Improved control for semiconductor processing systems.

Abstract

To provide processing of optical data with improvements in latency, repeatability, stability, signal detectability, and other benefits.SOLUTION: The improved processing can be used to more accurately and consistently monitor and control semiconductor processes. In one example, a method of processing spectral data includes the steps of: (1) collecting a time-ordered sequence of optical emission spectroscopy data over one or more wavelengths; (2) extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data; (3) analyzing characteristics of the one or more attributes; (4) determining conditioning of the one or more attributes; (5) processing the one or more attributes according to a predetermined set of filters, the conditioning, and the characteristics; and (6) selecting a filter configuration for processing the spectral data on the basis of the processing of the one or more attributes.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 389,416, entitled "Improved Control for Semiconductor Processing Systems," filed by Chris Pylant on July 15, 2022, which is incorporated by reference in its entirety and is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to optical spectroscopy systems and methods of use, and more particularly to improved signal processing for lower latency, increased repeatability, and other benefits of control between a spectrometer used for optical signal collection and real-time data collected from a semiconductor tool controller. [Background technology]

[0003] Optical monitoring of semiconductor processes is an established method for controlling processes such as etching, deposition, chemical mechanical polishing, and implantation. Optical emission spectroscopy (OES) and interferometric endpoint (IEP) are two basic types of modes of operation for data collection. In OES applications, light emitted from a process, usually from a plasma, is collected and analyzed to identify and track changes in atomic and molecular species that indicate the state or progress of the process being monitored. In IEP applications, light is usually provided from an external source, e.g., a flash lamp, and directed at the workpiece. When reflected from the workpiece, the provided light carries information in the form of the workpiece reflectance that indicates the condition of the workpiece. Extracting and modeling the workpiece reflectance allows understanding of film thickness and feature size / depth / width, among other properties. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] U.S. Pat. No. 7,049,156 Summary of the Invention [Means for solving the problem]

[0005] In one embodiment, the present disclosure provides a method for processing spectral data. In one example, the method includes: (1) collecting a time-ordered sequence of optical emission spectroscopy data across one or more wavelengths, (2) extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data, (3) analyzing a quality of the one or more attributes, (4) determining an adjustment for the one or more attributes, (5) processing the one or more attributes according to a predetermined set of filters, adjustments, and quality, and (6) selecting a filter configuration for processing the spectral data based on the processing of the one or more attributes.

[0006] In another aspect, the present disclosure provides a method for controlling a semiconductor process. In one example, the method of controlling includes: (1) collecting optical emission spectroscopy data across one or more wavelengths, (2) processing the data using a preselected method selected to achieve minimal process delay in determining an endpoint indication, and (3) altering the semiconductor process based on the processing of the data.

[0007] In yet another aspect, the present disclosure provides a computing device. In one example, the computing device includes one or more processors that perform operations including: (1) collecting optical emission spectroscopy data across one or more wavelengths, (3) processing the data using a preselected method selected to achieve minimal process delay in determining an endpoint indication, and (3) altering a semiconductor process based on the processing of the data.

[0008] In yet another aspect, the present disclosure provides a computer program product having a set of operational instructions stored on a non-transitory computer readable medium that when initiated by the processor to direct the operation of one or more processors to perform operations for processing spectral data. In one example, the operations include: (1) collecting a time-ordered sequence of optical emission spectroscopy data across one or more wavelengths from a semiconductor process, (2) extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data, (3) analyzing characteristics of the one or more attributes, (4) determining adjustments for the one or more attributes, (5) processing the one or more attributes according to a predetermined set of filters, adjustments, and characteristics, and (6) selecting a filter configuration using one or more filters from the predetermined set of filters to process the spectral data based on the processing of the one or more attributes.

[0009] Reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which: [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 shows a block diagram of a system for monitoring and / or controlling the state of a plasma or non-plasma process in a semiconductor process tool using OES and / or IEP. [Diagram 2] FIG. 1 is a schematic diagram generally illustrating the functional elements of a typical area CCD sensor. [Diagram 3] FIG. 2 shows a plot of a typical OES light signal (spectrum) resulting from conversion of collected light in accordance with the present disclosure. [Figure 4] FIG. 4 illustrates a plot of a raw signal trend extracted from a digitized signal collected from an optical sensor, for example the OES optical signal of FIG. 3, in accordance with the present disclosure. [Diagram 5] 1 is a flow diagram of a method for collecting data from an optical sensor and processing the data for lower latency, increased repeatability, and other benefits in accordance with the present disclosure. [Figure 6A] FIG. 5 illustrates a plot of the noise evolution over time associated with the trend of FIG. 4, in accordance with the present disclosure. [Figure 6B] FIG. 5 illustrates a histogram plot of noise associated with the trend of FIG. 4, in accordance with the present disclosure. [Figure 6C] FIG. 5 illustrates a noise power spectral density plot associated with the trend of FIG. 4 in accordance with the present disclosure. [Figure 7] 5 illustrates a plot of selected estimated signals and features from the trend of FIG. 4 in accordance with the present disclosure. [Figure 8A] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8B] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8C] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8D] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8E] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8F] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 8G] 5A-5C show plots of various filtering methods applied to the trends of FIG. 4 in accordance with the present disclosure. [Figure 9] FIG. 5 illustrates a plot comparing the trend calculated endpoint latencies of FIG. 4 when various filters are applied, in accordance with the present disclosure. [Figure 10A] 5A-5C show plots of the trend of FIG. 4 variously filtered with and without adjustment in accordance with the present disclosure. [Figure 10B] 5A-5C show plots of the trend of FIG. 4 variously filtered with and without adjustment in accordance with the present disclosure. [Figure 11A]1A-1D are plots of representative IEP optical signal data that have been variously processed in accordance with the present disclosure. [Figure 11B] 1A-1D are plots of representative IEP optical signal data that have been variously processed in accordance with the present disclosure. [Figure 12] 1 is a block diagram of a spectrometer and certain associated systems in accordance with the present disclosure. [Figure 13] 1 illustrates a block diagram of an example computing device configured to apply spectral and trend processing to spectral data in accordance with the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] In the following description, reference is made to the accompanying drawings, which form a part hereof, and in which are shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be used. It is also to be understood that structural, procedural and system changes may be made without departing from the spirit and scope of the invention. Therefore, the following description should not be taken in a limiting sense. For clarity of illustration, similar features shown in the accompanying drawings are designated with similar reference numerals, and similar features as shown in alternative embodiments in the figures are designated with similar reference numerals. Other features of the invention will become apparent from the accompanying drawings and from the following detailed description. It should be noted that for clarity of illustration, certain elements in the figures may not be drawn to scale.

[0012] The constant advancement of semiconductor processes towards faster processes, smaller feature sizes, and more complex structures places great demands on process monitoring technologies. For example, higher data rates are required to accurately monitor much faster etch rates in very thin layers, where changes in angstroms (a few atomic layers) are significant in fin field effect transistors (FINFETs) and three-dimensional NAND (3D NAND) structures, etc. Wider optical bandwidths and greater signal-to-noise are often required for both OES and IEP methodologies to help detect small changes in either / both reflectance and optical emission. Cost and package size are also under constant pressure, as process equipment becomes more complex and expensive itself. All of these requirements drive increased performance in optical monitoring of semiconductor processes. Whether for OES or IEP methodologies, a key component of many optical monitoring systems is the spectrometer and their ability to consistently and accurately convert received optical data into electrical data for control and monitoring of semiconductor processes.

[0013] Thus, disclosed herein are processes, systems, and apparatus that provide improved processing of optical data for lower latency, increased repeatability, improved process stability, increased signal detectability, and other benefits through characterization of the effects of noise, tuning, and filter selection on optical trend data and / or optical features, collectively referred to as attributes. The improved processing can be used to more accurately and consistently monitor and control semiconductor processes.

[0014] With particular regard to monitoring and evaluating the state of a semiconductor process within a process tool, Figure 1 illustrates a block diagram of a process system 100 that uses OES and / or IEP to monitor and / or control the state of a plasma or non-plasma process within a semiconductor process tool 110. The semiconductor process tool 110, or simply process tool 110, generally contains a wafer 120 and possibly a process plasma 130 in a typically partially evacuated volume of a chamber 135 that may contain various process gases. The process tool 110 may include one or more optical interfaces, or simply interfaces, 140, 141, and 142, to enable observation of the chamber 135 at various locations and orientations. The interfaces 140, 141, and 142 may include multiple types of optical elements, such as, but not limited to, optical filters, lenses, windows, apertures, optical fibers, etc.

[0015] For IEP applications, the light source 150 may be connected to the interface 140 directly or via a fiber optic cable assembly 153. As shown in this configuration, the interface 140 is oriented perpendicular to the surface of the wafer 120 and is often centered with respect to the wafer 120. Light from the light source 150 may enter the interior volume of the chamber 135 in the form of a collimated beam 155. The beam 155 reflected from the wafer 120 may be re-received by the interface 140. In a general application, the interface 140 may be an optical collimator. Following reception by the interface 140, the light may be forwarded via a fiber optic cable assembly 157 to a spectrometer 160 for detection and conversion to a digital signal. The light may include light provided and detected, and may include, for example, a wavelength range from deep ultraviolet (DUV) to near infrared (NIR). Wavelengths of interest may be selected from any subrange of the wavelength range. For larger circuit boards, or when understanding wafer non-uniformity is a concern, an additional optical interface (not shown in FIG. 1 ) can be used, typically oriented with the wafer 120. The processing tool 110 can also include additional optical interfaces placed in different locations for other monitoring options.

[0016] For OES applications, interface 142 may be oriented to collect optical emissions from plasma 130. Interface 142 may simply be a viewport or may additionally include other optical components such as lenses, mirrors, and optical wavelength filters. Fiber optic cable assembly 159 may direct any collected light to spectrometer 160 for detection and conversion to a digital signal. Spectrometer 160 may include a CCD sensor and converter for detection and conversion, such as CCD sensor 200 and converter 250 of FIG. 2. Multiple interfaces may be used individually or in parallel to collect OES-related optical signals. For example, as shown in FIG. 1, interface 141 may be positioned to collect emissions from near the surface of wafer 120, while interface 142 may be positioned to view the bulk of plasma 130.

[0017] In many semiconductor processing applications, it is common to collect both OES and IEP optical signals, which presents several problems for using spectrometer 160. Typically, OES signals are continuous in time, while IEP signals may be either continuous in time or discrete / both. Mixing these signals creates a number of difficulties, since process control often requires detection of small changes in both OES and IEP signals, and inherent variations in either signal may mask observation of changes in the other signal. For example, it is not advantageous to support multiple spectrometers of each signal type due to the cost, complexity, and inconvenience of signal timing synchronization, calibration, and packaging.

[0018] After detection of the received optical signal by the spectrometer 160 and conversion to an analog electrical signal, the analog electrical signal is typically amplified and digitized within a subsystem of the spectrometer 160 and passed to the signal processor 170. The signal processor 170 may be, for example, an industrial PC, a PLC, or other system that uses one or more algorithms to generate an output 180, such as an analog or digital control value representing the intensity of a particular wavelength or the ratio of two wavelength bands. Instead of a separate device, the signal processor 170 may alternatively be integrated with the spectrometer 160. The signal processor 170 may use one or more OES algorithms to analyze the radiation intensity signal at a given wavelength and determine trend parameters that represent trends related to the state of the process, which may be used to access that state, e.g., end point detection, etch depth, etc. For IEP applications, the signal processor 170 may use one or more algorithms that analyze a broadband portion of the spectrum to determine the thickness of the film. See, for example, U.S. Patent No. 6,399,323, which is incorporated herein by reference. 11A and 11B show representative IEP optical signal data that has been variously processed in accordance with the present disclosure. Output 180 can be transmitted to process tool 110 via communication link 185 to monitor and / or modify the production process occurring within chamber 135 of process tool 110.

[0019] The components of FIG. 1 are simplified for convenience and are commonly known. In addition to common functions, the spectrometer 160 or the signal processor 170 may also be configured to identify stationary and transient optical and non-optical signals and process these signals according to the methods and / or features disclosed herein. As such, the spectrometer 160 or the signal processor 170 may include one or more algorithms, processing capabilities, and / or logic to identify and process optical signals and time trends extracted therefrom. The algorithms, processing capabilities, and / or logic may take the form of hardware, software, firmware, or any combination thereof. The algorithms, processing capabilities, and / or logic may be within one computing device or distributed across multiple devices, e.g., the spectrometer 160 and the signal processor 170.

[0020] 2 is a schematic diagram generally showing the functional elements of a conventional area CCD sensor 200. The sensor 200 includes an active pixel area 210, which may generally be divided into an array of individual pixels, such as 1024(H)×122(V), as in the S7031 CCD sensor from Hamamatsu, Japan. For definition and clarity, it is noted that in this specification, the use of "horizontal" and "vertical" when dealing with optical sensors refers to the long and short physical axes, respectively, of the optical sensor being discussed. In spectroscopy applications, it is common that the long / horizontal axis of an optical sensor is aligned with the direction of chromatic dispersion, while the short / vertical axis is associated with the imaging or collection of a defined light source or illuminated aperture, e.g., a fiber or optical slit.

[0021] The sensor 200 also includes a horizontal shift register 220 closest to the pixel area 210. An optical signal integrated into the sensor 200, e.g., from the fiber optic cable assembly 157 or 159, is typically read via shifting the accumulated charge in each pixel of the pixel area 210 vertically as indicated by arrow 230 in the horizontal shift register 220. All or a portion of the active pixel area 210 may be so shifted in a row-by-row manner. After the vertical shift, a horizontal shift may be performed as indicated by arrow 240. As each pixel of the horizontal shift register 220 is shifted (upwards in FIG. 2), its signal content may be converted by a converter 250 from an analog signal to a digital signal basis, e.g., from an analog electrical signal to a digital electrical signal. Subsequent handling and processing of the resulting digital data can occur internal or external to the spectrometer and can include averaging, curve fitting, thresholding, filtering, and / or other mathematical operations such as those described herein to obtain consistency in the processing of the spectral data and to reduce latency in the detection of one or more attributes.

[0022] Sensor 200 may further include one or more regions of unilluminated or partially illuminated elements, such as shift register elements 260 and 261 and pixel area elements 270, 271, and 272. In general, elements 260 and 261 may be referred to as "blank" pixels, and elements 270, 271, and 272 may be referred to as "bevel" pixels. One or more of these regions of elements may be included in sensor 200 to provide a characterization of non-optical signal levels inherent to sensor 200. Non-optical signals may generally include signal offsets, signal transients, and other forms of signal variations driven by temperature or other non-optical factors.

[0023] FIG. 3 shows a plot 300 that provides a picture of a typical OES light signal (spectrum) 320 that may be collected via a spectrometer, e.g., spectrometer 160 of FIG. 1, as it evolves over time with the semiconductor process being monitored and from which continuous trends may be extracted and processed as described herein. Plot 300 has an x-axis in units of wavelength and a y-axis in units of total signal. Spectrum 320 may be derived from light incident on a sensor, e.g., sensor 200 of FIG. 2. Spectrum 320 shows features characteristic of both molecular (broadband structure near 400 nm) and atomic emissions (much narrower peaks). One example of a narrow peak, narrow feature 330, corresponds to the 656 nm radiation of hydrogen and may be extracted for use in monitoring and endpointing a semiconductor etch process.

[0024] FIG. 4 shows a plot 400 of raw signal trend 410 that may be extracted from a time series of spectra, such as OES optical spectrum 320 of FIG. 3. Plot 400 has an x-axis of time (seconds) and a y-axis of signal totals. Specifically, trend 410 may be created by selecting a range of spectral values ​​that occur closest to the spectral feature of interest. For example, to monitor 656 nm hydrogen emissions, such as those represented by narrow feature 330 in FIG. 3, values ​​corresponding to the spectral region from 655 to 657 may be averaged or summed and stored in a time-organized array to create trend 410. Due to optical calibration and resolution limitations, the spectral features have a finite width in the collected spectra, and a spectral region wider than the actual radiation width may be used for processing. Trend 410 was collected over a 5 second period, which generally corresponds to a high speed semiconductor process. The individual points of trend 410 and the initial corresponding spectra may be collected at an adjustable rate suitable for analysis. In this example, the trend 410 is collected at 50 samples per second, but may be collected at rates ranging from a few samples per second to 100 samples per second. The sampling rate and resulting number of points in the trend may be adjusted to best suit the process and control requirements as described herein, and the described process may be run at one or more sampling rates to determine favorable results. Note that the trend 410 is shown after collection and is therefore non-real-time and may include additional data both before and after a particular end-point step or process being monitored. A real-time data update would include the portion of the trend 410 up to the current process and / or collection time. Trends applicable to processes as described herein may include, for example, single wavelength trends, multiple wavelength trends, and / or combinations of wavelength trends such as ratios, products, sums, and differences.

[0025] FIG. 5 shows a flow diagram of one example method 500 of reading data from an optical sensor and processing the data for lower latency, increased repeatability, and other benefits. It is noted that method 500 may include steps performed in real time or non-real time during the controlled process, before the controlled process, and / or after the controlled process. Real time may be defined as occurring during active control or monitoring of a process. Real time may be associated with causal processing since the data includes only current and past times. Non-causal processing after the data is collected includes data at times representing before, during, at, and after the monitoring event.

[0026] Method 500 begins with a preparatory step 510 during which any preparatory actions may be taken. These actions may include mechanical connection of optical measurement system components, selection of a sampling rate of a spectrometer, and determination of a spectral line or feature of interest. Step 510 is an example of a step of method 500 that may be performed prior to a controlled process. After any preparatory actions, method 500 proceeds to step 520 where spectral data may be collected. Spectral data may be collected using a spectrometer and accessories as described in accordance with Figures 1 and 2 above.

[0027] At step 530, trend data from one or more trends may be extracted from the spectral data collected during step 520. For real-time analysis and control, the individual trend value extraction is approximately contemporaneous with the collection of each spectrum included within the spectral data collected during step 520. For non-real-time analysis and control, trend extraction may occur after the collection of any or all portions of the spectral data at step 520. Trends, such as trend 410 of FIG. 4, may be extracted from various samples of the collected spectral data. Then, at step 540, one or more characteristics of the trend data are analyzed. Characteristics determined and analyzed from the trend data may include, for example, noise characteristics, signal estimates, endpoint characteristics, endpoint detectability, and / or signal to noise assessments, such as those discussed below in connection with FIGS. 6-9. At step 550, the trend data is adjusted. Before, after, and / or contemporaneous with the analysis of the trend data at step 540, the trend data may be adjusted at step 550. Conditioning may include, for example, scaling, normalization, standardization, grading, offset adjustment, or other mathematical operations that aid in trend data processing. In general, conditioning of data improves its usefulness and applicability to the control application in which it is used. For example, offset adjustment may be applied to trend data to remove undesirable DC signal offsets from trend data whose primary information content is encoded in the fluctuations in the trends rather than in the general signal values. Additionally, grading may be used to remove systematic common mode noise and / or signal fluctuations that may complicate subsequent trend data processing.

[0028] In step 560, the trend data is processed based on the analysis of the characteristics in step 540 and the adjustment of the trend data in step 550. The trend data may be processed in real time or post-processed after collection to apply and evaluate adjustments and filter combinations such as those described herein below with respect to Figures 6-10. The trend data may be selected and obtained from one or more trends extracted in step 530. Processing the trend data may include understanding the signal and noise associated with the signal and then deciding how to process the trend data or going through different techniques to optimize how to process. Determining how to process may include testing and evaluating different filters and / or combinations of filters, such as those described herein, for trend data having different values. The desired result of the processing is consistency in identifying features and the amount of time (latency) between the "real" time of occurrence of the feature and the actual time of identification. For example, a feature ideally occurred at time 5 seconds but was not identified / detected until time 5.5 seconds with a resulting latency of 0.5 seconds. Processing may not include identifying a particular trend, but may be directed to identifying the absence of one or more features that identify a particular trend. Thus, processing of step 560 may occur with defined metrics that include, for example, identifying a particular trend, identifying a feature (a particular process metric), and / or a combination of both.

[0029] In step 570, one or more semiconductor processes are altered based on the analysis, adjustments, processing, or combinations thereof of steps 540-560 of method 500. Provided that method 500 is applied in real time, the semiconductor process may be altered in real time, and the semiconductor process may be the process for which the spectral data is collected in step 520. Another semiconductor process may also be altered in non-real time of the present semiconductor process of step 520. As an example of non-real time processing of trend data, a description of the processing and analysis methodology of trend data from method 500, or portions thereof, may be stored and programmed into a control system for later use during another subsequent real time semiconductor process. The description of the processing and analysis of trend data may include, for example, a number of mathematical operations, equations, formulas, and processes applied to the data to effect the adjustments and processing as described herein. Descriptions of the processing and analysis of trend data may be stored and / or programmed, for example, in spectrometer 160 or signal processor 170 of process system 100, memory / storage 1190 of optical system 1100, FPGA 1160, processor 1170, and / or external system 1120, and / or memory 1234, processor 1236 of computing device 1200. Memory / storage 1190 and memory 1234 may be non-transitory computer readable media.

[0030] Method 500 continues with step 580, where it ends. During real-time processing, step 580 may include ending the semiconductor process and storing the associated data for future analysis. It should be noted that method 500 may be performed any number of times and may be designed to be updated based on additional characterization, analysis, and processing, in real-time or non-real-time.

[0031] Working with non-real-time data such as trend 410 allows for the application of non-causal signal processing such as Savitzky-Golay filtering to be applied to the collected trends to allow for signal estimation and noise extraction and characterization. Savitzky-Golay filtering as well as other filtering processes such as Weiner filters and other general "matched filters" may be used causally (usually real-time) or non-causally (usually non-real-time). FIG. 6A shows a plot 600 and trend 610 of noise associated with the trend of FIG. 4 as extracted via processing with a low order polynomial Savitzky-Golay filter. FIG. 6A has an x-axis in units of time and a y-axis in units of noise total count. Similarly, FIG. 6B shows a histogram plot 650 of noise associated with the trend of FIG. 4. FIG. 6B has an x-axis in units of noise total count and a y-axis in units of "number of occurrences." In addition, FIG. 6C shows a power spectral density plot 670 of the noise associated with the trends of FIG. 4. FIG. 6C has an x-axis in units of frequency and a y-axis in units of power spectral density (dB / Hz). Noise processing and analysis methods support further processing of the trends and provide insight into the time and frequency variations of the noise amplitude. For example, the power spectral density plot 670 shows a clear frequency distribution of the noise and its variations below about 3 Hz that are not immediately apparent in a time or histogram plot.

[0032] FIG. 7 shows a plot 700 of an estimated signal 720 and selected features 730 and 740 from the trend 410 of FIG. 4. FIG. 7 has an x-axis in time and a y-axis in counts. Vertical lines indicate peak and trough inflection point locations in time based on a non-causal Savitsky-Golay first derivative estimate. These inflection points and other features may be useful in characterizing various processing methods and resulting feature detection latencies that may further relate to the determination of end points and other process control events. For example, a control system that processes a control trend according to the methods described herein would first identify an inflection point 730 and then an inflection point 740, signaling an end point time a few seconds or samples after the identification of the inflection point 740. Thus, the inflection points 730 and 740 are points of change (trend features) identified via a non-causal analysis (control points) that may need to be controlled.

[0033] Figures 8A-8G show plots 800, 815, 830, 845, 855, 875, and 890 of various filtering methods applied to the trends of Figure 4. Figures 8A-8G each have an x-axis in units of time and a y-axis in units of counts. Inflection points 730 and 740 are included in each of plots 800, 815, 830, 845, 855, 875, and 890. For the illustrative example described below, the following table (Table 1) summarizes the various filters and adjustable parameters corresponding to the plots of Figures 8A-8G:

[0034] [Table 1]

[0035] Plots 800, 815, 830, 845, 855, 875, and 890 each show the output trends resulting from applying each filter type (each of the preceding plots) over its range of parameter values. For each filter type and each range of filter parameter values, variations in noise reduction, signal offset, signal gain, and trend delay can be observed. For example, an increasing tradeoff between delay and noise reduction can be observed in plot 800 for the IIR filter and plot 815 for the averaging filter. Similarly, for plots 830 and 845, Butterworth and Elliptic filters, respectively, high noise reduction and large delay are observed for certain values ​​of each filter.

[0036] Plot 890 of Figure 8G shows an expanded detail of plot 875 of Figure 8F to more clearly show the noise reduction and other changes to the trend provided by various configurations of the combined Savitsky-Golay and four-sample averaging filter operation. Specifically, for the majority of configurations, the detection time of the inflection point 740 is determined quite consistently with no delay observed for certain other filters.

[0037] FIG. 9 shows a plot 900 comparing the calculated endpoint latency of the trends of FIG. 4 when various filters are applied. For example, since the example trends are approximately second order polynomials, a causal implementation of Savitzky-Golay filtering (a "smooth" filter) with a polynomial order fixed at "2" can be applied and typically provides low latency results. For other trends that approximate other orders of polynomials, the polynomial order of the filter can be changed. For Savitzky-Golay filtering, the inflection points should also be appropriately separated by a number of samples that coincide with the filter window length. For Butterworth and elliptic low-pass filters, the noise spectrum (the noise power at about 3.5 Hz is about 40 dB higher than the noise at DC) suggests that low-pass filters can be effective for processing, but these filters generally result in an overall increase in delay due to the increase in filter complexity. The filter specified by smooth(Avg2(n=4)) achieves low latency and is largely insensitive to the smooth() length parameter by benefiting from the combination of an appropriate model-based estimation (a second order polynomial) and a short moving average.

[0038] 10A and 10B show plots 1000 and 1050 of the trend of FIG. 4 variously filtered with and without adjustment. The legend of FIG. 9 also applies to FIGS. 10A and 10B. Without signal adjustment, multiple filter implementations may rely on transients and other responses that perturb the expected performance metrics of the filter when applied to the trend (latency, smoothing, gain, ringing, settling time, etc.). Transients and ringing as easily noticeable within the first second for all trends are shown in plot 1000. Adjustments may include one or more manipulations of the data in the trend to mitigate undesirable perturbations. Adjustments may include scaling, normalization, standardization, ranking, offset adjustments, or other mathematical operations. For example, an adjustment applied to the trend of plot 1050 includes subtracting the first value of the trend from all subsequent values ​​prior to application of the filter. It can be observed that in plot 1050, when compared to plot 1000, transients and ringing are absent. An alternative adjustment of these same trends may include subtracting the average of multiple initial values ​​from all subsequent values.

[0039] Although the above examples are performed for processing and analysis of trend data, such as a single value over a range of time, or otherwise called scalar trend data, the methods and processes may be applied to multi-valued data, where multiple values ​​are associated with each time point (so-called vector trend data). This type of data is more commonly associated with IEP optical data. FIGS. 11A and 11B are plots of representative IEP optical signal data collected and processed in various ways according to the present disclosure. Both figures have an x-axis in units of wavelength and a y-axis in units of count. Plot 1100 of FIG. 11A includes samples of IEP spectra collected at two different times. Specifically, data 1110 is from an earlier time than data 1120. A comparison of data 1110 and 1120 shows that there are complex differences in the signals over the wavelength range of about 325 nm to 800 nm. These differences may be more clearly exposed by processing, filtering, and conditioning as discussed herein. In plot 1150 of FIG. 11B, data 1160 is the subtraction of data 1110 and 1120 with an offset adjustment applied to each data set prior to subtraction. The complex difference in signal is more clearly expressed as an oscillating set of features, but there is a strong residual signal (e.g., spikes near the peak at 520 nm) that is a result of fluctuations in the flash lamp used during data collection. In the control case, where detection of the peak near 520 nm is critical, the residual signal obscures this detection. Data 1170 is a filtered version of data 1160 where a Savitsky-Golay filter has been applied. Similar to the filtered trends of FIGS. 8A-8G, noise reduction can be observed, but a significant phase shift was introduced by this filtering process. Furthermore, similar to the trends in FIGS. 8A-8G, various filters can be reviewed to determine those with the best desired results, such as minimal latency or maximized attribute detectability.

[0040] 12 is a block diagram of an optical system 1200 including a spectrometer 1210 and certain associated systems according to one embodiment of the present disclosure. The spectrometer 1210 can incorporate systems, features, and methods disclosed herein that are advantageous for measuring, characterizing, analyzing, and processing optical signals from semiconductor processes, and can be associated with the spectrometer 160 of FIG. 1. The spectrometer 1210 can receive optical signals from an external optical component 1230, for example, via a fiber optic cable assembly 157 or 159, and can transmit data, following integration and conversion, for example, output 180 of FIG. 1, to an external system 1220 that can also be used to control the spectrometer 1210, for example, by selecting a mode of operation or controlling integration timing as defined herein. The spectrometer 1210 can include an optical interface 1240, such as a subminiature assembly (SMA) or ferrule connector (FC) fiber optic connector or other opto-mechanical interface. Further optical components 1245, such as slits, lenses, filters, and gratings, can act to shape, direct, and color-separate the received optical signals and direct them to the sensor 1250 for integration and conversion. The sensor 1250 can be associated with the sensor 200 of FIG. 2. The low-level functions of the sensor 1250 can be controlled by elements such as the FPGA 1260 and the processor 1270. Following the optical-to-electrical conversion, the analog signal can be directed to an A / D converter 1280 and converted from the electrical analog signal to an electrical digital signal that can then be stored in memory 1290 for immediate or later use and transmission, for example, to an external system 1220 (see signal processor 170 of FIG. 1). Certain interfaces and relationships are indicated by arrows, although not all interactions and control relationships are shown in FIG. 12. The spectral data shown in FIG. 3 may be collected, stored and / or acted upon, for example, according to one or more steps of process 500 of FIG. 5 and within / by one or more of memory / storage 1290, FPGA 1260, processor 1270 and / or external system 1220.As such, spectrometer 1200 can be configured (i.e., designed, built, or programmed with the necessary logic and / or features to perform one or more tasks) to process signals by testing and evaluating different filters and / or combinations of filters at different values ​​based on detection consistency and latency. Spectrometer 1210 also includes a power supply 1295, which can be a conventional AC or DC power supply typically included with a spectrometer.

[0041] 13 illustrates a computing device 1300 that can be used for processes disclosed herein, such as identifying signals in spectral data and processing signals. The computing device 1300 can be a spectrometer or a portion of a spectrometer disclosed herein, such as spectrometer 160 or 1210. The computing device 1300 can include at least one interface 1332, a memory 1334, and a processor 1336. The interface 1332 includes the necessary hardware, software, or combinations thereof for receiving, e.g., raw spectral data, and for transmitting, e.g., processed spectral data. A portion of the interface 1332 can also include the necessary hardware, software, or combinations thereof for communicating analog or digital electrical signals. The interface 1332 can be a conventional interface that communicates over various communication systems, connections, buses, etc. according to a protocol, e.g., a standard protocol or a proprietary protocol (e.g., the interface 1332 can support I2C, USB, RS232, SPI, or MODBUS).

[0042] The memory 1334 is configured to store various software and digital data aspects related to the computing device 1300. Additionally, the memory 1334 is configured to store a set of operating instructions corresponding to one or more algorithms that direct the operation of the processor 1336 when initiated, for example, to identify anomalous signals in the spectral data and to process the identified anomalous signals. The process 500 and its variations are representative examples of algorithms. Processing may include removing or modifying the signal data or different actions. The memory 1334 may be a non-transitory computer-readable medium (e.g., flash memory and / or other media).

[0043] The processor 1336 is configured to direct the operation of the computing device 1300. As such, the processor 1336 includes the necessary logic for communicating with the interface 1332 and the memory 1334, and for performing the functions described herein, for example, for identifying and processing anomalous signals in the spectral data, in one or more of the steps of the method 500.

[0044] Portions of the aforementioned devices, systems or methods may be implemented in or performed by various, e.g., conventional, digital data processors or computers, where the computer is programmed or stores an executable program of a sequence of software instructions to perform one or more of the steps of the method. Such program or code software instructions may represent algorithms and may be encoded in a machine-executable form in various types of non-transitory digital data storage media, e.g., magnetic or optical disks, random access memories (RAM), magnetic hard disks, flash memories, and / or read-only memories (ROM), to enable the digital data processors or computers to perform one, several, or all of the steps of one or more of the aforementioned methods, or functions, systems, or devices described herein.

[0045] Portions of the disclosed embodiments may relate to a computer storage product having a non-transitory computer-readable medium having program code thereon for performing various computer-implemented operations to implement portions of an apparatus, device, or to implement steps of the methods described herein. In this specification, non-transitory refers to all computer-readable media, except for transitory, propagating signals. Examples of non-transitory computer-readable media include, but are not limited to: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROM disks; magneto-optical media, such as floptical disks; and hardware devices specially configured to store and execute program code, e.g., ROM and RAM devices. Examples of program code include both machine code, e.g., produced by a compiler, and files containing higher level code that may be executed by a computer using an interpreter. Configured means, for example, designed, constructed, or programmed with the necessary logic, algorithms, processing instructions, and / or characteristics to perform one or more tasks.

[0046] The foregoing and other modifications may be made in the optical measurement systems and subsystems described herein without departing from the scope thereof. For example, while certain examples are described in connection with semiconductor wafer processing equipment, it may be understood that the optical measurement systems described herein may be adapted to other types of processing equipment, such as roll-to-roll thin film processing, solar cell fabrication, or any application in which high precision optical measurements may be required. Additionally, while certain embodiments discussed herein describe the use of a general optical analysis device, e.g., an imaging spectrograph, it is understood that multiple optical analysis devices having known relative sensitivities may be used. Additionally, although the term "wafer" is used herein when describing aspects of the present invention, it is understood that other types of workpieces may be used, including quartz crystals, phase shift masks, LED circuit boards, and other non-semiconductor processing related circuit boards, as well as solid, gas, and liquid workpieces.

[0047] The exemplary embodiments described herein have been selected and described in order to best explain the principles and practical applications of the invention, and to enable others skilled in the art to understand the invention in various embodiments with various modifications as suitable for the particular use contemplated. The specific embodiments described herein are not intended to limit the scope of the invention in any way, since the invention may be implemented in various modifications and environments without departing from the scope and spirit of the invention. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein.

[0048] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart illustrations, as well as combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a dedicated hardware-based system that performs the specified functions or acts, or a combination of dedicated hardware and computer instructions.

[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will be understood that the terms "comprises" and / or "comprises" as used herein specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0050] It will be appreciated by those skilled in the art that the present invention may be embodied as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all generally referred to herein as "circuits" or "modules." Furthermore, the present invention may take the form of a computer program product in the form of a computer-usable storage medium having computer-usable program code embodied in the medium.

[0051] Various aspects of the present disclosure may be claimed, including the apparatus, systems, and methods disclosed herein. Aspects disclosed herein and mentioned in the Summary of the Invention include:

[0052] A. A method for processing spectral data, comprising: (1) collecting a time-ordered sequence of optical emission spectroscopy data across one or more wavelengths; (2) extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data; (3) analyzing characteristics of the one or more attributes; (4) determining adjustments for the one or more attributes; (5) processing the one or more attributes according to a predetermined set of filters, adjustments, and characteristics; and (6) selecting a filter configuration for processing the spectral data based on the processing of the one or more attributes.

[0053] B. A method for controlling a semiconductor process comprising: (1) collecting optical emission spectroscopy data across one or more wavelengths; (2) processing the data using a preselected method selected to achieve minimal process delay in determining an endpoint indication; and (3) altering the semiconductor process based on the processing of the data.

[0054] C. A computing device having one or more processors that perform operations including: (1) collecting optical emission spectroscopy data across one or more wavelengths; (3) processing the data using a preselected method selected to achieve minimal process delay in determining an endpoint indication; and (3) altering a semiconductor process based on the processing of the data.

[0055] D. A computer program product having a set of operational instructions stored on a non-transitory computer readable medium that when initiated by it directs operation of one or more processors to perform operations for processing spectral data. In one illustrative example, the operations include: (1) collecting a time-ordered sequence of optical emission spectroscopy data across one or more wavelengths from a semiconductor process, (2) extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data, (3) analyzing characteristics of the one or more attributes, (4) determining adjustments for the one or more attributes, (5) processing the one or more attributes according to a predetermined set of filters, adjustments, and characteristics, and (6) selecting a filter configuration using one or more filters from the predetermined set of filters to process the spectral data based on the processing of the one or more attributes.

[0056] Each of aspects A, B, C, and D may have one or more of the following additional elements in combination: Element 1: where the set of filters includes a single filter. Element 2: where the set of filters includes at least one filter selected from a group of filters consisting of infinite impulse response filters, averaging filters, Butterworth filters, elliptic filters, Savitzky-Golay smoothing filters, and Savitzky-Golay smoothing / averaging filters. Element 3: where the processing of the one or more attributes includes changing parameter values ​​of at least one filter of the set of filters. Element 4: where the collecting, extracting, analyzing, determining, and processing of the one or more attributes is in real time. Element 5: where the filter configuration includes filters from a predetermined set of filters, and the processing of the spectral data is in real time. Element 6: where the selection is based on consistency and latency of detection of the one or more attributes during the processing of the one or more attributes. Element 7: Wherein, the one or more attributes include one or more trends, one or more features, or a combination of one or more trends and one or more features. Element 8: Wherein, optical emission spectroscopy data is received by a spectrometer from a processing tool. Element 9: Wherein, the filter configuration includes a filter from a predetermined set of filters. Element 10: Wherein, a preselected method is selected by extracting one or more attributes from a time-ordered sequence of optical emission spectroscopy data, analyzing a characteristic of the one or more attributes, determining an adjustment of the one or more attributes, processing the one or more attributes according to the predetermined set of filters, characteristics, and adjustments, and selecting the preselected method based on the processing of the one or more attributes. Element 11: Wherein, the one or more attributes include one or more trends, one or more features, or a combination of one or more trends and one or more features. Element 12: Wherein, optical emission spectroscopy data is collected from a semiconductor process.Element 13: Wherein, a preselected method is selected by extracting one or more attributes from the time-ordered sequence of optical emission spectroscopy data, analyzing characteristics of the one or more attributes, determining adjustments for the one or more attributes, processing the one or more attributes according to a predetermined set of filters, characteristics, and adjustments, and selecting the preselected method based on the processing of the one or more attributes. Element 14: Wherein, the one or more attributes include one or more trends. Element 15: Wherein, the one or more attributes further include one or more features or a combination of one or more trends and one or more features. Element 16: Wherein, the computing device is a spectrometer.

Claims

1. A method for processing a real-time time-ordered sequence of optical emission spectroscopy data for semiconductor process control, comprising: collecting a non-real-time time-ordered sequence of optical emission spectroscopy data across one or more wavelengths; extracting one or more attributes from the non-real-time time-ordered sequence of optical emission spectroscopy data; analyzing a quality of the one or more attributes; determining an adjustment to the one or more attributes; processing said one or more attributes according to a set of predefined filters, each filter having a corresponding predefined parameter range, said adjustments, and said characterization; selecting a filter from the set of predefined filters and a parameter from the corresponding predefined parameter range for processing the real-time time-ordered sequence based on the processing of the one or more attributes; A method comprising:

2. The method of claim 1 , wherein the set of filters comprises a single filter.

3. The set of filters: Infinite Impulse Response Filters, Averaging filter, Butterworth filter, Elliptic filter, Savitsky-Golay smoothing filter, and Savitsky-Golay smoothing / averaging filter 2. The method of claim 1, further comprising at least one filter selected from the group of filters consisting of:

4. The method of claim 1 , wherein the processing of the one or more attributes comprises changing a parameter value of at least one filter of the set of filters.

5. The method of claim 1 , wherein the collecting, extracting, analyzing, determining, and processing of the one or more attributes is in real time.

6. The method of claim 5 , wherein the processing of the real-time chronological sequence is in real-time.

7. The method of claim 1 , wherein the selecting step is based on consistency and latency of detection of the one or more attributes during the processing of the one or more attributes.

8. The method of claim 1 , wherein the one or more attributes include one or more trends, one or more characteristics, or a combination of one or more trends and one or more characteristics.

9. The method of claim 1 , wherein the optical emission spectroscopy data is received by a spectrometer from a processing tool.

10. 1. A method for controlling a semiconductor process, comprising: collecting a non-real-time time-ordered sequence of optical emission spectroscopy data across one or more wavelengths; processing the real-time time-ordered sequence of optical emission spectroscopy data using a preselected method selected to achieve minimal process delay in determining an endpoint indication; altering the semiconductor process based on the processing of the real-time chronological sequence; the preselecting method includes: extracting one or more attributes from the non-real-time time-ordered sequence; analyzing characteristics of the one or more attributes; determining adjustments for the one or more attributes; processing the one or more attributes according to a set of predefined filters, each filter having a corresponding predefined parameter range, the adjustments, and the characteristics; and based on the processing of the one or more attributes, selecting a filter from the set of predefined filters and selecting a parameter from the corresponding predefined parameter range for processing the real-time time-ordered sequence.

11. The method of claim 10 , wherein the one or more attributes include one or more trends, one or more characteristics, or a combination of one or more trends and one or more characteristics.

12. The method of claim 10 , wherein the optical emission spectroscopy data is collected from the semiconductor process.

13. collecting a non-real-time time-ordered sequence of optical emission spectroscopy data across one or more wavelengths; processing the real-time time-ordered sequence of optical emission spectroscopy data using a preselected methodology selected to achieve minimal process delay in determining an endpoint indication; modifying a semiconductor process based on said processing of said real-time time-ordered sequence; One or more processors performing operations including A computing device comprising: A computing device, wherein the preselection method includes extracting one or more attributes from the non-real-time time-ordered sequence, analyzing characteristics of the one or more attributes, determining adjustments for the one or more attributes, processing the one or more attributes according to a set of predetermined filters, each filter having a corresponding predetermined parameter range, the adjustments, and the characteristics, and based on the processing of the one or more attributes, selecting a filter from the set of predetermined filters and selecting a parameter from the corresponding predetermined parameter range for processing the real-time time-ordered sequence.

14. The computing device of claim 13 , wherein the one or more attributes include one or more trends.

15. The computing device of claim 14 , wherein the one or more attributes further comprise one or more characteristics or a combination of the one or more tendencies and the one or more characteristics.

16. The computing device of claim 13 , wherein the computing device is a spectrometer.

17. A computer program product having a set of operational instructions stored on a non-transitory computer readable medium that directs the operation of one or more processors when initiated by the processor to perform operations for processing a real-time time-sequence of optical emission spectroscopy data for semiconductor process control, the operations comprising: collecting a non-real-time time-ordered sequence of optical emission spectroscopy data across one or more wavelengths from a semiconductor process; extracting one or more attributes from the non-real-time time-ordered sequence of optical emission spectroscopy data; analyzing a quality of the one or more attributes; determining an adjustment to the one or more attributes; processing said one or more attributes according to a set of predefined filters, each filter having a corresponding predefined parameter range, said adjustments, and said characterization; selecting a filter from the set of predefined filters and a parameter from the corresponding predefined parameter range for processing the real-time time-ordered sequence based on the processing of the one or more attributes; 2. A computer program product comprising:

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