Abnormal pixel detection

Abstract

A method for automatic quality control of at least one photodetector (114), a photodetector (114) for measuring optical radiation (118) and a spectrometer (110) for spectroscopic analysis of optical radiation (118) provided by at least one object (112) are disclosed. The photodetector (114) comprises a plurality of pixels i (126), wherein i is a pixel position (136) and igt; 2, where each pixel (126) comprises at least one light sensitive area, where each of the pixels (126) is configured to generate a signal in response to its respective light sensitive area being illuminated by light radiation (118), the method comprises classifying anomalous pixels by: a) measuring a plurality of signals Si of a pixel (126) by measuring at least one object (112) using a photodetector (114); b) determining, for each pixel i (126), at least one quantization factor Ci by comparing the measurement signal Si with at least one reference spectrum SF, i by using at least one processor (130) to quantify a deviation between the respective measurement signal Si and the reference spectrum SF, i, and comparing the respective quantization factor Ci with at least one threshold Cmax, if the corresponding quantization factor Ci exceeds a limit Cmax, the pixel (126) is classified as an abnormal pixel. A computer program and a computer-readable storage medium for performing the method are further disclosed.

Description

Technical Field

[0001] The present invention relates to a method for automatically performing quality control on at least one photodetector, a photodetector, and a spectrometer. Such methods and devices can generally be used for purposes of research or monitoring, particularly in the infrared (IR) spectral region, especially in the near-infrared (NIR) spectral region, as well as for the detection of heat, flames, fires, or smoke. However, other types of applications are also possible. Background Art

[0002] Adjacent pixels of an NIR spectrometer can be noticeable. For example, adjacent pixels may provide unreasonable signals, such as due to unwanted electrical or optical connections or instability in the electrical or optical characteristics of individual pixels over time or environmental conditions (such as temperature). This can affect detectors having more than one pixel, which reproducibly show indistinguishable spectral trends or strong deviations from the sample response. These pixels can be identified by measuring a spectrum with sufficient dynamics within the affected pixel range.

[0003] If a pixel does not follow the sample response, while the sampling rate of the spectral data does not reach the resolution of the spectrometer's dispersion element and there is no reproducibility due to high noise (low signal-to-noise ratio), these pixels represent abnormal pixels. Abnormal pixels do not contain the spectral information required for chemometric modeling. Therefore, a sensor array module with identified abnormal pixels may not be suitable for the spectrometer and needs to be marked as bad or replaced.

[0004] Typically, abnormal pixels are searched for manually in the spectrogram, which is very time-consuming.

[0005] US2012 / 323533 A1 describes a cosmic spike filter for removing noise spikes in spectral data. Spikes are eliminated by locating, smoothing, and filtering the spikes. A cosmic spike filter is also provided that combines a data collection method and a statistical method to remove cosmic spike noise from the collected signal without distorting the true signal. Further, a statistical method for identifying and removing negative peaks from a spectrum is provided, where the negative peaks are caused by bad pixels in a charge-coupled device.

[0006] US2014 / 268136 A1 describes a method and spectrometer system for correcting light source quality, exposure time, y-direction distortion, x-direction distortion, temperature correlation, pixel alignment variability, dark pixels, bad pixels, pixel read noise, and pixel dark current noise.

[0007] Problem to be Solved

[0008] Accordingly, it is desirable to provide methods and devices that at least partially address the above technical challenges. Specifically, a method and device will be proposed for automatically performing quality control on the pixels of a spectrometer, which can save time and ensure high reliability. Summary of the Invention

[0009] This problem is solved by a method, a photodetector, a spectrometer, a computer program, and a computer-readable storage medium for automatically performing quality control on at least one photodetector having the features of the independent claims. Advantageous embodiments that can be implemented independently or in any arbitrary combination are listed in the dependent claims and throughout the specification.

[0010] In a first aspect of the present invention, a method for automatically performing quality control on at least one photodetector including a plurality of pixels i is disclosed, where i is the pixel position and i > 2. Each pixel includes at least one photosensitive region. Each of the pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation.

[0011] As used herein, the term "photodetector" is a broad term and will be given its ordinary and conventional meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, an optical detector or optical sensor configured to detect light radiation, such as for detecting illumination and / or light spots generated by at least one light beam.

[0012] As used herein, the term "photosensitive region" is a broad term and will be given its ordinary and conventional meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, a unit of a photodetector configured to be illuminated, or in other words to receive light radiation, and to generate at least one signal (such as an electrical signal) in response to the illumination. The photosensitive region can be located on the surface of the photodetector. The photosensitive region can specifically be a single, closed, uniform photosensitive region. However, other options may also be feasible. The photosensitive region can also be referred to as a pixel. The photodetector can include a plurality of pixels, which can be arranged in at least one of an array or a matrix. The pixels can be arranged in a 2D distribution. The pixels can be arranged in a pattern. The pattern can be periodic or aperiodic. The pattern can be a rectangle, a hexagon, or a pattern of other shapes. The pixel can include at least one substrate. A single pixel can be a substrate having at least one single photosensitive region that generates a physical response to illumination in a given wavelength range. However, other options may also be feasible.

[0013] The illumination can be provided by at least one measurement object. The provision can include at least one of reflection, transmission, and emission. Specifically, before interacting with the measurement object, the illumination can be emitted, for example, by at least one radiation source, in particular a spectrometer including a photodetector or another radiation source. As used herein, the term "radiation source" is a broad term and will be given its ordinary and customary meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a device configured to emit light radiation. The radiation source can be configured to emit light radiation towards the measurement object, such as light radiation in the form of a light beam. The radiation source can be configured to emit light radiation isotopically (e.g., uniformly in all spatial directions), where only a portion of the emitted light radiation can illuminate the measurement object. The radiation source can include at least one of a semiconductor-based radiation source or a thermal radiator. At least one semiconductor-based radiation source can be selected from at least one of a light-emitting diode (LED) or a laser (specifically a laser diode). The LED can include at least one fluorescent and / or phosphorescent material. The thermal radiator can include at least one of an incandescent lamp, a blackbody emitter, and a microelectromechanical system (MEMS) emitter. The radiation source can be a modulated radiation source. Other types of radiation sources may also be feasible.

[0014] As used herein, the term "light" is a broad term and will be given its ordinary and customary meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a portion of electromagnetic radiation, which is commonly referred to as the "optical spectral range" and includes one or more of the visible spectral range, the ultraviolet spectral range, and the infrared spectral range. The term "ultraviolet spectrum" or "UV" generally refers to electromagnetic radiation with a wavelength of 1 nm to 380 nm, preferably 100 nm to 380 nm. The term "visible" generally refers to wavelengths of 380 nm to 760 nm. The term "infrared" or "IR" generally refers to wavelengths of 760 nm to 1000 μm, where wavelengths of 760 nm to 3 μm are commonly referred to as "near infrared" or "NIR", while wavelengths of 3 μm to 15 μm are commonly referred to as "mid infrared" or "MidIR", and wavelengths of 15 μm to 1000 μm are referred to as "far infrared" or "FIR".

[0015] Preferably, the illumination for the typical purposes of the present invention is IR radiation, more preferably NIR radiation, in particular radiation having a wavelength in the range of 760 nm to 3 μm, preferably in the range of 1 μm to 3 μm. The illumination can specifically be light radiation irradiating a photodetector or more specifically a photosensitive area. The term "illumination" can also be referred to herein as "light radiation" or "light". The photodetector can be configured to detect light radiation within a wavelength range of 300 nm to 3000 nm, specifically 500 nm to 2500 nm, more specifically 1400 nm to 2000 nm. The pixels of the photodetector can respond to the incident illumination and can be configured to generate an electrical signal indicative of the illumination intensity. The photodetector can be sensitive within one or more of the visible light spectral range, the ultraviolet spectral range, or the infrared spectral range (specifically the near-infrared spectral range (NIR)). Each photodetector can be sensitive to electromagnetic radiation within a wavelength range of 600 nm to 1000 μm, specifically within a wavelength range of 760 nm to 15 μm, more specifically within a wavelength range of 1 μm to 5 μm, more specifically within a wavelength range of 1 μm to 3 μm.

[0016] The spectrum can be a partition of the optical spectral range, in particular the IR spectral range (in particular at least one of the NIR spectral range or the MidIR spectral range), studied by a spectrometer device. Each part of the spectrum can be constituted by an optical signal defined by a signal wavelength and a corresponding signal intensity. As used herein, the term "constituent wavelength component" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, an optical signal forming part of a spectrum. Specifically, the optical signal can include a signal intensity corresponding to a corresponding wavelength or wavelength interval. The pixels of the photodetector can be configured to receive at least a part of one of these constituent wavelength components and to generate a corresponding signal based on the illumination of the corresponding pixel by at least a part of the corresponding constituent wavelength component.

[0017] The illumination can be modulated, for example, by using a modulated radiation source. The radiation source can be a modulated radiation source. As used herein, the term "modulated" (including any of its grammatical variants) is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, the process of changing, specifically periodically changing, at least one characteristic of the light radiation, specifically one or both of the intensity or phase of the light radiation. As will be known to the person skilled in the art, the intensity is also related to the amplitude of the light radiation. The modulation can be full modulation from a maximum value to zero, or can be partial modulation from a maximum value to an intermediate value greater than zero. The modulation can include the use of a modulation element. The modulation element can be configured to, for example, mechanically modulate the light radiation (e.g., by using a rotating chopper wheel) and / or to electronically modulate the light radiation (e.g., by using the electro-optical effect and / or the acousto-optical effect, e.g., by using a Pockels cell and / or a Kerr cell). Other options are also feasible.

[0018] The photosensitive region can include at least one photoconductive material. The photoconductive material can be selected from at least one of PbS, PbSe, Ge, InGaAs, InSb, or HgCdTe. Other options (such as a photodiode or a thermopile) can also be feasible. The photodetector can be configured to generate at least one signal (such as a photocurrent), specifically a measurement signal in response to the illumination of the photosensitive region. The photodetector can specifically be or can include an optical semiconductor sensor. As an example, specifically, in the case where the photodetector is sensitive in the infrared spectral range (such as in the near-infrared spectral range), the optical semiconductor sensor can be or can include at least one semiconductor sensor whose at least one material is selected from the group consisting of: Si, PbS, PbSe, Ge, InGaAs, extended InGaAs, InSb, or HgCdTe. For example, the photodetector can be or can include at least one line sensor that includes a one-dimensional pixel array, such as a CCD line sensor, a CMOS line sensor, etc. For example, the photodetector can be or can include a two-dimensional pixel array, such as a CCD sensor, a CMOS sensor, etc.

[0019] The photodetector may specifically include at least one detector array, and the at least one detector array includes a plurality of pixelated sensors, wherein each pixelated sensor is configured to detect at least a portion of at least one component wavelength component. The photodetector may include a plurality of pixels arranged in a linear array. The linear array of photosensitive elements may include from 10 to 1000 pixels, specifically from 100 to 500 pixels, specifically from 200 to 300 pixels, and more specifically 256 pixels. However, other numbers of pixels may be feasible. As used herein, the term "pixel position" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term may specifically refer to, but is not limited to, the position of the corresponding pixel in the array.

[0020] The photodetector may further include at least one readout electronics unit. As used herein, the term "readout" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term may specifically refer to, but is not limited to, the action or process of quantifying and / or processing at least one physical property and / or the change of at least one physical property detected by at least one device (specifically by the at least one photodetector, or more specifically the photosensitive region). The readout may include the individual readout of a device (such as a photosensitive region). Additionally or alternatively, the readout may include the readout of a group of devices (such as a group of photosensitive regions).

[0021] As used herein, the term "readout electronics unit" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term may specifically refer to, but is not limited to, an electronics unit configured to quantify and / or process at least one physical property and / or the change of at least one physical property detected by the photodetector or more specifically the photosensitive region. The readout electronics unit may include at least one of the following: operational amplifier; analog-to-digital converter; voltage divider; shunt; ASIC, specifically for subtracting a constant current to generate a signal current.

[0022] A photodetector can be an element of a spectrometer for spectral analysis of optical radiation provided by at least one measurement object. As used herein, the term "spectrometer" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a device capable of optically analyzing at least one sample to generate at least one piece of information about at least one spectral characteristic of the sample. Specifically, the term can refer to a device capable of recording the signal intensity corresponding to the wavelengths of a spectrum or its partitions (such as wavelength intervals), where the signal intensity can preferably be provided in the form of an electrical signal, which can be used for further evaluation. Optical elements specifically including at least one wavelength selection element (such as an optical filter and / or a dispersive element) can be used to separate the incident light into a spectrum having component wavelength components, and the respective intensities of these component wavelength components are determined by using a detector device. In addition, additional optical elements that can be designed to receive the incident light and transmit the incident light to the optical elements can be used. Generally, a spectrometer can operate in a reflection mode and / or can operate in a transmission mode. For possible embodiments of the spectrometer, reference is made to the description of the spectrometer further outlined in detail below.

[0023] As used herein, the term "signal" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a signal generated by a photodetector, specifically referring to at least one output signal of a pixel. The at least one output signal can be selected from at least one of an electronic signal and an optical signal. The at least one output signal can be an analog signal and / or a digital signal. The output signals of adjacent pixels can be generated simultaneously or in a temporally continuous manner. For example, during a line scan or a row scan, it may be possible to generate a sequence of output signals corresponding to a series of photosensitive elements that can be arranged in rows. Additionally, each pixel can preferably be an active pixel sensor, which can be adapted to amplify the output signal before providing the output signal as a detector signal to an external processor. For this purpose, a pixel can include one or more signal processing devices, such as one or more filters and / or analog-to-digital converters, for processing and / or preprocessing the electronic signal.

[0024] As used herein, the term "quality" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a measurement of the reasonableness of a signal generated by a pixel of a photodetector. Adjacent pixels of a photodetector may provide unreasonable signals, for example, due to unwanted electrical or optical connections, which may result in indistinguishable spectral trends or deviations from the sample response that occur in a reproducible manner. For example, two or more pixels may be short-circuited. The pixels showing unreasonable signals are referred to as abnormal pixels. As used herein, the term "abnormal pixel" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, pixels showing unreasonable signal behavior. For example, an abnormal pixel may have a systematic deviation from the signal behavior of other pixels of the photodetector or the signal behavior of one or more other regions of the photodetector in terms of signal behavior, for example, a deviation from the signal behavior of other pixels of an array or matrix of pixels or the signal behavior of one or more other regions of the array or matrix.

[0025] As used herein, the term "quality control" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, at least one process for determining whether there are abnormal pixels. Quality control can include classifying the pixels of a photodetector into reliable pixels and abnormal pixels. The result of quality control can be stored in at least one log file. Quality control can include masking all pixels classified as abnormal pixels. Quality control can include issuing at least one indication of the presence of abnormal pixels, for example, by using at least one user interface. As used herein, the term "user interface" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can refer to, but is not limited to, a feature of a spectrometer configured to interact with its environment (such as for the purpose of one-way or two-way exchange of information, such as for the exchange of one or more of data or commands). For example, a user interface can be configured to share information with a user and receive information provided by the user. A user interface can be a feature that visually interacts with the user (such as a display), or a feature that auditorily interacts with the user. As an example, a user interface can include one or more of the following: a graphical user interface; a data interface, such as a wireless and / or wired data interface. Quality control can include replacing pixels classified as abnormal pixels.

[0026] As used herein, the term "automatically" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, a process that is performed entirely by at least one computer and / or computer network and / or machine, particularly without manual actions and / or user interactions. The method can be implemented at least in part by a computer. As used herein, the term "computer-implemented method" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a method involving at least one computer and / or at least one computer network. The computer and / or computer network can include at least one processor that can be configured to execute at least one method step of the method according to the present invention. Specifically, each of these method steps is executed by the computer and / or computer network. The method can be performed completely automatically, specifically without user interaction.

[0027] The method includes classifying abnormal pixels by the following steps:

[0028] a) Measuring a plurality of signals S of these pixels by measuring at least one object using the photodetector i ;

[0029] b) Using at least one processor, comparing these measured signals S i with at least one reference spectrum S F，i to determine, for each pixel i, at least one quantization factor C i , to quantify the deviation between the corresponding measured signal S i and the reference spectrum S F，i , and comparing the corresponding quantization factor C i with at least one threshold C max , wherein, in the case where the corresponding quantization factor C i exceeds the limit C max , the pixel is classified as an abnormal pixel.

[0030] The method steps can be performed in the indicated order. However, it should be noted that different orders are also possible. The method can include additional method steps not listed. Further, one or more of the method steps can be performed once or repeated. Further, two or more of the method steps can be performed simultaneously or in a time-overlapping manner. The method can include repeating steps a) and b) at a predefined time or continuously.

[0031] The signal S iIt can be a signal generated by pixel i in response to light. The method can include measuring a plurality of signals S for each of the pixels i i , for example, by repeatedly measuring the object with a photodetector. For example, the method can include performing two, three, four, five, up to ten or even more measurements for each pixel i.

[0032] The method can include determining a plurality of measurement spectra by using the signals of the pixels and a function of pixel i. For example, in the case of modulating a radiation source, the measurement spectra can be determined by recording a plurality of imaging frames (e.g., 1000 imaging frames). A plurality of signals of the photodetector (depending on the modulation frequency) can be measured with and without light. These signals can be evaluated, for example, by using one or more of at least one FFT or DFT. The evaluation can further include using a standard white measurement to thereby determine the measurement spectra. For example, the measurement spectra can be determined by determining the average value of the pixel signals varying over time. The method can include determining a plurality of spectra, for example, during step a) and / or by repeating step a). These measurement spectra can be used to classify whether the deviation (outlier) of the pixels with suspicious results is caused by the SNR or is systematically present and thus must be classified as abnormal pixels.

[0033] As used herein, the term "spectrum" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, a partition of optical radiation, where the spectrum consists of an optical signal defined by a signal wavelength and a corresponding signal intensity. In particular, the spectrum can include spectral information related to at least one object, such as the type and composition of at least one material forming the object, and this spectral information can be determined by recording at least one spectrum related to the object. The spectrum can be presented in a graph, in which, for example, the spectral quantity is plotted as a function of the pixel position.

[0034] As used herein, the term "object" (also referred to as a measurement object) is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, any entity selected from living and non-living bodies. The measurement object can specifically include at least one material undergoing study. The object generally can refer to the object to be measured (e.g., the object whose spectrum is to be recorded), where the object can in principle have any characteristics (e.g., any optical characteristics or any shape). The object can include at least one solid sample. However, other measurement objects such as fluids may also be feasible.

[0035] Any rising or falling part of a spectrum having a slope high enough can be used to classify anomalous pixels, where only the pixels located on the slope are used. The term "high enough" can refer to a slope that results in a signal difference between adjacent pixels that is higher than the signal fluctuations caused by noise at these pixels. The noise may originate from fluctuations in the light source, detector, and other components. The signal difference caused by the slope between adjacent pixels can be at least 2 times the noise limit, preferably 4 times or even 6 times higher than the noise limit. Otherwise, the noise may cause false detection of anomalous pixels due to statistical fluctuations.

[0036] The object can include at least one material having multiple dynamic spectral regions. As used herein, the term "dynamic spectral region" is a broad term and will be given its ordinary and customary meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a spectrum having high contrast, for example, ideally covering the entire dynamic range of an optical system. For example, relative to the maximum possible signal level of the optical system, the spectrum can cover a range from 50% to 90%, preferably from 10% to 90%, more preferably from 1% to 90%. The contrast variation can be on a length scale (pixel to pixel) similar to the optical resolution of the optical system.

[0037] The dynamic spectral region can generate at least one fringe pattern. The fringe pattern can allow for several parts having a slope high enough, which can be used to classify anomalous pixels. As used herein, the term "fringe pattern" is a broad term and will be given its ordinary and customary meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, the characteristic that the signal intensity of the object spectrum varies continuously with the signal wavelength. The fringe pattern in which the signal intensity varies continuously with the signal wavelength does not exclude the possibility of wavelength points (such as the maximum or minimum of the signal intensity in the object spectrum) where the signal intensity does not vary locally with the signal wavelength in the object spectrum. The fringe pattern can specifically include a periodic variation in the signal intensity of the object spectrum. As an example, the fringe pattern can include a variation in which the object spectrum follows a sine or cosine behavior. The fringe pattern can be a regular fringe pattern, such as a pattern having a constant periodicity, or an irregular fringe pattern having a varying periodicity. To identify anomalous pixels, it may be advantageous to measure a material (such as a material showing a fringe pattern) having as many dynamic (such as falling or rising) spectral regions as possible. In particular, the method can include determining whether two pixels generate the same signal, although different slopes are expected in the spectrum. This may be feasible in the case of measuring an object having many dynamics.

[0038] As used herein, the term "reference spectrum" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. The term can specifically refer to, but is not limited to, a spectrum used as a reference. The reference spectrum can be a spectrum obtained from a measured spectrum by using a processor. The reference spectrum can be a signal recorded with a reference device (e.g., "gold standard"). The reference spectrum can be taken from the literature or a metrology institution or others.

[0039] For example, a reference spectrum S can be determined from signal S i by applying at least one smoothing filter to signal S i . The smoothing filter can be at least one filter selected from the group consisting of: Savitzky–Golay filter, nth order polynomial where n > 4, moving average filter, local regression smoothing, low-pass filtering, or other filters in pixel space or Fourier space. For example, in step a), a smoothing filter is applied to signal S F,i . For example, a Savitzky–Golay filter can be used, which performs a least squares fit of the signal at position i and adjacent signals with an nth order polynomial. i For example, a Savitzky–Golay filter can be used, which performs a least squares fit of the signal at position i and adjacent signals with an nth order polynomial.

[0040] As used herein, the term "processor" is a broad term and will be given its ordinary and customary meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, any logic circuit configured to perform the basic operations of a computer or system, and / or generally refers to a device configured to perform computational or logical operations. In particular, a processor can be configured to process the basic instructions that drive a computer or system. A processor can be or can include at least one of an integrated circuit (specifically, an application-specific integrated circuit (ASIC)) or a data processing device, specifically at least one of a digital signal processor (DSP), a field-programmable gate array (FPGA), a microcontroller, a microcomputer, a computer, or an electronic communication unit (specifically, a smartphone or a tablet). By way of example, a processor can include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or a digital coprocessor, multiple registers, specifically registers configured to provide operands to the ALU and store the operation results, and a memory such as L1 and L2 cache memories. In particular, a processor can be a multi-core processor. Specifically, a processor can be or can include a central processing unit (CPU). Additionally or alternatively, a processor can be or can include a microprocessor, and thus specifically, the elements of a processor can be contained in a single integrated circuit (IC) chip. Additionally or alternatively, a processor can be or can include one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) and / or one or more tensor processing units (TPUs) and / or one or more chips, such as a dedicated machine learning optimization chip, etc. A processor can specifically be configured to perform one or more evaluation operations, for example, by software programming. Other components can be feasible, specifically at least one preprocessing device or data acquisition device. A processor can preferably be configured to execute at least one computer program, specifically at least one computer program that executes or supports the steps of the method according to the present invention.

[0041] The processor may include at least one communication interface, in particular at least one of a wireless interface or a wired interface. Further, the processor may be designed to fully or partially control or drive additional devices, such as at least one photodetector. Information determined by the processor may be provided to at least one of an additional device or a user, in particular preferably in at least one of an electronic, visual, auditory, or tactile manner. Further, the information may be stored in at least one data storage unit, specifically in an internal data storage unit included by the photodetector or at least a spectrometer, in particular by at least one processor, or in a separate storage unit to which the information may be transmitted via at least one communication interface. The at least one electronic communication unit may include the separate storage unit. The storage unit may be specifically configured to store at least one spreadsheet, such as at least one lookup table. As used herein, the term "data storage unit" is a broad term and will be given its ordinary and conventional meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term may refer to, but is not limited to, any memory device configured to store data. Specifically, the data storage unit may be an electronic memory device, a magnetic memory device, and / or a mechanical memory device. The data storage unit may be further configured to store data, specifically in an organized manner, such as stored in a database, more specifically stored in at least one database record.

[0042] The communication interface may be configured to perform at least one of the following: transmit data from the processor, transmit data to the processor, or transmit data within the processor. As used herein, the term "communication interface" is a broad term and will be given its ordinary and conventional meaning to those of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term may refer to, but is not limited to, an article or element that forms a boundary configured to convey information. In particular, the communication interface may be configured to convey information from a computing device (e.g., a computer), such as for sending or outputting information to, for example, another device. Additionally or alternatively, the communication interface may be configured to convey information to a computing device, such as to a computer, such as for receiving information. The communication interface may specifically provide a means for conveying or exchanging information. In particular, the communication interface may provide a data transfer connection, such as Bluetooth, NFC, inductive coupling, etc. As an example, the communication interface may be or may include at least one port, where the at least one port includes one or more of a network or internet port, a USB port, and a disk drive. The communication interface may include at least one web interface.

[0043] The processor can be at least partially cloud-based. As used herein, the term "cloud-based" is a broad term and will be given its ordinary and customary meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, outsourcing the processor or a part of the processor to external devices that are at least partially interconnected, specifically computers or computer networks with greater computing power and / or data storage capacity. The external devices can be arbitrarily distributed in space. The external devices can change over time, specifically according to demand. The external devices can be interconnected by using the Internet. Each of the external devices can include at least one communication interface.

[0044] As used herein, the term "quantification factor" is a broad term and will be given its ordinary and customary meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, a measure for quantifying the deviation between a measured spectrum and a reference spectrum. The quantification factor can be determined by the following formula:

[0045]

[0046] For example, the quantification factor can be determined by the following formula:

[0047]

[0048] where n is the power, σ F,i is the standard deviation of the reference spectrum, and σ i is the standard deviation of the measured signal of pixel i. Alternatively or additionally, the quantification factor can be determined by using a weighting factor for one or more of σ F,i and σ i according to the above equation. The standard deviation of the signal of a pixel can be determined by using multiple signals determined for the pixel. For example, when n = 2, the quantification factor is determined by the following formula:

[0049]

[0050] The quantification factor C i of a pixel can be compared with the limit C max . Once the following formula is satisfied, the pixel may not meet this standard:

[0051] C i ≥C max .

[0052] In the case of C i ≥C max , pixel i can be classified as an abnormal pixel. Therefore, by comparing the detected spectrum S i with the reference spectrum determined according to S iWhen comparing with the generated smooth spectrum, the proposed method allows to provide a significance rating for the identification of abnormal pixels. This technique can be used to distinguish abnormal pixels from noise.

[0053] Abnormal pixels will not be confused with noise (noise pixels). Noise pixels are pixels with too little or too much generated signal, rather than caused by electrical errors, optical errors, or system or random fluctuations. To ensure highly reliable detection of abnormal pixels, the method may include performing a two-step check. In addition to step b), the method may include analyzing the signal behavior around pixel position i. The method may include searching for a predefined number of abnormal pixels in a row, especially adjacent abnormal pixels. Analyzing the signal behavior around pixel position i may include testing one or more criteria C j . If each individual criterion C for abnormal pixels is satisfied j , then a group of pixels at positions i + 1, … i + N (also referred to as a pixel cluster) can be considered abnormal pixels, where N is a predefined number, and these individual criteria are combined into one criterion C:

[0054] C = C1 ∧ C2 ∧ … C N .

[0055] For example, for N = 2, the method may include searching for two short-circuit pixels. For example, for N = 3, the method may include searching for three short-circuit pixels. In particular, when pixel i + 1 is connected to pixel i + 3, the method includes such a search. The method may include using multiple criteria. In some embodiments, a pixel can be considered an abnormal pixel when all criteria are satisfied. In some embodiments, a pixel can be considered an abnormal pixel when a subset of the criteria is satisfied. Method steps a) and b) can be performed before analyzing the signal behavior around pixel position i. Alternatively, as will be outlined in more detail below, analyzing the signal behavior around pixel position i can be performed without performing steps a) and b).

[0056] For example, the method may include a linear criterion. Analyzing the signal behavior around pixel position i may include comparing the local derivative of signal S i+1 – S i with the derivatives at adjacent pixels. The change in the derivative in the signal is limited by the optical resolution of the spectrometer system. By comparing the local derivatives in the spectrum between adjacent pixels, additional outlier criteria are defined. When there are large fluctuations in the derivative around pixels i + 1... i + N, then this group of pixels may be abnormal pixels. For example, if the following equation is satisfied, pixels i + 1, … i + N are classified as abnormal pixels:

[0057]

[0058] This can be rewritten as:

[0059]

[0060] where t d is at least one predefined threshold. As used herein, the term "predefined threshold" is a broad term and will be given its ordinary and customary meaning to one of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, a numerical value used as a reference value for pixel classification. Specifically, classification can refer to abnormal characteristics of a pixel. If the above-identified criterion is higher than the numerical value of the predefined threshold, the corresponding pixel can be classified as an abnormal pixel. However, if the above-identified criterion is equal to or lower than the predefined threshold, the corresponding pixel may not be classified as an abnormal pixel. The predefined threshold can be a fixed numerical value. The predefined threshold can be defined before performing the method, specifically before step b). The predefined threshold t d can be selected to specify the intensity at which abnormal pixels should be identified. In particular, the predefined threshold can be selected to allow distinguishing noise from abnormal pixels, or to determine that an abnormal pixel can be ignored because it does not have a significant impact on the measurement result. For example, t d can be in the range of 1.5 to 1.7.

[0061] For example, the method can include a peak skipping criterion. Analyzing the signal behavior around pixel position i can include comparing the sign of the local derivative at pixel i with the signs of the derivatives of adjacent pixels. If the following equation is satisfied, pixels i + 1,... i + N can be classified as abnormal pixels:

[0062] f(S i - S i+1 ) * g(S i+N - S i+1+N ) > 0,

[0063] where f and g are arbitrary functions, such as linear functions, polynomial functions, and / or power-law functions. As an example, if the following equation is satisfied, pixels i + 1,... i + N can be classified as abnormal pixels:

[0064] (S i - S i+1 ) * (S i+N - S i+1+N ) > 0.

[0065] This can be rewritten as:

[0066] (S i+1 - S i ) * (S i+1+N - S i+N ) > 0,

[0067] Thus, the peak skipping criterion can include comparing the sign of the local derivative at pixel i with the derivatives of adjacent pixels, essentially searching for the zero crossing of the derivative, i.e., the saddle point of the signal.

[0068] For example, the method can include a slope ratio criterion. Analyzing the signal behavior around pixel position i involves comparing the fluctuations of the derivatives around pixel i. If the following equation is satisfied, pixels i+1, … i+N can be classified as abnormal pixels:

[0069] |f(S i -S i+1 )-g(S i+N -S i+1+N )| < a*|h(S i -S i+1 )+i(S i+N -S i+1+N )|,

[0070] where f, g, h, and i are arbitrary functions, such as linear functions, polynomial functions, and / or power-law functions and For example, if the following equation is satisfied, pixels i+1, … i+N can be classified as abnormal pixels:

[0071]

[0072] This can be rewritten as:

[0073]

[0074] Thus, the slope ratio criterion can include comparing the fluctuations of the derivatives (which are similar to second-order derivatives). The slope ratio criterion can include checking whether the clusters of abnormal pixels on both sides change to the slope symmetrically enough.

[0075] If the significance rating of step b) is performed and, optionally, one or more of the mentioned criteria are checked, all abnormal pixels can be reliably identified and automatically masked.

[0076] Method steps a) and b) can be performed by using a stripe pattern, especially the first stripe pattern. Steps a) and b) and, optionally, additional checks of one or more of the mentioned criteria can be repeated with a second stripe pattern that has a phase shift with respect to the first measurement spectrum. This can allow the peak region and valley region of the first pattern to also be covered by the dynamic region of the second pattern.

[0077] The method includes at least one measurement step, especially after steps a) and b). The measurement step includes determining at least one spectrum using a photodetector. The masked pixels can be ignored or corrected mathematically using adjacent pixels.

[0078] In another aspect of the present invention, a method for automatically performing quality control on at least one photodetector is disclosed.

[0079] The photodetector includes a plurality of pixels i, where i is the pixel position and i > 2, and each pixel includes at least one photosensitive region. Each of the pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation.

[0080] The method includes classifying abnormal pixels by analyzing the signal behavior around the pixel position i, where the analysis includes testing one or more of the following criteria C j among others,

[0081] i) Linear criterion, where the local derivative of the measured signal is compared with the derivatives of adjacent pixels;

[0082] ii) Peak skip criterion, where the sign of the local derivative at pixel i is compared with the signs of the derivatives of these adjacent pixels;

[0083] iii) Slope ratio criterion, where the fluctuations of the derivatives around pixel i are compared with each other.

[0084] For the definition and possible embodiments of the method or parts thereof, reference is made to the definitions and embodiments described for the method as described in the first aspect.

[0085] If each individual criterion C i) 、C ii) and C iii) of the abnormal pixel is satisfied, then a group of pixels at positions i + 1,..., i + N can be considered as abnormal pixels, where N is a predefined number, and these individual criteria are combined into one criterion C:

[0086] C = C i) ∧C ii) ∧C iii) .

[0087] These criteria can allow for reliable detection of weaker abnormal pixels, especially those within noise. These criteria can be particularly suitable for low-frequency (e.g., high-resolution) spectra, such as fringe patterns.

[0088] The method can include using additional criteria, such as using a quantization factor C i , as described above. For example, the quantization factor C i can be determined by the following formula

[0089]

[0090] where n is the power, σF，i is the standard deviation of the reference spectrum, and σ i is the standard deviation of the measured signal of pixel i. Alternatively or additionally, the quantization factor can be determined according to the above equation by using a weighting factor for one or more of σ F，i and σ i . The standard deviation of the signal of a pixel can be determined by using multiple signals determined for the pixel. For example, when n = 2, the quantization factor is determined by the following formula:

[0091]

[0092] The quantization factor C i of a pixel can be compared with the limit C max . Once the following formula is satisfied, the pixel may not meet this standard:

[0093] C i ≥C max .

[0094] In the case of C i ≥C max , pixel i can be classified as an abnormal pixel. Therefore, by comparing the detected spectrum S i with the smoothed spectrum generated according to S i , the proposed method allows for providing a significance rating for the identification of abnormal pixels. This technique can be used to distinguish abnormal pixels from noise.

[0095] The method can include using multiple criteria. In some embodiments, a pixel can be considered an abnormal pixel when all criteria are met. In some embodiments, a pixel can be considered an abnormal pixel when a subset of the criteria is met.

[0096] The method can be computer-implemented.

[0097] A computer program is further disclosed and proposed herein, which includes computer-executable instructions for performing one or more of the methods according to the present invention in one or more of the embodiments included herein when the program is executed on a computer or a computer network. Specifically, the computer program can be stored on a computer-readable data carrier and / or a computer-readable storage medium.

[0098] As used herein, the terms "computer-readable data carrier" and "computer-readable storage medium" can specifically refer to non-transitory data storage devices, such as hardware storage media on which computer-executable instructions are stored. The computer-readable data carrier or storage medium can specifically be or can include storage media such as random access memory (RAM) and / or read-only memory (ROM) etc.

[0099] Accordingly, specifically, one, more than one or even all of the method steps indicated above (such as method steps a) to b)) can be performed by using a computer or a computer network, preferably by using a computer program.

[0100] A computer program product having program code means is further disclosed and proposed herein for performing one or more methods according to the present invention in one or more embodiments included herein when the program is executed on a computer or a computer network. Specifically, the program code means can be stored on a computer-readable data carrier and / or a computer-readable storage medium.

[0101] A data carrier having a data structure stored thereon is further disclosed and proposed herein, which can perform one or more methods according to one or more embodiments disclosed herein after being loaded into a computer or a computer network (such as being loaded into the working memory or main memory of a computer or a computer network).

[0102] A computer program product having program code means stored on a machine-readable carrier is further disclosed and proposed herein for performing one or more methods according to one or more embodiments disclosed herein when the program is executed on a computer or a computer network. As used herein, a computer program product refers to a program as a tradable product. The product can generally exist in any format, such as in a paper format, or on a computer-readable data carrier and / or a computer-readable storage medium. Specifically, the computer program product can be distributed via a data network.

[0103] Finally, a modulated data signal is disclosed and proposed herein, which contains instructions readable by a computer system or a computer network for performing one or more methods according to one or more embodiments disclosed herein.

[0104] Referring to the computer-implemented aspects of the present invention, one or more or even all of the method steps of one or more of the methods according to one or more of the embodiments disclosed herein can be performed by using a computer or a computer network. Thus, generally, any of the method steps including the provision and / or manipulation of data can be performed by using a computer or a computer network. Generally, these method steps can include any method steps, except for method steps that typically require manual work, such as providing samples and / or performing certain aspects of actual measurements.

[0105] Specifically, further disclosed herein is:

[0106] - A computer or a computer network, the computer or the computer network including at least one processor, wherein the processor is adapted to execute a method according to one of the embodiments described in the present specification,

[0107] - A computer-loadable data structure, the computer-loadable data structure being adapted to execute a method according to one of the embodiments described in the present specification when the data structure is executed on a computer,

[0108] - A computer program, wherein the computer program is adapted to execute a method according to one of the embodiments described in the present specification when the program is executed on a computer,

[0109] - A computer program, the computer program including program means for executing a method according to one of the embodiments described in the present specification when the computer program is executed on a computer or a computer network,

[0110] - A computer program, the computer program including the program means according to the previous embodiment, wherein the program means is stored on a computer-readable storage medium,

[0111] - A storage medium, wherein a data structure is stored on the storage medium, and wherein the data structure is adapted to execute a method according to one of the embodiments described in the present specification after being loaded into the main storage device and / or the working storage device of a computer or a computer network, and

[0112] - A computer program product having program code means, wherein the program code means can be stored on or is stored on a storage medium for executing a method according to one of the embodiments described in the present specification when the program code means is executed on a computer or a computer network.

[0113] In another aspect of the present invention, a photodetector for measuring optical radiation is disclosed. The photodetector is configured to perform one or more of the methods according to the present invention (such as according to any one of the embodiments disclosed above and / or any one of the embodiments disclosed in further detail below). The photodetector includes a plurality of pixels i, where i is the pixel position and i>2, and each pixel includes at least one photosensitive region. Each of the pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region by optical radiation. The photodetector includes at least one readout electronics unit. For the definition and possible embodiments of the photodetector or parts thereof, reference is made to the definitions and embodiments described with respect to the method.

[0114] In a further aspect of the present invention, a spectrometer for spectral analysis of optical radiation provided by at least one measurement object is disclosed. The spectrometer includes:

[0115] - at least one radiation source configured to emit optical radiation at least partially towards the object; and

[0116] - at least one photodetector according to the present invention (such as according to any one of the embodiments disclosed above and / or according to any one of the embodiments disclosed in further detail below).

[0117] The spectrometer can be a reflection spectrometer device or a transmission spectrometer device.

[0118] The spectrometer can further include at least one optical element. The optical element can be positioned in the beam path before the photodetector. The optical element can include at least one wavelength selection element. As used herein, the term "wavelength selection element" is a broad term and will be given its ordinary and conventional meaning to a person of ordinary skill in the art and is not limited to a special or custom meaning. Specifically, the term can refer to, but is not limited to, an optical element configured to selectively transmit light of different wavelengths. Specifically, the wavelength selection element can be configured to transmit an incident beam, and thereby modify the spectral composition of the incident light upon transmission. The modification of the transmitted light can include one or more of the following: spatially separating light of different wavelengths; attenuating light of different wavelengths. For example, the wavelength selection element can be configured to selectively transmit light within a specific wavelength range while absorbing, filtering, and / or interfering with the rest of the light. The wavelength selection element can include at least one element selected from the group consisting of: a prism; a grating; a linear graded filter; an optical filter.

[0119] For further details regarding the spectrometer, reference can be made to the above and the description of the photodetector and method as described in more detail below.

[0120] In another aspect of the present invention, there is disclosed a use of a spectrometer as described in any of the embodiments of the spectrometers in the embodiments further described in detail above or below for a use purpose selected from the group consisting of: infrared detection applications; thermal detection applications; thermometer applications; heat-seeking applications; flame detection applications; fire detection applications; smoke detection applications; temperature sensing applications; spectroscopy applications; exhaust monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; motion control applications; emission control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; mobile spectroscopy applications; food analysis applications.

[0121] As used herein, the terms "having", "including", or "comprising" or any arbitrary grammatical variations thereof are used in a non-exclusive manner. Thus, these terms can either refer to a situation where no additional features exist in the entity described in the context other than the features introduced by these terms, or to a situation where one or more additional features exist. As an example, the expressions "A has B", "A includes B", and "A comprises B" can either refer to a situation where no other elements exist in A other than B (i.e., the situation where A consists solely and exclusively of B), or to a situation where one or more additional elements (such as element C, elements C and D, or even additional elements) exist in entity A in addition to B.

[0122] Furthermore, it should be noted that the terms "at least one", "one or more", or similar expressions indicating that a feature or element can occur once or more than once are typically used only once when introducing the corresponding feature or element. In most cases, the expressions "at least one" or "one or more" are not repeated when referring to the corresponding feature or element, but in fact, the corresponding feature or element may occur once or more than once.

[0123] Furthermore, as used herein, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically", or similar terms are used in conjunction with optional features without restricting the possibilities of alternatives. Thus, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. As those skilled in the art will recognize, the present invention can be implemented by using alternative features. Similarly, features introduced by "in an embodiment of the present invention" or similar expressions are intended to be optional features, without any limitation to alternative embodiments of the present invention, without any limitation to the scope of the present invention, and without any limitation to the possibility of combining the features introduced in this way with other optional or non-optional features of the present invention.

[0124] In summary, and without excluding other possible embodiments, the following embodiments can be envisaged:

[0125] Embodiment 1. A method for automatically controlling the quality of at least one photodetector comprising a plurality of pixels i, wherein i is a pixel position and i>2, wherein each pixel comprises at least one photosensitive region, wherein each of the pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region with optical radiation, wherein the method comprises classifying abnormal pixels by:

[0126] a) measuring a plurality of signals S of the pixels by measuring at least one object using the photodetector i ;

[0127] b) using at least one processor to transform these measurement signals S i With at least one reference spectrum S F，i Compare and determine at least one quantization factor C for each pixel i i , to quantify the corresponding measurement signal S i With the reference spectrum S F，i The deviation between them, and the corresponding quantization factor C i With at least one threshold C max Compare, where the corresponding quantization factor C i Exceeding this limit C max In this case, the pixel is classified as an abnormal pixel.

[0128] Embodiment 2. The method according to the previous embodiment, wherein the method comprises: by using the signals S of these pixels i To determine the measured spectrum, where the reference spectrum S F，i The processor is responsible for these signals S i At least one smoothing filter is applied to the signals S i Determined, wherein the smoothing filter is at least one filter selected from the group consisting of: a Savitzky–Golay filter, an n-th order polynomial with n>4, a moving average filter, a local regression smoothing, a low-pass filter, or other filters in pixel space or Fourier space.

[0129] Embodiment 3. The method according to any one of the preceding embodiments, wherein the quantization factor is determined by the following formula:

[0130]

[0131] where σ F，i is the standard deviation of the reference spectrum, and σ iis the standard deviation of the measurement signal of pixel i.

[0132] Example 4. The method according to any one of the preceding examples, wherein, at C i ≥C max pixel i is classified as an abnormal pixel.

[0133] Example 5. The method according to any one of the preceding examples, wherein the method further comprises analyzing the signal behavior around pixel position i.

[0134] Example 6. The method according to the preceding example, wherein the method comprises searching for a predefined number of abnormal pixels in a row, wherein if each individual criterion C for an abnormal pixel is satisfied j , then a group of pixels at positions i + 1,... i + N are considered to be abnormal pixels, where N is the predefined number, and these individual criteria are combined into one criterion C:

[0135] C = C1 ∧ C2 ∧... C N .

[0136] Example 7. The method according to any one of the preceding examples, wherein the method comprises a linear criterion, wherein analyzing the signal behavior around pixel position i comprises comparing the local derivative of the signal S i+1 –S i with the derivatives at these adjacent pixels.

[0137] Example 8. The method according to any one of the preceding two examples, wherein the group of pixels is classified as an abnormal pixel in the following case:

[0138]

[0139] where t d is at least one predefined threshold.

[0140] Example 9. The method according to any one of the preceding three examples, wherein the method comprises a peak skipping criterion, wherein analyzing the signal behavior around pixel position i comprises comparing the sign of the local derivative at pixel i with the signs of the derivatives of these adjacent pixels.

[0141] Example 10. The method according to any one of the preceding four examples, wherein the group of pixels is classified as an abnormal pixel in the following case:

[0142] (S i+1 -S i )*(S i+1+N -S i+N ) > 0.

[0143] Example 11. The method according to any one of the preceding five examples, wherein the method includes a slope ratio criterion, wherein analyzing the signal behavior around pixel position i includes comparing the fluctuations of the derivatives around pixel i, and wherein if the following formula is satisfied, pixels i+1, … i+N are classified as abnormal pixels:

[0144]

[0145] Example 12. The method according to any one of the preceding examples, wherein the object includes at least one material having a plurality of dynamic spectral regions, such that at least one fringe pattern is generated.

[0146] Example 13. The method according to the previous example, wherein the method is repeated with a second fringe pattern having a spectral phase shift.

[0147] Example 14. The method according to any one of the preceding examples, wherein the method includes masking all pixels classified as abnormal pixels.

[0148] Example 15. The method according to the previous example, wherein the method includes at least one measurement step, wherein the measurement step includes using the photodetector to determine at least one spectrum, and wherein the masked pixels are ignored or corrected mathematically using adjacent pixels.

[0149] Example 16. The method according to any one of the preceding examples, wherein the method is computer-implemented.

[0150] Example 17. A method for automatically performing quality control on at least one photodetector, wherein the photodetector includes a plurality of pixels i, where i is the pixel position and i>2, wherein each pixel includes at least one photosensitive region, and wherein each of these pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation, and wherein the method includes classifying abnormal pixels by analyzing the signal behavior around pixel position i, and wherein the analysis includes testing one or more of the following criteria C j among:

[0151] i) Linear criterion, wherein the local derivative of the measured signal is compared with the derivatives of adjacent pixels;

[0152] ii) Peak skip criterion, wherein the sign of the local derivative at pixel i is compared with the signs of the derivatives of these adjacent pixels;

[0153] iii) Slope ratio criterion, wherein the fluctuations of the derivatives around pixel i are compared with each other.

[0154] Example 18. The method according to the previous embodiment, wherein the method is computer-implemented.

[0155] Example 19. A photodetector for measuring optical radiation, the photodetector being configured to perform the method according to any one of the previous embodiments of the method described above, wherein the photodetector comprises a plurality of pixels i, where i is the pixel position and i > 2, wherein each pixel comprises at least one photosensitive region, wherein each of these pixels is configured to generate a signal in response to illumination of its corresponding photosensitive region by optical radiation, and wherein the photodetector comprises at least one readout electronics unit.

[0156] Example 20. A spectrometer for spectral analysis of optical radiation provided by at least one object, the spectrometer comprising:

[0157] - at least one radiation source configured to emit optical radiation at least partially towards the object; and

[0158] - at least one photodetector according to the previous embodiment.

[0159] Example 21. A computer program comprising instructions which, when the program is executed by the photodetector according to Example 19, cause the photodetector to perform one or more of the methods according to any one of the previous embodiments of the method described above.

[0160] Example 22. A computer-readable storage medium comprising instructions which, when the instructions are executed by the photodetector according to Example 19, cause the photodetector to perform one or more of the methods according to any one of the previous embodiments of the method described above.

[0161] Example 23. A non-transitory computer-readable medium comprising instructions which, when the instructions are executed by one or more processors, cause the one or more processors to perform one or more of the methods according to any one of the previous embodiments of the method described above.

[0162] Use of the spectrometer according to embodiment 20 for a use purpose selected from the group consisting of: infrared detection applications; thermal detection applications; thermometer applications; heat seeking applications; flame detection applications; fire detection applications; smoke detection applications; temperature sensing applications; spectroscopy applications; exhaust monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; motion control applications; emission control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; mobile spectroscopy applications; food analysis applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0163] Additional optional features and embodiments will be preferably disclosed in more detail in the subsequent embodiments in combination with the dependent claims. Among them, as those skilled in the art will recognize, the corresponding optional features can be implemented in an independent manner and in any feasible combination. The scope of the present invention is not limited by the preferred embodiments. The embodiments are schematically depicted in the drawings. Among them, the same reference numerals in these drawings denote the same or functionally equivalent elements.

[0164] In the drawings:

[0165] Figure 1 An embodiment of a spectrometer for spectral analysis of light radiation provided by at least one object and a photodetector for measuring light radiation is shown;

[0166] Figures 2A to 2D An exemplary spectrum of a PET sample is shown;

[0167] Figure 3 A flowchart of an embodiment of a method for automatically quality controlling at least one photodetector is shown; and

[0168] Figure 4 A method for Figure 3 An embodiment of a fringe pattern of the method is shown. DETAILED DESCRIPTION

[0169] Figure 1 An exemplary embodiment of a spectrometer 110 for spectral analysis of light radiation provided by at least one object 112 and a photodetector 114 for measuring light radiation is shown. As can be seen from Figure 1As can be seen, in addition to the photodetector 114, the spectrometer 110 includes at least one radiation source 116 that is configured to emit light radiation 118 at least partially toward the object 112. The radiation source 116 can include at least one of a semiconductor-based radiation source or a thermal radiator. The at least one semiconductor-based radiation source can be selected from at least one of a light-emitting diode (LED) or a laser (specifically, a laser diode). The LED can include at least one fluorescent and / or phosphorescent material. The thermal radiator can include at least one of an incandescent lamp, a blackbody emitter, and a microelectromechanical system (MEMS) emitter. However, other types of radiation sources are also feasible. As can be seen from Figure 1 As can be seen, an exemplary embodiment of the spectrometer 110 is a reflection spectrometer, and the radiation source 116 can be an internal radiation source, specifically included within the housing of the spectrometer 110 together with other components of the spectrometer 110. However, the spectrometer 110 can also be a transmission spectrometer, and the radiation source 116 can be an external radiation source.

[0170] The spectrometer 110 can further include at least one optical element 120. The optical element 120 can be positioned in the beam path before the photodetector 114. The optical element 120 can include at least one wavelength selection element 122. For example, the wavelength selection element 122 can be configured to selectively transmit light within a specific wavelength range while absorbing, filtering, and / or interfering with the rest of the light. The wavelength selection element 122 can include at least one element selected from the group consisting of: a prism; a grating; a linear gradient filter; an optical filter. As Figure 1 shown, the light radiation 118 reflected by the object 112 can be selectively transmitted by the optical element 120 toward the photodetector 114. When transmitting the light radiation 118 to the photodetector 114, the optical element 120 can be configured to separate the incident light radiation 118 into a spectrum having component wavelength components 124.

[0171] The photodetector 114 includes a plurality of pixels i (represented by reference numeral 126), where i is the pixel position and i > 2. Each pixel 126 includes at least one photosensitive region. Each of the pixels 126 is configured to generate a signal in response to illumination of its corresponding photosensitive region by the light radiation 118. In Figure 1 an exemplary embodiment, the plurality of pixels 126 of the photodetector 114 are arranged in an array of pixels 126, specifically arranged in a linear array of pixels 126. However, other types of arrangements (such as a matrix, etc.) are also feasible.

[0172] The photodetector 114 includes at least one readout electronics unit 128. The readout electronics unit 128 may include at least one of the following: an operational amplifier; an analog-to-digital converter; a voltage divider; a shunt; an ASIC, specifically for subtracting a constant current to generate a signal current.

[0173] The spectrometer 110 may further include at least one processor 130. The processor 130 may be at least partially cloud-based. The processor 130 may include at least one communication interface 132, particularly at least one of a wireless interface or a wired interface. The communication interface 132 may be configured to perform at least one of the following: transmit data from the processor 130 or to the processor or within the processor. For example, as Figure 1 shown, the communication interface 132 may be configured to transmit data, specifically spectral data (such as multiple signals for pixel 126), from the photodetector 114, specifically from the readout electronics unit 128, to the cloud-based processor 130 via a wireless connection.

[0174] The photodetector 114 is configured to perform a method for automatically quality controlling at least one photodetector 114 including a plurality of pixels 126. Specifically, the photodetector 114 may be configured to perform the method according to Figure 3 the embodiment shown. Thus, for the description of the method, reference may be made to Figure 3 the description. Further, the photodetector 114 may also be configured to perform the method in any other possible embodiments disclosed herein.

[0175] Figures 2A to 2D An exemplary spectrum of a PET sample is shown. Figures 2A to 2D The spectrum shown in Figure 1 may be measured using the spectrometer 110 according to the present invention (such as the spectrometer 110 exemplarily shown in

[0176] Figure 2A shows the absorbance 134 of a PET sample as a function of the pixel position i 136. Specifically, as can be seen from the enlarged view in Figure 2A , the figure shows the absorbance of five sample measurements (represented by reference numeral 135) and the absorbance after smoothing with a Savitzky–Golay filter (represented by reference numeral 137). As will be further outlined in detail below, the filtered measurement value 137 may be used as a reference spectrum S F，i . Further, as can be seen from Figure 2AAs can be best seen in the enlarged view, signals from some of the pixels in pixel 126 may deviate from the expected curve of the PET sample and thus represent abnormal pixels. In this example, the pixels at positions i = 154 and i = 155 represent abnormal pixels. Pixels at positions i = 200 and above may be noise pixels.

[0177] Figures 2B to 2D A comparison of noise and a pair of abnormal pixels is shown. In particular, this pair of abnormal pixels shows that abnormal pixels are not necessarily 100% short-circuited (same absorbance), but rather, in the case of a high-ohm short circuit, abnormal pixels may cause only a small distortion.

[0178] In Figure 2B similar to Figure 2A absorbance 134 is shown as a function of pixel position i 136. Pixels 134 to 135 are abnormal pixels. Starting from pixel 235, noise dominates, such that these pixels must be ignored as they may be random outliers. Figure 2C An enlargement of an abnormal pixel region with a high signal-to-noise ratio is shown. Figure 2D An enlargement of an abnormal pixel region with a low signal-to-noise ratio is shown. To reliably detect whether these pixels are abnormal pixels, a method for automatically quality controlling at least one photodetector 114 can be performed as exemplarily shown in Figure 3 .

[0179] Figure 3 A flowchart of an exemplary embodiment of a method for automatically quality controlling at least one photodetector 114 is shown. The photodetector 114 can be implemented as shown in Figure 1 . Thus, for the description of the photodetector 114, reference is made to the description in Figure 1 .

[0180] The method includes classifying abnormal pixels (represented by reference numeral 144) by the following steps:

[0181] a) (represented by reference numeral 146) measuring a plurality of signals S of pixel 126 by using the photodetector 114 to measure at least one object 112, and optionally determining a reference spectrum S from the corresponding measured signals S by using at least one processor 130 i , i ; F，i

[0182] b) (represented by reference numeral 148) comparing the measured signal S by using the processor 130 with the reference spectrum S to determine at least one quantization factor C for each pixel i 126 i to quantify the corresponding measured signal S F，i , i ​i the deviation from the reference spectrum S F，i and comparing the corresponding quantization factor C i with at least one threshold C max wherein, when the corresponding quantization factor C i exceeds the limit C max the pixel 126 is classified as an abnormal pixel.

[0183] The method steps can be executed in the indicated order. However, it should be noted that different orders are also possible. The method can include additional method steps not listed. Further, one or more of the method steps can be executed once or repeatedly. Further, two or more of the method steps can be executed simultaneously or in a time-overlapped manner. The method can include repeating step a) and step b) at a predefined time or continuously.

[0184] the signal S i can be a signal generated by the pixel i 126 in response to illumination. The method can include measuring multiple signals S for each of the pixels i126 i , for example, by repeatedly measuring the object 112 with the photodetector 114. For example, the method can include performing two, three, four, five, up to ten or even more measurements for each pixel i 126. The method can include determining multiple measurement spectra by using the signals of the pixels 126 and a function of the pixel i 126. For example, in the case of a modulated radiation source, the measurement spectra can be determined by recording multiple imaging frames (e.g., 1000 imaging frames). Multiple signals of the photodetector (depending on the modulation frequency) can be measured with and without illumination. These signals can be evaluated, for example, by using one or more of at least one FFT or DFT. The evaluation can further include using a standard white measurement to determine the measurement spectra. For example, the measurement spectra can be determined by determining the average value of the pixel signals varying with time. The method can include determining multiple spectra, for example, during step a) and / or by repeating step a). These measurement spectra can be used to classify whether the deviation (outlier) of the pixels with suspicious results is caused by the SNR or is systematically present and thus must be classified as abnormal pixels. Additionally, the reference spectrum S can be determined from the signal S i by applying at least one smoothing filter to the signal S i F，i ​。The smoothing filter can be at least one filter selected from the group consisting of: Savitzky–Golay filter, nth-order polynomial with n > 4, moving average filter, local regression smoothing, low-pass filtering, or other filters in pixel space or Fourier space. For example, in step a), the smoothing filter is applied to the signal Si. For example, the Savitzky–Golay filter can be used, which performs a least-squares fit of the signal at position i and adjacent signals with an nth-order polynomial.

[0185] In Figure 3 an embodiment of the method, method steps a) and b) can be specifically performed by using the stripe pattern 150, especially by using the first stripe pattern. Figure 4 An exemplary embodiment of the stripe pattern 150 for performing method steps a) and b) is shown. Specifically, in Figure 4 the figure, the signal intensity 152 is shown as a function of the pixel position i 136. Steps a) and b) and optionally one or more checks of the criteria further outlined in detail below can be repeated with a second stripe pattern that has a phase shift with respect to the first measurement spectrum. This can allow the peak and valley regions of the first pattern 150 to also be covered by the dynamic regions of the second pattern.

[0186] As an example, the quantization factor can be determined by the following formula:

[0187]

[0188] where n is the power, σ F，i is the standard deviation of the reference spectrum, and σ i is the standard deviation of the measured signal of pixel i 126. The standard deviation of the signal of pixel 126 can be determined by using multiple signals determined for the pixel 126. For example, when n = 2, the quantization factor is determined by the following formula:

[0189]

[0190] The quantization factor C i of pixel 126 can be compared with the limit C max . Once the following formula is satisfied, pixel 126 may not meet this standard:

[0191] C i ≥C max .

[0192] In the case of C i ≥C max , pixel i 126 can be classified as an abnormal pixel. Thus, by comparing the detected spectrum S i with that according to S iWhen comparing with the generated smooth spectrum, the proposed method allows for providing a significance rating for the identification of abnormal pixels. This technique can be used to distinguish abnormal pixels from noise.

[0193] Abnormal pixels will not be confused with noise (noise pixels). Noise pixels are pixels 126 that generate too little signal and are not caused by electrical or optical errors. This can be seen in the Figure 4 figure. In Figure 4 , the noise pixels are represented by reference numeral 154, while the abnormal pixels are marked by the circle 156. The significance rating of step b) can be used to reliably identify the noise pixels 154 at pixel position i = 235 and above. These noise pixels 154 can not be classified as pseudo-abnormal pixels. Therefore, although the short-circuit pixels at reference numeral 156 are only weaker, the noise pixels 154 can be distinguished from the abnormal pixels 156 by using the method according to the present invention.

[0194] To ensure highly reliable abnormal pixel detection, the method can include performing a two-step check. In addition to step b), the method can include analyzing the signal behavior (represented by reference numeral 158) around pixel position i 136. The method can include searching for a predefined number of abnormal pixels 126 in a row, especially adjacent abnormal pixels 126. Analyzing the signal behavior around pixel position i 136 can include testing one or more criteria C j . If each individual criterion C j for abnormal pixels is met, a group of pixels (also referred to as a pixel cluster) at positions i + 1,... i + N 136 can be considered as abnormal pixels, where N is a predefined number, and these individual criteria are combined into a single criterion C:

[0195] C = C1 ∧ C2 ∧... C N .

[0196] For example, for N = 2, the method can include searching for two short-circuit pixels 126. For example, for N = 3, the method can include searching for three short-circuit pixels 126. In particular, when pixel i + 1 126 is connected to pixel i + 3 126, the method includes such a search.

[0197] For example, the method can include a linear criterion. Analyzing the signal behavior around pixel position i 136 can include comparing the signal S i+1 – S iThe local derivative at a pixel is compared with the derivative at an adjacent pixel 126. The change in the derivative in the signal is limited by the optical resolution of the spectrometer system. By comparing the local derivatives in the spectrum between adjacent pixels, additional outlier criteria are defined. When there are large fluctuations in the derivative around pixels i+1...i+N 126, then this set of pixels may be outlier pixels. For example, if the following equation is satisfied, then pixels i+1,...i+N 126 are classified as outlier pixels:

[0198]

[0199] This can be rewritten as:

[0200]

[0201] where t d is at least one predefined threshold. The predefined threshold t d can be chosen to specify the intensity at which outlier pixels should be identified. In particular, the predefined threshold can be chosen to allow the distinction between noise and outlier pixels. For example, t d can be in the range of 1.5 to 1.7.

[0202] For example, the method can include a peak skipping criterion. Analyzing the signal behavior around pixel position i 136 can include comparing the sign of the local derivative at pixel i 126 with the sign of the derivative of an adjacent pixel 126. If the following equation is satisfied, then pixels i+1,...i+N can be classified as outlier pixels:

[0203] f(S i -S i+1 )*g(S i+N -S i+1+N ) > 0,

[0204] where f and g are functions such as linear functions, polynomial functions, and / or power law functions. As an example, if the following equation is satisfied, then pixels i+1,...i+N 126 can be classified as outlier pixels:

[0205] (S i -S i+1 )*(S i+N -S i+1+N ) > 0.

[0206] This can be rewritten as:

[0207] (S i+1 -S i )*(S i+1+N -S i+N ) > 0.

[0208] Accordingly, the peak skipping criterion can include comparing the sign of the local derivative at pixel i126 with the derivatives of adjacent pixels 126, essentially searching for the zero crossing of the derivative, i.e., the saddle point of the signal.

[0209] For example, the method can include a slope ratio criterion. Analyzing the signal behavior around pixel position i 136 includes comparing the fluctuations of the derivatives around pixel i 126. If the following equation is satisfied, pixels i+1, … i+N can be classified as abnormal pixels:

[0210] |f(S i -S i+1 )-g(S i+N -S i+1+N )| < a*|h(S i -S i+1 )+i(S i+N -S i+1+N )|,

[0211] where f, g, h, and i are arbitrary functions, such as linear functions, polynomial functions, and / or power-law functions and . For example, if the following equation is satisfied, pixels i+1, … i+N 126 can be classified as abnormal pixels:

[0212]

[0213] This can be rewritten as:

[0214]

[0215] Accordingly, the slope ratio criterion can include comparing the fluctuations of the derivatives (which are similar to second derivatives). The slope ratio criterion can include checking whether the clusters of abnormal pixels on both sides change symmetrically enough to the slope.

[0216] If the significance rating of step b) is performed and, optionally, one or more of the mentioned criteria are checked, all abnormal pixels can be reliably identified and automatically masked. The method can further include at least one measurement step (represented by reference numeral 160), particularly after steps a) and b). Measurement step 160 includes determining at least one spectrum using a photodetector 114. The masked pixels can be ignored or corrected mathematically using adjacent pixels 126.

[0217] List of Reference Numerals

[0218] 110 Spectrometer

[0219] 112 Object

[0220] 114 Photodetector

[0221] 116 Radiation source

[0222] 118 Optical radiation

[0223] 120 Optical element

[0224] 122 Wavelength selection element

[0225] 124 Constituent wavelength component

[0226] 126 Pixel

[0227] 128 Readout electronics unit

[0228] 130 Processor

[0229] 132 Communication interface

[0230] 134 Absorbance

[0231] 135 Five - sample measurements of PET

[0232] 136 Pixel position

[0233] 137 Filtered measurements of PET

[0234] 138 Absorbance

[0235] 144 Classify abnormal pixels

[0236] 146 Measure multiple signals

[0237] 148 Compare the measured signal with a reference spectrum

[0238] 150 Fringe pattern

[0239] 152 Signal intensity

[0240] 154 Noise pixel

[0241] 156 Mark

[0242] 158 Analyze the signal behavior around the pixel position

[0243] 160 Measurement step

Claims

1. A method for automatically performing quality control on at least one photodetector (114) including a plurality of pixels i (126), where i is a pixel position (136) and i > 2, wherein, Each pixel (126) includes at least one photosensitive region, wherein these pixels (126) are arranged as at least one of an array or a matrix, wherein the pixel position is the position of the corresponding pixel in the array or matrix, wherein the photodetector (114) is configured to detect light radiation from at least one object (112), wherein each of these pixels (126) is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation (118), and wherein the method includes classifying abnormal pixels by the following steps: a) Measuring a plurality of signals S of these pixels (126) by measuring at least one object (112) using the photodetector (114) i ; b) By using the processor (130), these measurement signals S i are compared with at least one reference spectrum S F，i to determine, for each pixel i (126), at least one quantization factor C i to quantify the corresponding measurement signal S i with respect to the deviation from the reference spectrum S F，i , and to compare the corresponding quantization factor C i with at least one threshold C max , wherein, in the case where the corresponding quantization factor C i exceeds the limit C max , the pixel (126) is classified as an abnormal pixel wherein the method further includes analyzing the signal behavior around the pixel position i (136).

2. The method according to the preceding claim, wherein, The method includes: determining a measurement spectrum by using the signals S of these pixels (126) i wherein the reference spectrum S F，i is determined by using the processor (130) to apply at least one smoothing filter to the signals S i in accordance with the signals S i wherein the smoothing filter is at least one filter selected from the group consisting of: a Savitzky–Golay filter, an n-th order polynomial with n>4, a moving average filter, local regression smoothing, low-pass filtering, or other filters in pixel or Fourier space.

3. The method according to any one of the preceding claims, wherein, The quantization factor is determined by the following formula: where σ F，i is the standard deviation of the reference spectrum, and σ i is the standard deviation of the measured signal of pixel i (126).

4. The method according to any one of the preceding claims, wherein, In C i ≥ C max In this case, the pixel i (126) is classified as an abnormal pixel.

5. The method according to any one of the preceding claims, wherein, The method includes searching for a predefined number of abnormal pixels in a row, where if each individual criterion C of the abnormal pixels is satisfied j , then a group of pixels at positions i+1, … i+N are considered to be abnormal pixels, where N is the predefined number, and these individual criteria are combined into one criterion C: C = C1 ∧ C2 ∧... C N 。 6. The method according to any one of the preceding claims, wherein, The method includes a linearity criterion, wherein analyzing the signal behavior around the pixel position i (136) includes comparing the local derivative of the signal S i+1 – S i with the derivatives at these neighboring pixels (126), and / or wherein the method includes a peak skipping criterion, wherein analyzing the signal behavior around the pixel position i (136) includes comparing the sign of the local derivative at pixel i (126) with the signs of the derivatives at these neighboring pixels (126), and / or wherein the method includes a slope ratio criterion, wherein analyzing the signal behavior around the pixel position i (136) includes comparing the fluctuations of the derivatives around pixel i (126).

7. The method according to any one of the preceding claims, wherein, The method includes masking all pixels (126) classified as abnormal pixels, wherein the method includes at least one measurement step, and wherein the measurement step includes using the photodetector (114) to determine at least one spectrum, and wherein these masked pixels are ignored or corrected mathematically using adjacent pixels (126).

8. The method according to any one of the preceding claims, wherein The method is computer-implemented.

9. The method according to any one of the preceding claims, wherein The photodetector (114) is a reflection spectrometer device or a transmission spectrometer device.

10. A method for automatically performing quality control on at least one photodetector (114), wherein, The photodetector (114) includes a plurality of pixels i (126), where i is the pixel position and i > 2, where each pixel (126) includes at least one photosensitive region, where each of these pixels (126) is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation (118), where the method includes classifying abnormal pixels by analyzing the signal behavior around the pixel position i, where the analysis includes testing one or more of the following criteria C j among: i) Linear standard, wherein the local derivative of the measurement signal is compared with the derivatives of adjacent pixels; ii) Peak skipping standard, wherein the sign of the local derivative at pixel i (126) is compared with the signs of the derivatives of these adjacent pixels; iii) Slope ratio standard, wherein the fluctuations of the derivatives around pixel i (126) are compared with each other.

11. A photodetector (114) for measuring optical radiation (118), the photodetector (114) being configured to perform the method according to any one of the preceding claims relating to the method, wherein, The photodetector (114) includes a plurality of pixels i (126), where i is the pixel position (136) and i>2, wherein each pixel (126) includes at least one photosensitive region, wherein each of these pixels (126) is configured to generate a signal in response to illumination of its corresponding photosensitive region by light radiation (118), and wherein the photodetector (114) includes at least one readout electronics unit (128).

12. A spectrometer (110) for spectral analysis of light radiation (118) provided by at least one measurement object (112), the spectrometer (110) comprising: - at least one radiation source (116), the at least one radiation source being configured to emit light radiation (118) at least partially towards the object (112); and - at least one photodetector (114) according to the previous claim.

13. A computer program, the computer program including instructions which, when the program is executed by the photodetector (114) according to claim 11, cause the photodetector (114) to perform one or both of the methods according to any one of the previous claims relating to methods.

14. A computer-readable storage medium comprising instructions that, when executed by the photodetector (114) according to claim 11, cause the photodetector (114) to perform one or both of the methods recited in any of the preceding claims related to the method.

15. Use of the spectrometer (110) according to claim 12 for a purpose selected from the group consisting of: infrared detection applications; thermal detection applications; thermometer applications; thermal seeking applications; flame detection applications; fire detection applications; smoke detection applications; temperature sensing applications; spectroscopy applications; exhaust monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; motion control applications; emission control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; mobile spectroscopy applications; food analysis applications.

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