CN117053921A

CN117053921A - Systems, apparatus, and methods for improving background correction and calibration of optical devices

Info

Publication number: CN117053921A
Application number: CN202310544693.XA
Authority: CN
Inventors: J·科利斯; C·皮兰特; A·奎尼
Original assignee: Verity Instruments Inc
Current assignee: Verity Instruments Inc
Priority date: 2022-05-13
Filing date: 2023-05-15
Publication date: 2023-11-14

Abstract

The present disclosure relates to a system, apparatus, and method for improving background correction and calibration of an optical device. The present disclosure provides features for improving the processing of optical data by identifying and characterizing stationary and transient signals in electrical data representative of data collected from optical sensors of an optical measurement system. In one example, an optical measurement system is disclosed comprising: (1) A pixel area of an optical sensor having a plurality of pixels storing charge corresponding to a received optical signal, (2) one or more reduced illumination areas providing a signal level inherent to the optical sensor; and (3) one or more processors configured to adjust a digital representation of the charge from the pixels of the pixel region during active operation of the system using a characterization of the signal level from the one or more reduced illumination regions.

Description

Systems, apparatus, and methods for improving background correction and calibration of optical devices

Cross Reference to Related Applications

The present application claims the benefit of U.S. provisional application No. 63/341,869 entitled "system, apparatus, and method for improving background correction and calibration of spectrometers (SYSTEM, APPARATUS, AND METHOD FOR IMPROVED BACKGROUND CORRECTION AND CALIBRATION FOR SPECTROMETERS)" filed by John corles et al at 2022, month 5, day 13, which is commonly assigned with the present application and incorporated herein by reference in its entirety.

Technical Field

The present disclosure relates generally to spectroscopy systems and methods of use, and more particularly to improved background correction and calibration of data recorded from imaging and non-imaging spectrometers used during semiconductor processing.

Background

Optical monitoring of semiconductor processes is an effective method for controlling processes such as etching, deposition, chemical mechanical polishing, and implantation. Optical Emission Spectroscopy (OES) and Interferometry Endpoint (IEP) are two basic types of data collection modes of operation. In OES applications, light emitted from a process (typically from a plasma) is collected and analyzed to identify and track changes in atomic and molecular species that are indicative of the state or progress of the process being monitored. In IEP applications, light is typically supplied from an external source (e.g., a flash lamp) and directed onto a workpiece. After reflection from the workpiece, the emitted light carries information in the form of reflectivity of the workpiece, which information is indicative of the state of the workpiece. Extraction and modeling of workpiece reflectivity enables knowledge of film thickness and feature size/depth/width, as well as other characteristics.

Disclosure of Invention

In one aspect, the present disclosure provides an optical measurement system. In one example, the optical measurement system includes: (1) A pixel area of an optical sensor having a plurality of pixels storing charge corresponding to a received optical signal, (2) one or more reduced illumination areas providing a signal level inherent to the optical sensor; and (3) one or more processors configured to adjust a digital representation of the charge from the pixels of the pixel region during active operation of the system using a characterization of the signal level from the one or more reduced illumination regions.

The present disclosure also provides a method of processing optical data. In one example, the method includes: (1) Defining a dark level estimate for an optical sensor receiving the optical data; (2) Collecting active pixel values from active pixels of the optical sensor; (3) Generating a dark subtracted pixel value from the active pixel value and the dark level estimate; and (4) generating corrected active pixel values by modifying the active pixel values based on the dark subtracted pixel values.

In yet another aspect, the present disclosure provides a computer program product having a series of operational instructions stored on a non-transitory computer readable medium that, when activated, directs one or more processors to perform operations for adjusting a digital representation of charge of pixels from a pixel region of an optical sensor. In one example, the operations include: (1) Characterizing a signal level of a pixel from the optical sensor, wherein the pixel includes pixels from one or more reduced illumination areas of the optical sensor; and (2) adjusting a digital representation of the charge using the characterization.

Drawings

Reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram of a system for monitoring and/or controlling the state of a plasma or non-plasma process within a semiconductor process tool using OES and/or IEP;

FIG. 2 is a schematic diagram generally depicting the functional elements of a typical area array CCD sensor;

FIG. 3A is a graph of a typical OES optical signal (spectrum) resulting from the conversion of collected light in accordance with the principles of the present disclosure;

FIG. 3B is a graph of an example of a background signal generated by the occurrence of a non-optical signal source in accordance with the principles of the present disclosure;

FIG. 4 is a graph indicating how a background signal may be affected by operating temperature and sampling frequency in accordance with the principles of the present disclosure;

FIGS. 5A and 5B are a set of graphs detailing transient effects on background signals affected by sampling frequency and thermal variations in accordance with the principles of the present disclosure;

FIG. 6 is a flow chart of an example method of reading spectral data from a CCD device and processing the spectral data by applying background correction and calibration to those signals in accordance with the principles of the present disclosure;

FIG. 7 is a block diagram of a spectrometer and particular related systems in accordance with the principles of the present disclosure; and

fig. 8 illustrates a block diagram of an example of a computing system configured to apply background correction and calibration to spectral data in accordance with the principles of the present disclosure.

Detailed Description

In the following description, reference is made to the accompanying drawings which form a part hereof, and in which is 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 utilized. It is also to be understood that structural, procedural and system changes may be made without departing from the spirit and scope of the present invention. The following description is, therefore, not to be taken in a limiting sense. For clarity of explanation, identical features shown in the drawings are indicated with identical reference numerals, and similar features shown in alternative embodiments in the drawings are indicated with similar reference numerals. Other features of the present invention will be apparent from the accompanying drawings and from the detailed description that follows. It should be noted that for clarity of illustration, certain elements in the figures may not be drawn to scale.

The continual progress of semiconductor processes toward faster processes, smaller feature sizes, and more complex structures places high demands on process monitoring techniques. For example, higher data rates are needed to accurately monitor faster etch rates on very thin layers, where variations in angstroms (a few atomic layers) are critical for field effect transistors (finfets) and 3D NAND structures, for example. In many cases, both OES and IEP methods require a wider optical bandwidth and a greater signal-to-noise ratio to help detect small changes in reflectivity, light emission, or both. Cost and package size are also facing continued pressure as process equipment itself becomes more complex and expensive. All of these requirements seek to improve the optical monitoring performance of semiconductor processes. Whether OES or IEP methods, an important component of many optical monitoring systems is the measurement device or system, such as a spectrometer, and their ability to consistently and accurately convert received optical data into electrical data to control and monitor semiconductor processes.

Accordingly, processes, systems, and devices are disclosed herein that provide improved processing of optical data by identifying and characterizing stationary and transient signals in electrical data that represent data collected from optical sensors within an optical measurement system. The electrical data collected from the optical sensor may generally include optical data from the conversion of incident light and non-optical data generated internally to the sensor or other system component that is not derived from the incident light. Since non-optical data does not indicate the process being monitored, it is desirable that this particular data should be as stable and minimized as possible to avoid improper characterization of the monitored process. The improved processing may also include modifying the electrical signal or data associated with the electrical signal in the analog or digital signal domain to provide a more realistic representation of the optical data, as well as isolating different types of variations in signal levels associated with, for example, non-optical data. The improved process may be used to more accurately monitor semiconductor processes.

In particular, with respect to monitoring and assessing the status of a semiconductor process within a process tool, fig. 1 illustrates a block diagram of a process system 100 utilizing OES and/or IEPs to monitor and/or control the status of a plasma or non-plasma process within a semiconductor process tool 110. The semiconductor process tool 110, or simply, the process tool 110, generally encloses a wafer 120 and possibly a process plasma 130 in a generally partially evacuated volume of a chamber 135, which may contain various process gases. The process tool 110 may include or simply be referred to as one or more optical interfaces 140, 141, and 142 to permit observation within the chamber 135 at various positions and orientations. Interfaces 140, 141, and 142 may include various types of optical elements such as, but not limited to, filters, lenses, windows, apertures, optical fibers, and the like.

For IEP applications, the light source 150 may be connected with the interface 140 directly or via a fiber optic cable assembly 153. As so configured, the interface 140 is oriented perpendicular to the surface of the wafer 120 and is generally centered with respect to the wafer. 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 may again be received by the interface 140 after reflection from the wafer 120. In a common application, the interface 140 may be an optical collimator. After being received through the interface 140, the light may be transmitted via the fiber optic cable assembly 157 to the optical measurement system 160 for detection and conversion to digital signals. The optical measurement system 160 may comprise or may be a spectrometer. As indicated in fig. 1, a spectrometer will be used herein as an example of an optical measurement system that can be used to convert light into an electrical signal. Other examples of optical measurement systems include fixed or scanning monochromators, single or multiple diode detectors with or without wavelength filtering. The light may include both source light and detected light, and may include a wavelength range from Deep Ultraviolet (DUV) to Near Infrared (NIR), for example. The wavelength of interest may be selected from any sub-range of wavelength ranges. Additional optical interfaces (not shown in fig. 1) oriented perpendicular to wafer 120 may be used for larger substrates or where wafer non-uniformity needs to be understood. The processing tool 110 may also include additional optical interfaces positioned in different locations for other monitoring options.

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

In many semiconductor processing applications, OES and IEP optical signals are commonly collected, and such collection presents a number of problems for the use of the spectrometer 160. Typically, the OES signal is continuous in time, while the IEP signal may be continuous or discrete in time. The mixing of these signals creates a number of difficulties because process control typically requires the detection of small changes in OES and IEP signals, and the inherent changes in either signal can mask the observation of changes in the other signal. Supporting multiple spectrometers for each signal type is disadvantageous due to, for example, cost, complexity, inconvenience of signal timing synchronization, calibration, and packaging.

After the spectrometer 160 detects and converts the received optical signal to an analog electrical signal, the analog electrical signal is typically amplified and digitized within a subsystem of the spectrometer 160 and passed to a signal processor 170. The signal processor 170 may be, for example, an industrial PC, PLC or other system that employs one or more algorithms to generate an output 180, e.g., analog or digital control values representing the intensity of a particular wavelength or the ratio of two wavelength bands. Instead of a separate device, the signal processor 170 may be integrated with the spectrometer 160. The signal processor 170 may employ OES algorithms that analyze the emission intensity signal at a predetermined wavelength and determine trend parameters related to process conditions and may be used to access such conditions, such as endpoint detection, etch depth, etc. For IEP applications, the signal processor 170 may employ an algorithm that analyzes a wide bandwidth portion of the spectrum to determine film thickness. For example, see U.S. patent 7,049,156, "systems and methods for in situ monitoring and control of film thickness and trench depth (System and Method for In-situ Monitor and Control of Film Thickness and Trench Depth)", which is incorporated herein by reference. The output 180 may be transmitted to the process tool 110 via the communication link 185 for monitoring and/or modifying a production process occurring within the chamber 135 of the process tool 110.

The components illustrated and described in fig. 1 are simplified for convenience and are well known. In addition to common functions, the spectrometer 160 or signal processor 170 may be configured to identify stationary and transient changes in optical and non-optical signals, and process these signals according to the methods and/or features disclosed herein. Thus, the spectrometer 160 or signal processor 170 may contain algorithms, processing power, and/or logic to identify and process stationary and transient optical and non-optical signals. The algorithms, processing capabilities, and/or logic may be in the form of hardware, software, firmware, or any combination thereof. The algorithms, processing power and/or logic may be within one computing device or may be distributed across multiple devices such as the spectrometer 160 and the signal processor 170. Accordingly, one or more processors may be configured to perform the identifying and processing. The processing may include adjusting a digital representation of the charge from the pixels of the optical sensor pixel area using a characterization of one or more reduced illumination areas signal levels from the optical sensor. The optical sensor may be part of the spectrometer 160. The optical sensor may be a Charge Coupled Device (CCD) sensor such as that shown in fig. 2.

Fig. 2 is a schematic diagram generally depicting the functional elements of a conventional area array CCD sensor 200. The sensor 200 typically includes an active pixel area 210 that can be divided into an array of individual pixels, e.g., 1024 (H) x 122 (V), as in the S7031 CCD sensor of japan bingo (Hamamatsu of Japan). The sensor 200 may be integrated with a spectrometer (e.g., the spectrometer 160 of fig. 1) or used with another type of optical measurement system. For the sake of definition and clarity, it should be noted that "horizontal" and "vertical" as used herein in reference to an optical sensor refer to the long physical axis and the short physical axis of the optical sensor in question, respectively. In spectroscopy applications, the long/horizontal axis of the optical sensor is typically aligned with the orientation of the wavelength dispersion, while the short/vertical axis is associated with the imaging or collection of a defined light source or illumination aperture (e.g., an optical fiber or optical slit).

The sensor 200 also includes a horizontal shift register 220 proximate to the pixel region 210. The optical signals concentrated on the sensor 200, for example from the fiber optic cable assemblies 157 or 159, are typically read via vertically shifting the charge stored in each pixel of the pixel region 210 into the horizontal shift register 220 as indicated by arrow 230. All or part of the active pixel area 210 may be so shifted in a row-by-row fashion. After the vertical shift, a horizontal shift may be performed as indicated by arrow 240. When each pixel of the horizontal shift register 220 is shifted (toward the top in fig. 2), its signal content may be converted from an analog signal to a digital signal base, e.g., from an analog electrical signal to a digital electrical signal, by the converter 250. Subsequent processing and handling of the resulting digital data may occur internal or external to the spectrometer (e.g., in the signal processor 170), and may include averaging, curve fitting, threshold detection, filtering, and other mathematical manipulations such as described herein.

Sensor 200 may also include one or more reduced illumination areas of non-illuminating or partially illuminating 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 "beveled" pixels. One or more of these regions or elements may be included within sensor 200 and provide signal levels inherent to sensor 200. The signal level may be a non-optical signal level. Non-optical signals may generally include signal offsets, signal transients, and other forms of signal variation driven by temperature or other non-optical factors.

Fig. 3A and 3B provide the context of optical and non-optical signal data and the methods for processing optical and non-optical signals described herein. Each of fig. 3A and 3B illustrates a graph of the signal collected from the spectrometer. Each graph has an x-axis in physical pixels (typically in unevenly spaced wavelength units) and a y-axis in signal counts. Fig. 3A illustrates a graph 300 of a typical OES optical signal (spectrum) 320 derived from light incident on a sensor of a spectrometer. Spectrum 320 shows typical characteristics of both molecules (e.g., broadband structures near 400 nm) and atomic emissions (e.g., all narrow peaks). Fig. 3B illustrates a graph 350 of a background signal 360 derived from a non-optical signal collected by the same sensor. It can be readily observed by comparing fig. 3A and 3B that significant modifications may be provided to the spectrum 320 if the background signal 360 is not removed. For example, the average level of background signal 360 is approximately 2080 counts, while the average signal level of spectrum 320 is approximately 5000 counts. In addition, the background signal 360 includes features that occur between pixels 1 and 100 that can be interpreted as spectral features. The static value and temporal evolution of the background signal (or spectrum) 360 and the average of any of its features may lead to misunderstanding of the features or changes in the associated spectrum (e.g., optical signal 320), resulting in semiconductor process control errors. For most advanced semiconductor processes, the control detection threshold may be near or below a few percent and may be easily masked by variations in the non-optical background signal.

Fig. 4 illustrates a graph 400 indicating how a background signal derived from a non-optical signal may be affected by the operating temperature and sampling frequency of a sensor (e.g., sensor 200). Graph 400 has an x-axis in physical pixels (typically in unevenly spaced wavelength units) and a y-axis in signal counts. Signals 410 and 420 are collected by sensors operating at a sampling period of 2ms or equivalently at a sampling rate of 500 spectra per second and ambient temperatures of 0 degrees celsius and 40 degrees celsius, respectively. Signals 410 and 420 are substantially indistinguishable and are characterized by nearly identical average signal levels and nearly identical pixel number variations.

Signals 430 and 440 are collected from sensors that are identical to signals 410 and 420 but operate at a sampling period of 100ms or equivalently a sampling rate of 10 spectra per second and ambient temperatures of 0 degrees celsius and 40 degrees celsius, respectively. Signals 430 and 440 are readily distinguishable and are characterized by an average signal level variation of about 30 counts and a number of differences in pixel number variation and varying RMS noise levels. This shows that it is preferable to operate the sensor with a sampling period of 2ms, but this is not practical, as the sampling period depends on factors such as the amount of incident light. For relatively very high incident light levels, a sampling period of 2ms may be suitable, but for significantly lower incident light levels, a longer sampling period is required to obtain a suitable signal level and signal-to-noise ratio.

Fig. 5A and 5B are a set of graphs 500 and 550 that further detail the transient effect of background signals affected by sampling frequency and thermal variations. Graph 500 has an x-axis in number of samples (which may be associated with time increments per sample) and a y-axis in signal count units. Graph 500 illustrates background signal variation driven by sample rate variation between a predetermined fixed idle time sampling period of 2ms and a sampling period used during an actual process signal collection period. Signal 510 has a process sampling period of 2ms and thus no transient behavior is indicated because the idle time rate and the actual rate are equal. The signal 520 has a process sampling period of 10ms and indicates transient behavior because the idle time rate and the actual rate are different. For signal 530, the sampling period is increased to 100ms, and a greater increase in transient behavior is observed. Since it is common practice to utilize the earliest portion (sample) of the collected data further during processing as a reference value, transients such as those shown in graph 500 may be detrimental to process control. Variability of transients may adversely affect process parameters such as baseline and threshold. The change between the active state (where ongoing signal collection and processing occurs for process control) and the idle state (where signal collection and processing may be altered or suspended while waiting for collection of process control to be initiated) may be due to varying different read frequencies of current to the sensor, which may change the thermal environment and cause shift offsets in the various components.

Graph 550 illustrates the thermal drive transient and its dependence on the sampling period. Graph 550 has an x-axis in time and a y-axis in signal counts. Signals 560 and 570 are time trends of averages of blank pixels and active pixels, respectively, from an optical sensor operating at a 2ms sampling period. For this operating condition, substantially no transients are observed, but excursions are easily observed. Signals 580 and 590 are time trends of averages of blank pixels and active pixels, respectively, from an optical sensor operating at a 100ms sampling period. For this operating condition, a transient of about 10 seconds is observed, as well as a shift in signal level. Transients and offsets such as those shown in graph 550 may result from changes in power usage of the optical sensor and associated thermoelectric cooler, as well as subsequent re-heating of the ambient environment by the sensor. These types of deviations can be detrimental to process control because it is common practice to utilize the earliest part (sample) of the collected data as a reference value further down during processing. Variability of transients may adversely affect process parameters such as baseline and threshold.

FIG. 6 is a flow chart of an example of a method 600 for reading spectral data from an optical sensor and processing the spectral data by applying background correction and calibration to those signals to account for transients, offsets, and other signal variations discussed herein. For example, the spectral data is processed to accommodate the offset/shape represented in fig. 4 and the transients represented in fig. 5A and 5B. Method 600 characterizes signal levels and variations from at least some reduced illumination regions, and uses the characterization to adjust a digital representation of spectral data from active pixels of an optical sensor. The method 600 may be performed by one or more processors and may be performed in real-time during active operation of an optical sensor or an optical measurement system including the optical sensor. For example, the method 600 may be performed by the spectrometer 160, the signal processor 170, or a combination thereof. The method 600 begins with initialization steps 610, 620, and 630, which may be repeatedly performed at a 2ms rate during idle time and at other rates during active collection time, also referred to as active operation as noted above. Idle times refer to those periods during which the spectrometer is not actively collecting optical data for process control and/or process monitoring. During step 610, values for pixels from each region (reduced illumination pixels) may be collected from the sensor. As discussed above in connection with fig. 2, various sensors may include a plurality of reduced illumination pixels, such as blank pixels and/or beveled pixels. If the reduced illumination pixels are associated with shift register elements only, then there may typically be and read 4-20 reduced illumination pixels. After collection and during step 620, a median or other mathematical value (e.g., an average or other more complex mathematically derived value) may be selected from the reduced illumination pixel values. As the cycle count during idle time increases, a running average of the values selected during step 620 may be calculated during step 630.

Once the active collection state of the spectrometer is entered, a dark level estimate may be defined during step 640. A dark level estimate is determined from the reduced illumination pixels of the sensor, also referred to as dark pixels, and may also include data from the active area of the sensor. The dark level estimate may be affected by similarities or differences between the configurations of the active and idle states. For example, the sampling period, temperature, or other characteristics may differ from state to state. In the specific instance that the configuration of the idle state and the active state are the same, the dark level estimate may be equal to the running average determined during step 630. In the event of a discrepancy between the idle state and the active state, the value selected during step 620 may be equal to the dark level estimate during step 640, with it being possible to bypass the running average and select the current value from the active state operation. In fig. 6, the option of using the running average of step 630 is represented by the position of the arrow indicating the process step switch. Using the mathematical representation from step 620, the running average or even another representation or calculation from step 630 may be predetermined and implemented, or may be determined in real time by a processor. Factors that consider which option to use may be thermal characteristics, sampling rate, other characteristics of the active and idle states, or a combination thereof. In addition to the specific examples described above, other values of the dark level estimate may be generated by mathematical operations on the blank pixel values. The dark level estimate may correspond to a dark spectrum of the spectrometer, which is a spectrum obtained without light striking the optical sensor.

During the activity collection in step 650, activity pixel values may be collected. After collection, the active pixel values may be mathematically combined with the dark level estimate from step 640 to provide a dark subtracted pixel in step 660. The mathematical combination of the active pixel value and the dark level estimate may be a simple combination as in subtracting the dark level estimate from the active pixel value, or may include additional manipulations.

The active pixel values from step 650 may also be processed along with the predetermined saturation threshold determined during step 670 to provide detection of saturated pixel values within the active pixel value set (typically an array of 1024 or 2048 values) during step 675. The saturation threshold may be a pixel value defined such that an active pixel value at or above this value may be affected by saturation, non-linear performance, or other undesirable changes. Detecting saturated pixels during step 675 results in the saturation mask defined in step 678. The saturation mask may be represented by a binary array that, for each array index, indicates true or false values corresponding to saturation or unsaturation of the active pixel values.

The wideband calibration vector may be predetermined in step 680. Sensor-to-sensor (or spectrometer-to-spectrometer) consistency is important; the process monitoring should be the same. But each processing machine includes different components and thus different sensitivities. Thus, the broadband calibration vector of 680 need only be applied to the actual optical signal. Thus, prior to broadband calibration, the dark level estimate of step 640 is subtracted from the active pixel values of step 650, for example by applying a broadband calibration vector, to obtain the dark subtracted pixel values of step 660. The broadband calibration vector or its value may be combined with the dark subtracted pixel values of step 660 to provide an intensity corrected value for each pixel.

The intensity correction value may be further adjusted by application of a predetermined uniform gain value during step 682. The gain factor is typically a scalar value, such as 1.5, that is applied to all data to provide a consistent response to the same amount of incident light at the input of the optical sensor. The gain helps to compensate for inconsistent responses of the optical sensor and other spectrometer components. After gain adjustment, a predetermined designed dark level value may be added to all pixel values to provide an appropriate offset of the pixel values relative to zero values during step 685. The predetermined designed dark level value may be based on the dark level estimate of step 640. After the signal level modification has been applied to modify or correct the active pixel values, a previously determined saturation mask may be applied to the corrected active pixel values during step 687 to generate adjusted active pixel values. The application of the saturation mask suitably reconstructs the saturated signal, e.g., any saturated signal, that may have been adjusted by any previous steps of method 600. After the saturation mask is applied, the pixel data may be interpolated into a uniform wavelength spatial representation of the adjusted active pixel values during step 690. The uniform wavelength spatial representation of the adjusted active pixel values is then output for further use during step 695. For example, the adjusted active pixel values may be provided to the signal processor 170 and used to monitor or modify the production process.

Process 600 may be advantageously used for multiple sets of features such as, but not limited to, 1) providing integration of broadband radiometric correction methods independent of baseline shift, 2) providing background correction that reduces temperature sensitivity (blank pixels follow active pixels with temperature shift), and 3) providing background correction that does not add noise to background levels and avoids baseline level transients due to self-heating and cooling within the CCD. Process 600 may be particularly suitable for low dark current systems (i.e., thermoelectric (TE) cooling devices, where in general dark current is low and generally a short integration time is used, so dark current may have limited impact), where ambient temperature variations are one of the main causes of baseline shifts and transients.

Fig. 7 is a block diagram of an optical system 700 including a spectrometer 710 and certain related systems according to one embodiment of the present disclosure. Spectrometer 710 can incorporate the systems, features, and methods disclosed herein to facilitate measuring optical signals from a semiconductor process and can be associated with spectrometer 160 of fig. 1. The spectrometer 710 may receive optical signals from the external optics 730, for example, via the fiber optic cable assemblies 157 or 159, and may send data after integration and conversion to an external system 720, such as the output 180 of fig. 1, which may also be used to control the spectrometer 710 by, for example, selecting an operating mode as defined herein or controlling integration timing. Spectrometer 710 can include an optical interface 740, such as a micro-assembly (SMA) or ferrule-connector (FC) fiber optic connector or other optical-mechanical interface. Other optical components 745, such as slits, lenses, filters, and gratings, may be used to form, direct, and colorimetrically separate the received optical signals and direct them to the sensor 750 for integration and conversion. Sensor 750 may be associated with sensor 200 of fig. 2. The low-level functions of the sensor 750 may be controlled by elements such as the FPGA 760 and the processor 770. After photoelectric conversion, the analog signal may be directed to an a/D converter 780 and converted from an electrical analog signal to an electrical digital signal, which may then be stored in memory 790 for immediate or later use and transmission, e.g., to an external system 720 (see signal processor 170 of fig. 1). Although some interfaces and relationships are indicated by arrows, not all interaction and control relationships are indicated in FIG. 7. The spectral data shown in fig. 3-5B may be collected, stored, and/or operated on, e.g., in/by one or more of memory/storage 790, FPGA 760, processor 770, and/or external system 720, in accordance with process 600 of fig. 6. Spectrometer 710 also includes a power supply 795, which may be a conventional AC or DC power supply commonly included with spectrometers.

Fig. 8 illustrates a computing system 800 that may be used in the processes disclosed herein, for example, to identify signals in spectral data and process the signals. The computing device 800 may be a spectrometer or a portion of a spectrometer, such as the spectrometer 160 or 710 disclosed herein. The computing device 800 may include at least one interface 832, memory 834, and a processor 836. The interface 832 includes the necessary hardware, software, or combination thereof to receive, for example, raw spectral data and to send, for example, processed spectral data.

A portion of interface 832 may also include the necessary hardware, software, or a combination thereof for communicating analog or digital electrical signals. Interface 832 may be a conventional interface that communicates via various communication systems, connections, buses, etc., according to a protocol, such as a standard protocol or a proprietary protocol (e.g., interface 832 may support I2C, USB, RS232, SPI, or MODBUS).

The memory 834 is configured to store various software and digital data aspects related to the computing device 800. In addition, the memory 834 is configured to store a series of operational instructions corresponding to an algorithm or algorithms that, when activated, direct the operation of the processor 836 to, for example, identify an anomaly signal in the spectral data and process the identified anomaly signal. Process 600 and its variants are representative examples of algorithms. Processing may include removing or modifying signal data or different actions. For example, the processor 836 may identify and characterize a background signal or a signal derived from a non-optical signal, and apply background correction and calibration to the background signal. Memory 834 may be a non-transitory computer-readable medium (e.g., flash memory and/or other medium). The processor 836 is configured to direct the operation of the computing device 800. Thus, the processor 836 includes the necessary logic to communicate with the interface 832 and memory 834, and to perform the functions described herein to identify and process abnormal signals in the spectral data.

Portions of the above-described apparatus, systems, or methods may be embodied in, or executed by, various means, such as a conventional digital data processor or computer, where the computer is programmed or stores an executable program of software instruction sequences to perform one or more steps of the methods. Software instructions of such programs or code may represent algorithms and are encoded on non-transitory digital data storage media, such as magnetic or optical disks, random Access Memory (RAM), magnetic hard disks, flash memory, and/or read-only memory (ROM), in a machine-executable form to enable various types of digital data processors or computers to perform one, more, or all of the steps, functions, systems, or devices of one or more of the above-described methods described herein.

Portions of the disclosed embodiments may relate to computer storage products with a non-transitory computer-readable medium that have program code thereon for performing various computer-implemented operations that embody a portion of an apparatus, device, or perform steps of a method set forth herein. Non-transitory as used herein refers to all computer readable media except 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 tape; optical media such as CD-ROM discs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program code, such as ROM and RAM devices. Examples of program code include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. Configuration refers to, for example, designing, constructing, or programming with necessary logic, algorithms, processing instructions, and/or features to perform a task or tasks.

Variations and the like described above may be made in the optical measurement systems and subsystems described herein without departing from the scope of the invention. For example, while certain examples are described in connection with semiconductor wafer processing equipment, it is to be understood that the optical measurement systems described herein may be adapted for other types of processing equipment, such as roll-to-roll thin film processing, solar cell fabrication, or any application where high precision optical measurements may be desired. Furthermore, while certain embodiments discussed herein describe the use of a common light analysis device, such as an imaging spectrometer, it should be understood that multiple light analysis devices with known relative sensitivities may be utilized. Furthermore, although the term "wafer" is used herein in describing aspects of the present invention, it should be understood that other types of workpieces, such as quartz plates, phase shift masks, LED substrates, and other non-semiconductor processing related substrates, as well as workpieces, including solid, gaseous, and liquid workpieces, may be used.

The examples described herein were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various applications with various modifications as are suited to the particular use contemplated. The specific examples described herein are in no way intended to limit the scope of the features disclosed herein, as the disclosure may be practiced in various variations and environments without departing from the scope and intent thereof. Thus, the present disclosure 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.

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 disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

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 indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As will be appreciated by one of skill in the art, the present disclosure may be embodied as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that are all generally referred to herein as a "circuit" or "module. Furthermore, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium.

Various aspects of the disclosure may be claimed, including the apparatus, systems, and methods disclosed herein. Aspects disclosed herein and pointed out in the summary include:

A. an optical measurement system comprising: (1) A pixel area of an optical sensor having a plurality of pixels storing charge corresponding to a received optical signal, (2) one or more reduced illumination areas providing a signal level inherent to the optical sensor; and (3) one or more processors configured to adjust a digital representation of the charge from the pixels of the pixel region during active operation of the system using a characterization of the signal level from the one or more reduced illumination regions.

B. A method of processing optical data, comprising: (1) Defining a dark level estimate for an optical sensor receiving the optical data; (2) Collecting active pixel values from active pixels of the optical sensor; (3) Generating a dark subtracted pixel value from the active pixel value and the dark level estimate; and (4) generating corrected active pixel values by modifying the active pixel values based on the dark subtracted pixel values.

C. A computer program product having a series of operational instructions stored on a non-transitory computer-readable medium that, when activated, directs one or more processors to perform operations for adjusting a digital representation of charge of pixels from a pixel region of an optical sensor, the operations comprising: (1) Characterizing a signal level of a pixel from the optical sensor, wherein the pixel includes pixels from one or more reduced illumination areas of the optical sensor; and (2) adjusting a digital representation of the charge using the characterization.

Each of aspects A, B and C can have one or more of the following additional elements in combination: element 1: wherein the one or more reduced illumination areas include blank pixels, beveled pixels, or a combination of both. Element 2: wherein the one or more reduced illumination regions include pixels in the pixel region that are not illuminated by the received optical signal. Element 3: wherein each of the representations corresponds to one of a plurality of different types of variations in the signal level. Element 4: wherein the plurality of different types of the variations correspond to non-optical signals. Element 5: wherein the plurality of different types of the variations include at least one of signal offsets or signal transients. Element 6: wherein the plurality of different types of the variations include signal variations driven by temperature, sampling frequency, or other non-optical factors. Element 7: wherein the plurality of different types of the variations include thermal saturation. Element 8: wherein the one or more processors are additionally configured to adjust the digital representation of the charge from one or more of the pixels of the pixel region according to at least one change associated with the pixel. Element 9: wherein the optical measurement system comprises a spectrometer. Element 10: wherein at least one of the one or more processors is integrated with the spectrometer. Element 11: wherein at least one of the one or more processors is external to the spectrometer. Element 12: wherein the one or more processors are additionally configured to provide the characterization. Element 13: wherein generating corrected active pixel values includes providing an intensity correction value for the active pixel values based on the dark subtracted pixel values. Element 14: it additionally includes applying a wideband calibration vector for processing spectral data, wherein the intensity correction value is a combination of a value from the wideband calibration vector and the dark subtracted pixel value. Element 15: the method additionally includes detecting saturated pixels within the active pixels using a saturation threshold, generating a saturation mask based on the saturated pixels, and generating adjusted active pixel values by applying the saturation mask to the corrected active pixel values. Element 16: it additionally includes providing the adjusted active pixel values as a uniform wavelength spatial representation using interpolation. Element 17: wherein generating the corrected active pixel value additionally includes modifying the active pixel value by applying a uniform gain value. Element 18: wherein generating the corrected active pixel value additionally includes providing a designed dark level value to the corrected active pixel value. Element 19: wherein the characterization includes defining a dark level estimate for the optical sensor. Element 20: wherein the dark level estimate is based on a running average of the signal levels from the pixels of the one or more reduced illumination areas. Element 21: wherein the dark level estimate is based on a mathematical representation of the signal level from the pixels of the one or more reduced illumination areas. Element 22: wherein the characterizing includes using a designed dark level value.

Claims

1. An optical measurement system, comprising:

a pixel region of the optical sensor having a plurality of pixels storing charges corresponding to the received optical signals;

one or more reduced illumination areas that provide a signal level inherent to the optical sensor; and

one or more processors configured to adjust a digital representation of the charge from the pixels of the pixel region during active operation of the system using a characterization of the signal level from the one or more reduced illumination regions.

2. The optical measurement system of claim 1, wherein the one or more reduced illumination areas include blank pixels, beveled pixels, or a combination of both.

3. The optical measurement system of claim 1, wherein the one or more reduced illumination regions include pixels in the pixel region that are not illuminated by the received optical signal.

4. The optical measurement system of claim 1, wherein each of the representations corresponds to one of a plurality of different types of changes in the signal level.

5. The optical measurement system of claim 4, wherein the plurality of different types of the changes correspond to non-optical signals.

6. The optical measurement system of claim 4, wherein the plurality of different types of the variations include at least one of signal offsets or signal transients.

7. The optical measurement system of claim 4, wherein the plurality of different types of the variations include signal variations driven by temperature, sampling frequency, or other non-optical factors.

8. The optical measurement system of claim 4, wherein the plurality of different types of the variations include thermal saturation.

9. The optical measurement system of claim 1, wherein the one or more processors are additionally configured to adjust the digital representation of the charge from the pixels of the pixel region according to at least one change associated with one or more of the pixels.

10. The optical measurement system of claim 1, wherein the optical measurement system comprises a spectrometer.

11. The optical measurement system of claim 10, wherein at least one of the one or more processors is integrated with the spectrometer.

12. The optical measurement system of claim 10, wherein at least one of the one or more processors is external to the spectrometer.

13. The optical measurement system of claim 1, wherein the one or more processors are additionally configured to provide the characterization.

14. A method of processing optical data, comprising:

defining a dark level estimate for an optical sensor receiving the optical data;

collecting active pixel values from active pixels of the optical sensor;

generating a dark subtracted pixel value from the active pixel value and the dark level estimate; and

a corrected active pixel value is generated by modifying the active pixel value based on the dark subtracted pixel value.

15. The method of claim 14, wherein generating the corrected active pixel value includes providing an intensity correction value for the active pixel value based on the dark subtracted pixel value.

16. The method of claim 15, further comprising applying a wideband calibration vector for processing spectral data, wherein the intensity correction value is a combination of a value from the wideband calibration vector and the dark subtraction pixel value.

17. The method of claim 14, further comprising detecting saturated pixels within the active pixels using a saturation threshold, generating a saturation mask based on the saturated pixels, and generating adjusted active pixel values by applying the saturation mask to the corrected active pixel values.

18. The method of claim 17, further comprising providing the adjusted active pixel values as a uniform wavelength spatial representation using interpolation.

19. The method of claim 14, wherein generating the corrected active pixel value further includes modifying the active pixel value by applying a uniform gain value.

20. The method of claim 14, wherein generating the corrected active pixel value further includes providing a designed dark level value to the corrected active pixel value.

21. A computer program product having a series of operational instructions stored on a non-transitory computer-readable medium that, when activated, directs one or more processors to perform operations for adjusting a digital representation of charge of pixels from a pixel region of an optical sensor, the operations comprising:

characterizing a signal level of a pixel from the optical sensor, wherein the pixel includes pixels from one or more reduced illumination areas of the optical sensor; and

the digital representation of the charge is adjusted using the characterization.

22. The computer program product of claim 21, wherein the characterization includes defining a dark level estimate of the optical sensor.

23. The computer program product of claim 22, wherein the dark level estimate is based on a running average of the signal levels from pixels of the one or more reduced illumination regions.

24. The computer program product of claim 22, wherein the dark level estimate is based on a mathematical representation of the signal level from pixels of the one or more reduced illumination regions.

25. The computer program product of claim 22, wherein the characterizing includes using designed dark level values.