Archive for August, 2015

How to Measure Anti-Blooming (2)

Monday, August 31st, 2015

This blog will focus on the measurement of the anti-blooming capabilities of a monochrome sensor.  As known, blooming occurs when a (group of) pixel(s) is overexposed and the photodiode can no longer store all generated charges.  With an anti-blooming structure inside the pixel, the excessively generated charges can be drained, e.g. excessive electrons can escape through the reset transistor to the power supply.  But every anti-blooming structure has its limitations, and with this measurement we try to find the limits of the anti-blooming structure present in a pixel.

What is checked with this measurement is simply the size of an overexposed sensor area.  If the illumination level is increased, ideally the size of such an overexposed area should stay constant even with an overexposure.  But in reality and while the illumination level is increasing, the size of the overexposed area will grow due to several mechanisms :

  • Diffraction at the various edges of the metal lines above a pixel will “guide” photons to neighbouring pixels,
  • Multiple reflections in the multi-level layer structure above the pixels can also “guide” photons to neighbouring pixels,
  • Fresnel reflections on the sensor surface and on the lens surface can result in ghosting structures,
  • Diffraction and reflections at the edges of the iris/diaphragm present in the optical system,
  • Optical and electrical cross-talk between the pixels,
  • Light piping underneath the metal lines and/or metal shields,
  • Blooming effects after the pixels are saturated and the anti-blooming is no capable of handling the excess charges.

All these effects are proportional to the amount of light that comes to the sensor.  In the measurements, the amount of light to the sensor is modulated by changing the exposure time.  That means that all these effects can be written down with a formula that contains a linear coefficient in relation to the exposure time.  This is true for all abovementioned effects, except for the blooming.  The blooming also has a linear relationship with exposure time, but with a particular threshold.  Below a certain exposure time, the pixel will not be saturated or the anti-blooming capabilities are performing well so that no blooming occurs.  Above a certain exposure time, the blooming effect will start and will be added to all other effects that grow the overexposure sensor area.  So the measurement will explore the size of an overexposure area as a function of exposure time and will try to find the knee point at which level the blooming effect occurs.

The measurement is performed as follows (to measure the anti-blooming along columns in vertical direction) :

  • The sensor is illuminated with a target that allows a black-white transition about half-way the sensor height, and the black-white transition horizontally crosses a column in the middle of the sensor (e.g. column 380 out of 752 active columns)
  • The location of the black-white transition is monitored in the images generated by the sensor.  To do so, the balck-white transition is defined at a level of 75 % of the white part of the test target (75 % is randomly chosen, any other value can do the job as well),
  • The illumination of the target is kept constant (white fluorescent DC light), and to get different light levels on the sensor, the exposure time of the imager is changed from very small values to very large values,
  • While changing the exposure levels, the location black-white transition is constantly calculated to monitor the growth of the overexposed area.

Figure 1 shows one of the images captured at the onset of saturation, Figure 2 illustrates the situation at 10 times overexposure, Figure 3 is the result while overexposing the sensor 100 times, and finally Figure 4 illustrates a factor of 1000 times overexposure.


Figure 1 : Image of the test target at the moment the sensor starts to saturate [0015].


Figure 2 : Image of the test target at the moment the sensor is 10 times overexposed [0019].


Figure 3 : Image of the test target at the moment the sensor is 100 times overexposed [0029].


Figure 4 : Image of the test target at the moment the sensor is 1000 times overexposed [0039].

A simple software tool is developed to check for every exposure time, on which row the horizontal black-white crossing is occurring in the images.  If the pixels are not saturated yet, the software tool simply outputs row number “500”, which actually does not exist.  As soon as at a particular illumination level the pixels in the white region reach 75 % of saturation, the measurement tool outputs the row number at which the black-white transition occurs.  If the overexposed area reaches the top of the image, like shown in Figure 4, the output of the measurement tool is equal to “0”.  The result of this analysis is shown in Figure 5.


Figure 5 : Position of the black-white transition (indicated as row number) as a function of exposure time.

In Figure 5, from left to right, the following information is available :

  • For small exposure times (< 1 ms), the white pixels are not yet reaching 75 % of saturation, this is indicated by the row value equal to “500”,
  • For an exposure value of 1.28 ms, saturation occurs (= 75 %) and the black-white transition is located at row number “224”,
  • From this moment onwards the large white area starts growing slowly due to all kind of optical artefacts, listed already earlier in this blog,
  • For exposure times larger than 200 ms, the area of the white spot grows very fast, as can be seen in the graph.  This change in “speed” is due to the blooming artefact that apparently occurs at very high exposure levels.

To calculate a number for the anti-blooming capabilities of the sensor, the same data as present in Figure 5 is shown again on a linear scale as illustrated in Figure 6.


Figure 6 : The same information is shown as already illustrated in Figure 5, but now on a linear scale.

The two important regions (saturated but no blooming and saturated with blooming) are approximated by means of a linear regression line.  And as can be seen, below 174 ms exposure time, blooming plays no important role, but above 174 ms exposure time, blooming is dominating over all other artefacts.  The exposure time of 174 ms seems to be a cross-over exposure time.

The anti-blooming capability is then defined as the ratio of the exposure time at which saturation is reached (texp = 1.28 ms) and the cross-over exposure time (texp = 174 ms), resulting in an anti-blooming capability of 136 times overexposure.

In conclusion : a long story to explain a relative simple measurement.  More anti-blooming stuff to follow.

Albert, 28-08-2015.

How (not) to Measure Anti-Blooming (1)

Monday, August 10th, 2015

After several months of silence, here is a new blog about measuring image sensors.  This time the blooming and/or anti-blooming of an imager is analyzed.  Actually in this first blog about blooming, it will be shown how NOT to measure blooming.

Blooming is the effect that is showing up in the case of strong overexposure of the image sensor.  If the pixels are seen as a buckets and the photon-generated electrons are seen as the water contained in these buckets, it is clear that the maximum amount of water that can be stored in the bucket is limited.  If more light is falling on the pixels, more water needs to be stored in the buckets.  But once a bucket is completely filled, any extra water is going to spill over to the neighbouring buckets.  The last effect is being known as blooming.  Any means in the pixel to prevent blooming is called anti-blooming.

The intention of the measurement reported in this blog, is to check out the anti-blooming capabilities of an image sensor.  Ideally this can be done by overexposing a single pixel and check any blooming in the neightbouring pixels, but that is not easy to realize.  An alternative way of measuring the anti-blooming capabilities is to use a colour sensor and illuminate the device with monochrome light. If the sensor is illuminated with blue light, the green and red pixels will have a smaller light sensitivity to blue light and the blue pixel will saturate much faster than the green and red pixels.  Once the blue pixel is saturated, its anti-blooming should become active.  Without anti-blooming, the blue pixel will spill over its charge into the green pixel (direct neighbours) and red pixel (diagonal neighbour).  If spilling occurs, the sensitivity of the green and/or red pixel will increase and this can be measured by monitoring the green and red output signal.

What is explained above is realized and the result is shown in Figure 1.

 Figure 1 : Response of the different colour planes (R, G, B) of a CMOS sensor under illumination with blue light (470 nm).

For the three colour planes, the regression line of the linear response is created as well.  The ratio between B and G response is 4347/1119 = 3.9.  The ratio between B and R response is 4347/140 = 31.  Unfortunately (for the measurement), no change in response can be seen in the G or R channel once the B channel is saturated.  Conclusion the anti-blooming towards direct neighbours is at least a factor 3.9, towards diagonal neighbours is at least a factor 31.

A similar measurement can be done with red light.  The result is illustrated in Figure 2.

 Figure 2 : Response of the different colour planes (R, G, B) of a CMOS sensor under illumination with red light (630 nm).

This time, the ratio between R and G response is 1169/243 = 4.8.  The ratio between R and B response is 1169/135 = 8.7.  Unfortunately (for the measurement), no change in response can be seen in the G or B channel once the R channel is saturated.  Conclusion the anti-blooming towards direct neighbours is at least a factor 4.8, towards diagonal neighbours is at least a factor 8.7.

Finally, the sensor was illuminated with green light, and the 3 colour channels were checked as shown in Figure 3.

 Figure 3 : Response of the different colour planes (R, G, B) of a CMOS sensor under illumination with green light (525 nm).

The ratio between G and B response is 1301/365 = 3.6, the ratio between G and R response is 1301/178 = 7.3.  Also for this situation no blooming artifacts can be found.

In conclusion : the anti-blooming capabilities are at least for direct neighbours a factor of 7.3, for diagonal neighbours a factor of 31.  These numbers are relatively small, but the measurement technique applied is not capable of doing better.  The numbers reported are limited by the characterization method and not by the sensor.  So actually, what is shown in this blog is how NOT to measure the anti-blooming of a sensor, unless your device-under-test has a very poor anti-blooming performance.

Albert, 10-08-2015.