into the chamber, changing pressure, and any other chamber controls, can be performed to bring the chamber into a state consistent with the learned setpoint.

[0030] In one embodiment, process 590 can proceed to step 592, which includes stopping the warm-up routine when the sensor data is equal to or greater than the learned value. Once one or more sensors reach the learned value, the warm-up step is considered complete. That is, the chamber is in a condition substantially consistent with the condition of the chamber running a predetermined recipe continuously. Thus, when the first substrate is processed, the results are expected to show no FWE, or at least the magnitude and / or duration of the FWE will be reduced.

[0031] It should be understood that such processes (e.g., process 480 or process 590) can be performed in any type of processing chamber typical of a semiconductor manufacturing environment. In certain embodiments, the elimination of FWE is achieved in a thermal chamber, such as a chamber performing a radical oxidation process. In such embodiments, the temperature at or around the time of substrate exchange has been shown to be strongly correlated with the presence or absence of FWE. Thus, by increasing the temperature to match the temperature of subsequent substrate exchanges, the first substrate is processed with minimal FWE.

[0032] While embodiments involving thermal processing have been described in detail herein, it should be understood that embodiments are not limited to such processing tools. For example, chambers utilizing plasma processes may also utilize one or more of the embodiments described herein. Furthermore, while temperature is explicitly described as one of the sensor (or virtual sensor) output values, it should be understood that various sensor types or combinations of various sensor types may be used to mitigate or eliminate FWE for various types of semiconductor processing recipes in idle chambers.

[0033] 6, a flow diagram of a process 670 for automatically using a warm-up routine is shown, according to one embodiment. In such an embodiment, an operator of a processing tool enables a warm-up feature as part of a recipe or process sequence. After the warm-up feature is enabled, the tool automatically collects critical sensor data for the recipe. When stable sensor data values ​​are learned, they are stored in a readiness database. Each time a recipe is run, the software executes process 670 after a user-defined maximum idle time (e.g., one minute or more, one hour or more, etc.).

[0034] In one embodiment, step 670 may begin with step 671, which involves importing the learned data into a warm-up routine developed for a particular chamber type. In one embodiment, the learned data may be obtained using a process similar to process 480, described in more detail above.

[0035] In one embodiment, process 670 may proceed to step 672, which includes performing a warm-up routine when a lot of substrates arrives at a tool that includes a chamber of a particular chamber type. For example, the warm-up routine may be initiated when a front opening unified pod (FOUP) arrives at the tool or when the FOUP door is opened. However, it should be understood that in some embodiments, the warm-up routine may be initiated before the FOUP arrives at the tool.

[0036] In one embodiment, process 670 can proceed to step 673, which includes sending the first substrate from a lot of substrates to the chamber exchange position during the warm-up routine. Moving the first substrate to the exchange position allows the substrate to be processed immediately after reaching a warm-up condition, thus reducing FWE.

[0037] In one embodiment, process 670 may proceed to step 674, which includes loading the first substrate into the chamber upon completion of the warm-up routine. In one embodiment, the first substrate is loaded immediately after the warm-up routine is completed; that is, following completion of the warm-up routine, there is an instruction to load the first substrate into the chamber. In other embodiments, the first substrate may be loaded into the chamber within a few seconds of completion of the warm-up routine or within one minute of completion of the warm-up routine. After the first substrate is loaded into the chamber, the first substrate may be processed according to the recipe.

[0038] Referring now to FIG. 7 , a block diagram illustrating an exemplary computer system 700 of a processing tool is shown, according to one embodiment. In one embodiment, the computer system 700 is connected to the processing tool and controls processing within the processing tool. The computer system 700 may be connected to (e.g., networked with) other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The computer system 700 may operate as a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computer system 700 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, switch, or bridge, or any machine capable of executing a series of instructions (sequential or otherwise) that define operations to be performed by the machine. Furthermore, although only a single machine is shown as computer system 700, the term "machine" is also intended to include any collection of machines (e.g., computers) that individually or jointly execute a set (or sets) of instructions to perform any one or more of the methods described herein.

[0039] The computer system 700 may include a computer program product, or software 722, having a non-transitory machine-readable medium having instructions stored thereon, which may be used to program the computer system 700 (or other electronic device) to perform processes according to embodiments. A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium may include a machine-readable storage medium (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), a machine-readable (e.g., computer-readable transmission medium (e.g., electrical, optical, acoustic, or other form of propagated signal (e.g., infrared signal, digital signal, etc.)), etc.

[0040] In one embodiment, computer system 700 includes a system processor 702, a main memory 704 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), a static memory 706 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory 718 (e.g., data storage device), which communicate with each other via a bus 730.

[0041] The system processor 702 may refer to one or more general-purpose processing devices, such as a microsystem processor, a central processing unit, or the like. More specifically, the system processor may be a complex instruction set computing (CISC) microsystem processor, a reduced instruction set computing (RISC) microsystem processor, a very long instruction word (VLIW) microsystem processor, a system processor that executes other instruction sets, or a system processor that executes a combination of instruction sets. The system processor 702 may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal system processor (DSP), a network system processor, or the like. The system processor 702 is configured to execute processing logic 726 for performing the operations described herein.

[0042] The computer system 700 may further include a system network interface device 708 for communicating with other devices or machines. The computer system 700 may also include a video play unit 710 (e.g., a liquid crystal display (LCD), a light emitting diode display (LED), or a cathode ray tube (CRT)), an alphanumeric input device 712 (e.g., a keyboard), a cursor control device 714 (such as a mouse), and a signal generating device 716 (e.g., a speaker).

[0043] The secondary memory 718 may include a machine-accessible storage medium 732 (or, more specifically, a computer-readable storage medium) having stored thereon one or more sets of instructions (e.g., software 722) that embody any one or more of the methods or functions described herein. This software 722 may also reside, completely or at least partially, within the main memory 704 and / or the system processor 702 while being executed by the computer system 700, with the main memory 704 and the system processor 702 also constituting machine-readable storage media. The software 722 may further be transmitted or received over the network 720 via the system network interface device 708. In one embodiment, the network interface device 708 may operate using RF, optical, acoustic, or inductive coupling.

[0044] While in an exemplary embodiment, machine-accessible storage medium 732 is shown as a single medium, the term "machine-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) on which one or more sets of instructions are stored. The term "machine-readable storage medium" should also be interpreted to include any medium capable of storing or encoding a set of instructions for execution by a machine and causing the machine to perform any one or more of the methods. Thus, the term "machine-readable storage medium" should be interpreted to include, but not be limited to, solid-state memory, optical media, and magnetic media.

[0045] In the foregoing specification, certain exemplary embodiments have been described. It will be apparent that various modifications may be made to the exemplary embodiments without departing from the scope of the following claims. Correspondingly, the specification and drawings should be regarded in an illustrative rather than a restrictive sense.

Claims

1. 1. A method for determining optimal chamber conditions, comprising: monitoring a parameter while executing a recipe for several substrates after the chamber is idle, the parameter being a pressure in the chamber, the pressure being monitored using a virtual sensor based on two or more physical sensors, a first physical sensor of the two or more physical sensors being a gas flow sensor, and a second physical sensor of the two or more physical sensors being a pressure sensor; determining when the repeatability of said parameter meets a stability specification; recording said parameters; and executing a warm-up routine based on the parameters, the warm-up routine being performed when no substrate is present. A method comprising:

2. The method of claim 1 , wherein the number of substrates is up to 25 substrates.

3. The method of claim 1 , wherein the number of substrates is up to five substrates.

4. The method of claim 1 , wherein the parameter is measured at the start of a substrate exchange operation.

5. 10. The method of claim 1, wherein the stability specification is between 0.1% and 25%.

6. 6. The method of claim 5, wherein the stability specification is 3%.

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