Abstract:
A system includes a probe arranged in a plasma processing chamber of the plasma processing system. A capacitor has one end connected to the probe. An RF source is configured to selectively supply an RF signal including RF bursts to another end of the capacitor. A plasma characterizing computing device is configured to collect a set of process data from the probe by measuring current supplied to the capacitor and voltage at the capacitor; identify a relevancy range for the set of process data, wherein the relevancy range includes process data collected after the capacitor begins discharging and before the capacitor is fully discharged; determine a set of seed values based on the process data in the relevancy range; and employ the relevancy range and the set of seed values as initial values for curve fitting corresponding to the one of the RF bursts to reduce a number of curve-fitting iterations.

Description:
PRIORITY CLAIM 
       [0001]    This application is a continuation of U.S. application Ser. No. 12/477,007 filed Jun. 2, 2009, which is related to and claims priority under 35 U.S.C. §119(e) to a commonly assigned provisional patent application entitled “Methods For Automatically Characterizing A Plasma,” by Keil et al., Application Ser. No. 61/075,948, filed on Jun. 26, 2008, which is incorporated by reference herein. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    Advances in plasma processing have provided for growth in the semiconductor industry. To supply chips for a typical electronic product, hundreds or thousands of substrates (such as semiconductor wafers) may be processed. In order for the manufacturing company to be competitive, the manufacturing company needs to be able to process the substrates into quality semiconductor devices with minimal processing time. 
         [0003]    Typically, during plasma processing, problems may arise that may cause the substrates to be negatively impacted. One important factor that may alter the quality of the substrate being processed is the plasma itself. In order to have sufficient data to analyze the plasma, sensors may be employed to collect processing data about each substrate. The data collected may be analyzed in order to determine the cause of the problems. 
         [0004]    To facilitate discussion,  FIG. 1  shows a simple schematic diagram of a data collecting probe in a portion of a plasma system  100 . Plasma system  100  may include a radio frequency (RF) source  102 , such as a pulsating RF frequency generator, capacitively-coupled to a reactor chamber  104  to produce plasma  106 . When RF source  102  is turn on, a bias voltage is developed across an external capacitor  108 , which may be about 26.2 nanofarads (nF). In an example, RF source  102  may provide a small burst of power (e.g., 11.5 megahertz) every few milliseconds (e.g., about five milliseconds) causing external capacitor  108  to be charged. When RF source  102  is turned off, a bias voltage remains on external capacitor  108  with a polarity such that probe  110  is biased to collect ions. As the bias voltage decays, the curves as shown in  FIGS. 2A ,  2 B and  3  may be traced. 
         [0005]    Those skilled in the art are aware that probe  110  is usually an electrical probe with a conducting planar surface that may be positioned against the wall of reactor chamber  104 . Probe  110  is thus directly exposed to reactor chamber  104  environment. Current and voltage data collected by probe  110  may be analyzed. Since certain recipe may cause a non-conducting deposition layer  116  to be deposited on probe  110 , not all probes may be able to collect reliable measurements. However, those skilled in the art are aware that a PIF (planar ion flux) probe enables data to be collected despite the non-conducting deposition layer since the PIF probe scheme is not required to draw a direct current (DC) to implement a measurement. 
         [0006]    The current and voltage signal in plasma system  100  is measured by other sensors. In example  100 , when RF source  102  is switched off, current sensor  112  and a high impedance voltage sensor  114 , are employed to measure the current and the voltage, respectively. The measurement data collected from current sensor  112  and voltage sensor  114  may then be plotted to create a current graph and a voltage graph. The data may be manually plotted or the data may be entered into a software program to create the graphs. 
         [0007]      FIG. 2A  shows a graph of voltage versus time after a RF charge cycle. At data point  202 , RF source  102  has been switched off after an RF charge has been provided (i.e., RF burst). In this example, at data point  202 , the voltage across probe  110  is about negative 57 volts. As plasma system  100  returns to a rest state (interval between data points  204  and  206 ), the voltage usually reaches a floating voltage potential. In this example, the floating voltage potential rises from about negative 57 volts to about zero volt. However, the floating voltage potential does not have to be zero and may be a negative or a positive bias voltage potential. 
         [0008]    Similarly,  FIG. 2B  shows a graph of current data collected after a RF charge. At data point  252 , RF source  102  has been switched off after an RF charge has been provided. During a decay period  254 , the return current at external capacitor  108  may be discharged. In an example, at full charge (data point  252 ), the current is about 0.86 mA/cm 2 . However, when the current is fully discharged (data point  256 ), the current has returned to zero. Based on the graph, the discharge takes about 75 milliseconds. From data point  256  to data point  258 , the capacitor remains discharged. 
         [0009]    Since both the current data and the voltage data are collected over a period of time, a current versus voltage graph may be generated by coordinating the time in order to eliminate the time variable. In other words, the current data collected may be matched against the voltage data collected.  FIG. 3  shows a simple current versus voltage graph for a single time interval between a RF burst. At data point  302 , RF source  102  has been switched off after an RF charge has been provided. 
         [0010]    By applying a non-linear fit to the data collected during each RF burst, plasma  106  may be characterized. In other words, parameters (e.g., ion saturation, ion saturation slope, electron temperature, floating voltage potential, and the like) that may characterize plasma  106  may be determined. Although plasma  106  may be characterized with the data collected, the process of calculating the parameters is a tedious manual process that requires human intervention. In an example, when the data has been collected after each RF burst (i.e., when the RF charge has been provided and then turned off), the data may be fed into a software analysis program. The software analysis program may perform a non-linear fit to determine the parameters that may characterize the plasma. By characterizing the plasma, the engineer may be able to determine how a recipe may be adjusted in order to minimize substandard processing of the substrates. 
         [0011]    Unfortunately, the prior art method of analyzing the data for each RF burst may require several seconds or as much as several minutes to complete. Since there are typically thousands, if not millions of RF bursts to analyze, the total time for characterizing the plasma for a recipe may take hours to calculate. As a result, the prior art method is not an effective method in providing timely relevant data for process control purposes. 
       BRIEF SUMMARY OF THE INVENTION 
       [0012]    The invention relates, in an embodiment, to a method for automatically characterizing plasma during substrate processing. The method includes collecting a set of process data, which includes at least data about current and voltage. The method also includes identifying a relevancy range for the set of process data, wherein the relevancy range includes a subset of the set of process data. The method further includes determining a set of seed values. The method yet also includes employing the relevancy range and the set of seed values to perform curve-fitting, wherein the curve-fitting enables the plasma to be automatically characterized. 
         [0013]    The above summary relates to only one of the many embodiments of the invention disclosed herein and is not intended to limit the scope of the invention, which is set forth in the claims herein. These and other features of the present invention will be described in more detail below in the detailed description of the invention and in conjunction with the following figures. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0014]    The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which: 
           [0015]      FIG. 1  shows a simple schematic diagram of a portion of a plasma system with a radio frequency (RF) source capacitively-coupled to a reactor chamber to produce plasma. 
           [0016]      FIG. 2A  shows a graph of voltage versus time after a RF charge. 
           [0017]      FIG. 2B  shows a graph of current data collected after a RF charge. 
           [0018]      FIG. 3  shows a simple current versus voltage graph for a single time interval between a RF burst. 
           [0019]      FIG. 4A  show a system including a plasma characterizing computing device; 
           [0020]      FIG. 4B  shows, in an embodiment of the invention, a simple flow chart illustrating the overall steps for automatically characterizing plasma during substrate processing. 
           [0021]      FIG. 5  shows, in an embodiment of the invention, a simple algorithm for determining the relevancy range and the seed values. 
           [0022]      FIG. 6A  shows an example of current versus time after a RF burst. 
           [0023]      FIG. 6B  shows an example of voltage versus time after a RF burst. 
           [0024]      FIG. 6C  shows an example of an inflection point. 
           [0025]      FIG. 6D  shows an example of a curve-fitting applied to a current versus voltage graph. 
       
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
       [0026]    The present invention will now be described in detail with reference to a few embodiments thereof as illustrated 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. It will be apparent, however, 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 detail in order to not unnecessarily obscure the present invention. 
         [0027]    Various embodiments are described hereinbelow, including methods and techniques. It should be kept in mind that the invention might also cover articles of manufacture that includes a computer readable medium on which computer-readable instructions for carrying out embodiments of the inventive technique are stored. The computer readable medium may include, for example, semiconductor, magnetic, opto-magnetic, optical, or other forms of computer readable medium for storing computer readable code. Further, the invention may also cover apparatuses for practicing embodiments of the invention. Such apparatus may include circuits, dedicated and/or programmable, to carry out tasks pertaining to embodiments of the invention. Examples of such apparatus include a general-purpose computer and/or a dedicated computing device when appropriately programmed and may include a combination of a computer/computing device and dedicated/programmable circuits adapted for the various tasks pertaining to embodiments of the invention. 
         [0028]    As aforementioned, the PIF probe method may be employed to collect data about the plasma that may be positioned within the reactor chamber environment. Data collected from a sensor (e.g., PIF probe) may be employed to characterize the plasma in the reactor chamber. Additionally, since the sensor employs a collection surface as shown in  FIG. 1 , data about the chamber surface may also be determined. In the prior art, the data collected by the PSD probe provides a ready source of data that is available for analysis. Unfortunately, the sheer volume of data that may be collected has made analyzing the data in a timely manner a challenge. Since thousands or even millions of data points may be collected, the task of identifying the relevant interval in order to accurately characterize a plasma may be a daunting task, especially since the data is usually being analyzed manually. As a result, the data collected has not been useful in providing the plasma processing system with a timely characterization of the plasma. 
         [0029]    However, if relevant data points that are needed in order to characterize a plasma are identified from the thousands/millions of data points that may be collected, then the time required to characterize a plasma may be significantly reduced. In accordance with embodiments of the invention, a method is provided for automatically characterizing plasma in a relatively short time period. Embodiments of the invention described herein provide for an algorithm for identifying the relevancy range in order to reduce the data points that may need to be analyzed in order to characterize a plasma. As discussed herein, the relevancy range refers to a smaller set of data points from the thousands or millions of data points that may be gathered between each RF burst. Embodiments of the invention further provide for estimating seed values that may be applied to a mathematical model that calculates the values for characterizing a plasma. By performing curve-fitting to the relevancy range, parameters that may be employed to characterize a plasma may be calculated. 
         [0030]    The features and advantages of the present invention may be better understood with reference to the figures and discussions that follow. 
         [0031]      FIG. 4A  shows a system  400  including a plasma characterization computing device  401  that is configured as described below.  FIG. 4B  shows, in an embodiment of the invention, a simple flow chart illustrating the steps for automatically characterizing plasma during substrate processing. Consider the situation wherein, an RF charge has been provided during substrate processing. 
         [0032]    At a first step  402 , current and voltage data are collected. In an example, after the RF source has been turned on, an RF charge (pulse) is provided. After the RF charge has been turn off, a current sensor and a voltage sensor may be employed to collect data at a probe, such as a planar ion flux probe, which may be mounted to a chamber wall of the reactor chamber. As aforementioned, the number of data points that may be collected by the sensors may range in the thousands or millions. In some cases, thousands to ten of thousands of data points may be collected between each RF burst making near-realtime analysis in the prior art nearly impossible. 
         [0033]    In the prior art, several hours may be allotted for analyzing the measurement data that is collected during semiconductor substrate processing. In one aspect of the invention, the inventors herein realized that the measurement data between each RF burst does not have to be analyzed in order to characterize a plasma. Instead, if curve-fitting is applied to a relevancy range of the data set, parameters that may be employed to characterize the plasma may be determined. 
         [0034]    At a next step  404 , a relevancy range is determined. As aforementioned, the relevancy range refers to a subset of the data set that has been collected between each RF burst. In the prior art, since the data is being manually analyzed, the sheer volume of the data collected make calculating the relevancy range a challenging task. In many instances, the relevancy range may be visually estimated. In identifying the relevancy range, noises that may exist may be substantially eliminated from the subset of data set. In an example, during complex substrate processing, a polymer buildup may occur on the probe, causing a portion of the data collected to be skewed. For example, the portion of the data that may be impacted tends to be the data that may be collected once the capacitor has been fully discharged. In identifying the relevancy range, data associated with the polymer buildup may be removed from the analysis. In other words, the determination of the relevancy range may enable plasma characterization to occur without being subject to random noises. Discussion about how a relevancy range may be determined, for an example, is provided later in the discussion of  FIG. 5 . 
         [0035]    In addition to identifying the relevancy range, the seed values may also be determined, at a next step  406 . As discussed herein, the seed values refer to the estimated value of the slope, the electron temperature, the ion saturation value, the floating voltage potential, and the like. Discussion about how the seed values may be estimated, for example, is provided in the discussion of  FIG. 5 . 
         [0036]    The relevance range and the seed values are utilized to perform curve-fitting. Since curve-fitting has to be performed before the next RF burst, the methods employed to determine the relevancy range and/or seed values have to utilize minimum overhead and produce values that are close to the final fit values, thereby reducing number of curve-fitting iterations that may be required in order to achieve a rapid convergence. 
         [0037]    With the relevancy range and the seed values, at a next step  408 , a non-linear fit (e.g., curve-fitting) may be performed, thereby enabling the plasma to be characterized within a shorter time period without requiring an expensive high-end computer. Unlike the prior art, the method allows for results from a decay interval due to a single RF burst to be characterized in approximately 20 milliseconds instead of requiring a few minutes or even a few hours to process. With near-realtime analysis capability, the method may be applied as part of an automatic control system to provide the engineer with relevant data during plasma processing. 
         [0038]      FIG. 5  shows, in an embodiment of the invention, a simple algorithm for determining the relevancy range and the seed values.  FIG. 5  will be discussed in relation to  FIGS. 6A ,  6 B,  6 C, and  6 D. 
         [0039]    At a first step  502 , the data collected during each RF burst is automatically plotted. In an example, the current data that may be collected by the current sensor is plotted into a current versus time graph  600 , such as the one shown in  FIG. 6A . In another example, the voltage data collected may be plotted into a voltage versus time graph  650 , as shown in  FIG. 6B . Although the data may produce similar graphs as the prior art, unlike the prior art, the data collected is automatically fed into the analysis program without requiring human intervention. Alternatively, the measurement data collected does not have to be plotted. Instead, the data may be fed directly into the analysis program. Instead, the graphs are provided as visual examples to explain the algorithm. 
         [0040]    Unlike the prior art, the entire data set is not analyzed in order to characterize a plasma. Instead, a relevancy range is determined. To determine the relevancy range, a percentage decay point may first be determined, at a next step  504 . As discussed herein, the percentage decay point refers to the data point at which the original value has decayed to a certain percentage of the original value. In an embodiment, the percentage decay point may represent the end of the data interval to be analyzed. In an example, when the RF source is switched off, the current value is about 0.86 mA/cm 2 . The value is represented by a data point  602  on graph  600  of  FIG. 6A . If the percentage decay point is set to ten percent of the original value, the percent decay point is at data point  604 , which is about 0.086 mA/cm 2 . In other words, the percentage decay point may be determined by applying a pre-defined percentage to the original value, which is value of the electrical charge when the RF source is switched off and the system is returning to an equilibrium state. In an embodiment, the percentage is empirically determined. In an embodiment, instead of employing a percentage decay point to determine the end of the data interval, the peak of a first derivative of the data collected for each RF burst may be calculated. 
         [0041]    At a next step  506 , the algorithm may determine the ion saturation interval, which is the data subset between the original value and a second decay point. As discussed herein, the ion saturation interval refers to the region of the current-voltage (IV) curve at which the probe potential is sufficiently negative with respect to the floating potential such that the electron flux to the probe is negligible. In this region the current to the probe increases slowly and linearly with increasingly negative potential. In addition, the ion saturation interval is the regime at which the bias voltage is sufficiently negative with respect to the floating potential such that the probe will collect all the available ions in the system. In other words, the collected current “saturates” as the bias voltage is raised sufficiently high. Also, as discussed herein, the “available ions” refers to the flux of ions impinging upon the sheath boundary, which may enlarge as the bias voltage is further increased. 
         [0042]    In other words, the ion saturation interval is the interval from data points  602  and  606  of  FIG. 6A . In an embodiment, the second decay point may be determined by taking a percentage of the original value (i.e., data point  602 ). In an example, if the second decay point is about 95 percent of the original value, the second decay point is about 0.81 mA/cm 2  (i.e., data point  606 ). Hence, the ion saturation interval is from the original value (data point  602 ) to the second decay point (data point  606 ). Note that the second decay point (such as data point  608 ) is between the original value (data point  602 ) and the percentage decay point (data point  604 ). Similar to the percentage decay point, the second decay point may also be based on a pre-defined threshold, in an embodiment. In an embodiment, the percentage is empirically determined. 
         [0043]    Once the ion saturation interval has been determined, at a next step  508 , the slope (s) and the ion saturation (i 0 ) may be estimated. As aforementioned, the slope (s) and the ion saturation (i 0 ) are two of the four seed values that may be applied to a mathematical model (Equation 2 below) to determine the parameters that characterize a plasma. In an example, the slope (s) may be determined by performing linear regression. In another embodiment, the algorithm may also determine the ion saturation (i 0 ) by taking the average of the data values between data points  602  and  606 . 
         [0044]    At a next step  510 , the algorithm may determine the inflection point, which is the point at which the first derivative changes sign. In an embodiment, the inflection point may be calculated by identifying the minimal value of the first derivative of the current values between the percentage decay point and the second decay point. To illustrate,  FIG. 6C  shows the first derivative of the values between a percentage decay point ( 664 ) and an original point ( 662 ) of a current signal  660 . The inflection point is the minimal data point of the first derivative ( 670 ), which has a value of −0.012 mA/cm 2  and an index value of 226 (as shown by data point  666 ). To determine the inflection value, the index value is mapped to current signal plot  660 . In this example, when the index value of the first derivative is mapped to current signal  660 , the inflection value is 0.4714 mA/cm 2 , as shown by data point  668 . 
         [0045]    In an embodiment, the relevancy range is defined as the range between the original value and the inflection point. Additionally or alternatively, a percent decay threshold may be set (e.g., at 35 percent) instead of calculating the inflection point. In an example, using the percent decay point of 35 percent, which may be empirically determined, the relevancy range may fall between points  602  and  604  of  FIG. 6A . 
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                 TABLE 1 
               
             
             
               
                   
               
               
                 Parameters defined 
               
             
          
           
               
                   
                 Parameters 
                 Variable name 
               
               
                   
                   
               
               
                   
                 I meas   
                 Current measured 
               
               
                   
                 V meas   
                 Voltage measured 
               
               
                   
                 t 
                 Current time 
               
               
                   
                 T e   
                 Electron temperature 
               
               
                   
                   
               
             
          
         
       
     
         [0046]    With the inflection point identified, the electron temperature may be estimated, at a next step  512 . The electron temperature may be estimated by employing Equation 1 above. The current and voltage data utilized to calculate the electron temperature is within the transition interval, which is usually when a probe is drawing less current than the ion saturation current. In an embodiment, the time at which the current and voltage data is measured may correspond with the inflection point. Alternatively, the inflection point of the current-voltage (I-V) curve may also be employed. Since the electron temperature is a ratio of a first derivative (as determined in calculating the percentage decay point) of the data collected for an RF burst at a time corresponding to the inflection point on the current-voltage curve, the computational overhead that may be required to generate the number is minimal. 
         [0047]    At a next step  514 , the algorithm may determine the floating voltage potential. Since the floating voltage potential is determined based on the voltage data collected, the floating voltage potential may be determined without first having to determine the values as calculated in steps  504 - 512 . Those skilled in the art are aware that floating voltage potential is the electrical potential at which the probe floats after the external capacitor has fully discharged. Typically, the floating voltage potential may be determined by looking at the signal that occurs right before the next RF burst. However, due to the possibility of polymer buildup causing distortion, erroneous data (i.e., noise) may be collected; thus, the floating voltage potential may be calculated by averaging the voltage values collected toward the end of the collection period. In an embodiment, the floating voltage potential may be calculated from data point  652  (the data point at which the voltage first reaches its floating potential) to data point  654  (the data point just right before the next RF burst), as shown in  FIG. 6B . In another embodiment, the floating voltage potential may be based on the voltage values within a window  656 , which is located between data points  652  and  654 , as shown in  FIG. 6B . In an embodiment, window  656  may be of any size as long as the window begins before the prior pulse has decayed more than 99 percent and ends when the next pulse begins. In one embodiment, the floating voltage potential may be determined from a window that provides an average value with a low standard deviation (error). 
         [0048]    As can be appreciated from the foregoing, the methods for determining the relevancy range and the seed values account for anomalies that may occur in the current, voltage and/or current-voltage (I-V) curves. In an example, polymer buildup may occur at the end of an RF burst. However, by applying the aforementioned algorithms, the relevancy range and the seed values are impervious to unexpected artifacts that may occur during processing. 
         [0049]    Once the relevancy range has been determined and the seed values have been calculated, at a next step  516 , the current values may be plotted against the voltage values and curve-fitting may be applied to generate graph  680  of  FIG. 6D . In an example, a non-linear curve fit, such as Levenberg-Marquardt algorithm, may be applied to perform the curve-fitting. By generating a curve-fitting graph and applying the seed values to the mathematical model, such as Equation 2 below, the four parameters that may be employed to characterize the plasma may be determined. 
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                 Current 
               
               
                   
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                 Voltage 
               
               
                   
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                 Electron temperature 
               
               
                   
                   
               
             
          
         
       
     
         [0050]    As can be appreciated from one or more embodiments of the present invention, an automated method for characterizing plasma during plasma processing is provided. By determining a relevancy range and a set of seed values, plasma characterization may occur without having to process thousands or millions of data points that are usually collected after a single RF burst. The automated method transforms a previously tedious and manual process into an automatic task that may be performed quickly and efficiently. With data analysis significantly shortened from a few minutes (or even a few hours) to a few milliseconds, plasma characterization may be performed during plasma processing instead of being a post-production process. Thus, relevant data may provide insights into the current plasma environment, thereby enabling recipe and/or tool adjustment to occur and waste to be minimized. 
         [0051]    While this invention has been described in terms of several preferred embodiments, there are alterations, permutations, and equivalents, which fall within the scope of this invention. Although various examples are provided herein, it is intended that these examples be illustrative and not limiting with respect to the invention. 
         [0052]    Also, the title and summary are provided herein for convenience and should not be used to construe the scope of the claims herein. Further, the abstract is written in a highly abbreviated form and is provided herein for convenience and thus should not be employed to construe or limit the overall invention, which is expressed in the claims. If the term “set” is employed herein, such term is intended to have its commonly understood mathematical meaning to cover zero, one, or more than one member. It should also be noted that there are many alternative ways of implementing the methods and apparatuses of the present invention. It is therefore intended that the following appended claims be interpreted as including all such alterations, permutations, and equivalents as fall within the true spirit and scope of the present invention.