Abstract:
A method, apparatus and system that allows for the identification of defective pixels, for example, defective pixel clusters, in an imager device. The method, apparatus and system determine, during use of the imager device, that a pixel defect, e.g., cluster defect, exists and accurately maps the location of the defective pixel. By analyzing more than one frame of an image, the method increases the accuracy of the defect mapping, which is used to improve the quality of the resulting image data.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 13/052,660, filed Mar. 21, 2011, which is a continuation of U.S. patent application Ser. No. 11/509,712, filed Aug. 25, 2006, now U.S. Pat. No. 7,932,938 the entire disclosures of which are hereby incorporated by reference in their entirety. 
    
    
     FIELD OF THE INVENTION 
     Embodiments of the invention relate generally to the field of solid state imager devices, and more particularly to methods, apparatuses and systems for accurately mapping pixel defects in a solid state imager devices. 
     BACKGROUND OF THE INVENTION 
     Solid state imager devices, including charge coupled devices (CCD) and CMOS imagers, among other types, have been used in photo imaging applications. A solid state imager device includes a focal plane array of pixel cells, each one of the cells including a photosensor, which may be a photogate, photoconductor or a photodiode having a doped region for accumulating photo-generated charge. 
     During the manufacture of solid state imager devices, the creation of defective pixels is unavoidable. Some of the pixels in the imager device may be always dark (often due to shorts) or always too bright (often due to abnormally high leakage current). These defective pixels, if not corrected, can cause severe degradation of image quality and, as a result, decrease the yield of parts during production. Thus, minimization of these pixel defects during fabrication using close manufacturing tolerances will yield a higher quality product. However, it is usually less expensive to make an imager device using less precise manufacturing tolerances. In general, semiconductor devices produced using less precise manufacturing tolerances have a higher probability of defects. Typical semiconductor fabrication rules define some tradeoff between the quality (i.e., lack of defects) and cost of manufacture. The manufactured semiconductor devices are tested for defects, and any semiconductor device having more than a certain number of defects is usually discarded. 
     Image acquisition semiconductor devices (i.e., imager devices) are sensitive to pixel defects and a sensor with such defects may not yield aesthetically pleasing images. It is especially evident when defects are located in low frequency areas or at image contour edges. Edges in images are areas with strong intensity contrasts. A bad pixel in an imager device will show up as a bad area on the acquired image. The defective pixels may not work at all or, alternatively, they may be significantly brighter or dimmer than expected for a given light intensity. Depending on the desired quality and the intended application, a single defective pixel may sometimes be sufficient to cause the imager device containing the pixel to be discarded. 
     In most instances, however, a small percentage of defective pixels can be tolerated and compensated for. Numerous techniques exist for locating and correcting single defective pixels in an imager device. Correction of multiple defective pixels in a small area of an array, termed “cluster defects” or “defective pixel clusters,” however, presents increased challenges. Accurate location of these pixel cluster defects is one of those challenges. 
     One simple technique for correcting defective pixels involves taking a signal from each pixel in an array and storing the pixel signal values in memory. During image processing, the saved value for a defective pixel can be replaced by a signal value which is based on one or more signals from the neighboring pixels of the defective pixel. For example, the defective pixel signal can be substituted for an adjacent pixel signal value or for an average of the signal values from more than one pixel in the neighboring area of the pixel array. 
     These substitution techniques rely on accurate knowledge of the defective pixel locations. One of the widely used methods for determining the locations of defective pixels is off-line testing performed at the time of imager device fabrication at a factory. The defective pixel location determined during this off-line testing can be stored in a non-volatile memory in the imager device. The main disadvantage to this approach is that the number of defects that can be corrected is limited by the size of non-volatile memory dedicated to this purpose. Another drawback of this approach is that it requires a separate manufacturing step for the identification and storage of the pixel defect locations on the imager chip itself. 
     On the other hand, “on-the fly” cluster correction methods, i.e., those performed during use of the imager device rather than at the time of manufacture, have difficulties distinguishing between “true” defects and small image elements in the presence of arbitrary image content, and therefore, can lead to a more destructive image. This is particularly true for detection of “true” cluster defects. 
     Accordingly, there is a need and desire for a method, apparatus and system capable of accurately locating pixel defects, for example, pixel cluster defects, in a pixel array during use of an imager device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a pixel array defect map that can be calibrated in accordance with an embodiment of the invention. 
         FIG. 2  depicts one set of neighboring pixels from the pixel array map shown in  FIG. 1 . 
         FIG. 3  shows a flowchart of a method for calibrating a cluster defect map in an imager in accordance with an embodiment of the invention. 
         FIG. 4  shows a block diagram of an imager constructed in accordance with an embodiment of the invention. 
         FIG. 5  shows a processor system, for example a camera system, incorporating at least one imager device constructed in accordance with an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof and show by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice them, and it is to be understood that other embodiments may be utilized, and that structural, logical, and electrical changes may be made. The progression of processing steps described is just one example embodiment; however, the sequence of steps is not necessarily limited to that set forth herein and may be changed as would be understood by those of skill in the art, with the exception of steps necessarily occurring in a certain order. 
     The term “pixel,” as used herein, refers to a photo-element unit cell containing a photosensor device and associated structures for converting photons to an electrical signal. 
     In addition, although the embodiments of the invention are described as being employed with a CMOS imager, it should be appreciated that the disclosed embodiments could be used with any solid state imager technology, including CCD and others. 
     Method, apparatus and system embodiments are described below for performing on-the-fly adjustment of a pixel defect map for an imager device. The disclosed embodiments allow for the location and detection of pixel defects, including pixel cluster defects (either dark or bright), without the need for elaborate defect identification and storage during manufacturing of the imager device. 
     Most conventional defect detection methods analyze just one frame in an image. By analyzing just one frame of data, it is impossible, with these conventional methods, to distinguish between “true” pixel defects and image elements in the presence of arbitrary image content. A “true” pixel defect is constant and does not depend on the imaged scene. On the other hand, it is possible for pixel image elements that appear to be defects to change position as a scene is changing. For example, what produces a dark signal in one frame may be caused by movement in the imaged scene, rather than a pixel defect. Embodiments of the invention permit adjustment of a pixel defect map during use, such that non-defect pixels are not mistakenly mapped as defective for the life of the imager device. 
     The embodiments of methods, apparatuses and systems described herein detect the location of pixel defects, including cluster defects, and maintain an accurate map of such defects, which can be stored in a volatile or non-volatile memory. In accordance with the embodiments, a defect location method is performed to identify “true” defects and to classify the defect as either dark or bright. This is equally applicable to identifying and classifying “true” cluster defects. The location and classification information is saved, and can be updated, for further processing. After a pixel defect map is created using any of the disclosed embodiments, any method for correcting the signal from defective pixels may be used during subsequent image processing using the data created and stored in the pixel defect map. 
     The embodiments described herein can provide a map of true cluster defects, true individual pixel defects, or a combination of both. In addition, except where otherwise explained, the embodiments described herein can be used for either creating or updating pixel defect maps. 
     Now with reference to the Figures, where like numerals represent like elements, various embodiments are now described.  FIG. 1  illustrates a pixel array defect map  100 . The large “X” through pixel  32  near the center of the map  100  represents that pixel  32  has been identified as a defect pixel. In order for this pixel to be classified as a cluster defect, one or more additional pixels near the defect pixel  32  would have to be defective. For example, with reference to  FIG. 2 , for purposes of defining a cluster, one could examine the eight pixels in the neighboring set  101  immediately surrounding defect pixel  32 . As shown in  FIG. 1 , if any one of these eight pixels in set  101  is defective, such as pixel  33 , a cluster defect  102  exists. The pixel defect map  100  shows that pixels  32  and  33  are both defective, forming the cluster defect  102 . The pixel defect map  100  also shows that pixel  61  is defective. Pixel  61  is identified as an individual pixel defect, however, because none of the eight pixels immediately surrounding pixel  61  contains a defect. 
       FIG. 3  illustrates an on-the-fly method  150  of defect mapping and adjustment using the embodiment of the imager device illustrated in  FIG. 4  and described below. The method  150  is now described with reference to  FIGS. 1-4 . At least some of the steps in the method  150  may be used, in a first instance, to create a pixel defect map  100 . However, the flowchart as a whole illustrates a process for updating the pixel defect map  100 . Accordingly, prior to the first step in method  150 , a pixel defect map, such as e.g., pixel defect map  100 , would be stored in a memory of or associated with the pixel array  240  of the imager device  300 . The stored pixel defect map  100  may be created on-the-fly or during manufacturing of the imager device  300  using any known technique for pixel defect mapping, or using at least part of method  150 . 
     The method  150  is initiated by a triggering event (at step  151 ) which determines that the detected image has changed. As shown, the triggering event (at step  151 ) may be, for example, detection of motion, performed using known motion detection methods. Other triggering events could also be implemented at step  151 , including an automatic start mechanism at a certain pre-determined time. The remaining steps of method  150  are performed to adjust, if necessary, the saved defect map  100  that was established prior to the method-triggering event. 
     At step  152 , image processing begins. This processing includes exposure of an image, which can provide, for each pixel, a readout of a pixel reset Vrst and pixel light signal Vsig from each of the pixels in pixel array  240 , which are subtracted to produce a pixel output signal. Next, for an individual pixel-under-test, e.g., pixel  32 , at step  153 , the readout pixel output signals for that pixel and its neighboring pixels are analyzed. At step  154 , a determination whether the pixel-under-test  32  is a defect pixel and whether a pixel defect cluster is detected at that pixel location is made. For example, if pixel  32  is the pixel-under-test at step  153 , the pixel output signals in the neighboring set  101  of pixels will be examined. The determination of a defect cluster at step  154  thus involves determining whether pixel  32  as well as one or more pixels in the neighboring set  101  include pixel defects of a similar character (i.e., defectively bright or dark). 
     For purposes of labeling pixels as pixel defects, known methods may be employed. For example, pixel  32  could be labeled as a pixel defect if its pixel output signal is significantly different than those signals of its neighboring set  101 . If the pixel output signal from pixel  32  is much higher than those in its neighboring set  101  by some pre-determined threshold amount or percentage, the pixel  32  could be deemed a “BRIGHT” pixel; conversely, if the pixel output signal from pixel  32  is much lower than those in its neighboring set  101 , the pixel  32  could be deemed a “DARK” pixel. As another example, pixel defect thresholds V DARK  and V BRIGHT  defining the signal values at which a pixel is determined to be, respectively, a “DARK” or “BRIGHT” defect pixel could be set. Thus, if the pixel output signal from pixel  32  is greater than V BRIGHT  or less than V DARK , pixel  32  would be respectively labeled in pixel defect map  100  as a BRIGHT or DARK defect pixel. 
     Using any of these, or other, pixel defect identification techniques, at step  154 , a defect cluster will be detected if more than one similarly labeled pixel defect exists in the neighboring set  101  under consideration. The type of cluster is also identified at this step as including multiple “BRIGHT” or “DARK” pixel defects in an area. 
     If a pixel defect is detected at step  154 , the method continues at step  155 . For example, if a cluster defect is detected at step  154  associated with pixel-under-test  32 , a determination is made as to whether this cluster, for example cluster  102 , exists in the previously stored pixel defect map  100  (step  155 ). If the cluster  102  was not previously in the map, the map  100  is updated to include the cluster location and to mark the cluster with a temporarily label such as “DEFECT” (at step  156 ). It should be noted that in the present example, it is presumed that the defect was already in the pixel defect map  100 , which would yield a “Yes” response at step  155  (described below). Those clusters which are marked “DEFECT,” may be mapped as a true cluster if they are again found to be the same type of defect cluster in the next run through the method  150  (for example, when the next imaged scene is captured). 
     If at step  155 , the identified cluster  102  was previously in the map, a determination is made at step  157  as to whether the cluster  102  is the same type as the type previously detected. Specifically, this step  157  compares the type (either BRIGHT or DARK) of the cluster currently identified to information about the cluster in the stored pixel defect map  100 . If the cluster  102  is determined to be the same type of cluster as previously classified, it is marked at step  158  as a “TRUE CLUSTER.” If the presently identified cluster  102  is not the same type as was previously classified, it is not a true cluster; instead, this defect is the result of imaging errors. In that case, the method moves to step  161  where the defect cluster is removed from the defect map  100 . 
     It should be understood that a similar series of steps would be performed if it is found that pixel-under-test  32  is an individual pixel defect, but not part of a cluster defect. Specifically, the location of the pixel-under-test  32  would be compared at step  155  with the information in the stored defect map  100  for that location. If the same individual defect of the same classification was previously located at the pixel-under test  32  area, the pixel is determined to be a “TRUE DEFECT” defect and is marked as an individual “TRUE DEFECT” at step  158 . 
     If at step  154  a defect or defect cluster is not detected, the method  150  continues at step  159  where a determination is made as to whether the pixel-under-test  32  was previously identified as a pixel defect or as part of an identified cluster  102  in the pixel defect map  100  created prior to the triggering event  151 . Put another way, step  159  asks whether this pixel location in the pixel defect map  100  was tagged as a pixel defect or as part of a defect cluster. If this pixel location was not previously part of a cluster in the pixel defect map  100 , the method  150  proceeds to step  162 . If, however, the present pixel-under-test  32  is not currently part of a cluster defect, but it is determined at step  159  that it was part of a cluster in the stored pixel defect map  100 , the method proceeds to step  160 . Similarly, if the pixel-under test  32  is not currently identified as a pixel defect, but it is determined at step  159  that it was marked as a defect pixel in the stored pixel defect map  100 , the method  150  also continues at step  160 . 
     At step  160 , it is determined whether the conditions for detecting pixel defects and defect clusters are “good.” Specifically, step  160  is utilized so that defect clusters  102  are not removed from the pixel defect map  100  if some conditions external to the imaged scene have made detecting defect clusters presently difficult. For example, if a pixel has reached saturation, further processing of pixel output signals could be affected such that detecting “true” clusters is difficult. Accordingly, conditions are checked at step  160 , for example, by comparing pixel output signals to a saturation threshold. Any type of external condition can be checked at this stage if desired. In addition, the conditions that are considered at step  160  may be different for identifying DARK and BRIGHT pixel defects. 
     At this point, if acceptable conditions have been found at step  160 , the method proceeds to step  161  where the previously detected pixel defect or defect cluster  102  is removed from the pixel defect map  100 . For the present example, this means that what was previously considered a defect cluster  102  was not a true defect for the pixel sensor, but rather, was an image element that created a falsely defective pixel output signal. Thus at least one of the pixels that created a defective pixel output signal previously in defect cluster  102 , is now producing normal output signals which can be utilized in reproducing the sensed image. The stored pixel defect map  100  is thus updated appropriately. 
     The method  150  proceeds at step  162  with an inquiry into whether the pixel that was just considered (i.e., pixel  32 ) is the last pixel in the array  240  ( FIG. 4 ). If not, the method  150  is repeated beginning with step  153  for the next pixel. If, however, the pixel  32  considered was the last pixel in the array  240 , the updated pixel defect map  100  is available for use in further image processing. Those pixels identified as “TRUE” individual defects can be corrected using known pixel correction techniques. Those pixels identified as part of a “TRUE CLUSTER” can be corrected using substituted pixel output signals from adjacent non-defect pixels. For example, the pixel output signal values for pixels identified as being part of a “TRUE CLUSTER” can be corrected using the methods described in U.S. Patent Pub. No. 2006/0044425, assigned to Micron Technology, Inc., and incorporated herein by reference. 
     As stated above,  FIG. 4  illustrates an imager device  300  having a pixel array  240  which may be used to implement the method  150  described above. Row lines of the array  240  are selectively activated by a row driver  245  in response to row address decoder  255 . A column driver  260  and column address decoder  270  are also included in the imaging device  300 . The imager device  300  is operated by the timing and control circuit  250 , which controls the address decoders  255 ,  270 . The control circuit  250  also controls the row and column driver circuitry  245 ,  260 . 
     A sample and hold circuit  261  associated with the column driver  260  reads a pixel reset signal Vrst and a pixel image signal Vsig for selected pixels of the array  240 . A differential signal (Vrst−Vsig) is produced by differential amplifier  262  for each pixel and is digitized by analog-to-digital converter  275  (ADC). The analog-to-digital converter  275  supplies digitized pixel signals to an image processor  280  which may be implemented in hardware, software using a processor, or a combination of hardware and software. Image processor  280  is constructed to perform the defect map calibration method  150  of the present invention, possibly in conjunction with other defect location and correction processes and any other image processing operations as described herein and as known in the art. A pixel defect map  100  created in accordance with the invention can be stored in memory  278  associated with the image processor  280  in the imager device  300  and could be used in creating corrected output signals for defective pixels by an image processor  280 , which in turn, outputs a digital image. 
       FIG. 5  shows system  1100 , a simplified processor system having the imager device  300  ( FIG. 4 ) that implements embodiments of the invention. The system  1100  is exemplary of a system having digital circuits that could include imager device  300 . Without being limiting, such a system could include a computer system, still or video camera system, scanner, machine vision, video phone, and auto focus system, or other imager applications. 
     System  1100 , for example a camera system, generally comprises a central processing unit (CPU)  1102 , such as a microprocessor for controlling camera operations, that communicates with one or more input/output (I/O) devices  1106  over a bus  1104 . Imaging device  300  also communicates with the CPU  1102  over the bus  1104 . The processor system  1100  also includes random access memory (RAM)  1110 , and can include removable memory  1115 , such as flash memory, which also communicate with the CPU  1102  over the bus  1104 . The imaging device  300  may be combined with the processor, such as a CPU  1102 , with or without memory storage on a single integrated circuit or on a different chip than the CPU  1102 . 
     While embodiments of the invention have been described in detail in connection with exemplary embodiments known at the time, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the embodiments can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described. 
     For example, while embodiments of the invention have been described in which either pixel cluster defects or individual pixel defects are detected and compared with a pixel defect map and used to update the map, the embodiments may be modified to provide for detection of individual pixel defects and pixel defect clusters simultaneously, to generate data used to create a pixel defect map or update an existing pixel defect map. In addition, the invention is not limited to the type of imager device in which it is used. Although for illustrative purposes a CMOS imager device has been described, the invention may also be implemented with a CCD or other imager device.