Harvest Imaging Blog

Again another year (almost) has passed.  I know it sounds a bit silly, but time is flying by, and I do have the impression that everything is moving faster than ever before.

2016 started with a great special issue of IEEE Transactions on Solid-State Imaging in January.  I had the honour of being the guest-editor-at-large for this special issue.  (What does the title of guest-editor-at-large mean ?  A lot of work !).  But I am a big fan of IEEE-ED and IEEE-JSSC, because these journals are great sources of information from and for our community.  So I was really pleased with the invitation of IEEE to serve as the guest-editor-at-large and I am happy that I could cooperate with my soul-mates in imaging.

In 2015 Harvest Imaging came with a new product on the market : a reverse engineering report of a particular imaging feature present in a commercial camera.  The first reverse engineering report was devoted to Phase Detection Auto-Focus Pixels.  And in the meantime, in 2016 I started with a new project.  Because the new project is still in the preparation phase, it is difficult to disclose the topic, but it will be based on tons and tons of measurements.  Recently I bought an EMVA1288 test equipment and I do hope to get started with it sometime after New Year.

The Harvest Imaging Forum 2016 was targeting “Robustness of CMOS Technology and Circuitry”.  I do have to admit that the interest in the 2016 Forum was less than in the 2015 Forum.  Something I do not immediately understand, because the robustness of CMOS is a topic that should be of interest to our imaging community as well.  The main objective of the Harvest Imaging Forum is to touch topics that are somewhat out of my own core expertise, but still important subjects for solid-state imaging.  (For subjects that belong to my own expertise, I do not have to hire external instructors of course.)  Nevertheless, Harvest Imaging will continue with the Forum, also in 2017.  I do have a topic and a speaker in mind, but the speaker himself does not know yet.  More info will follow  in the Spring 2017 I guess.

Although (or maybe just because ?) we did not had a new IISW in 2016 (the next one will be in 2017), 2 new conferences were launched in Europe : the AutoSens and the MediSens.  I attended both, also because both of them are organized by a good friend of mine, Robert Stead and his crew.  I was happy to see that new applications were introduced by young engineers that are working in the solid-state imaging field.  I am pretty sure that the next generation will be capable of continuing to grow the solid-state imaging business.  Imaging was never ever that big and appealing as it is today, and I am pretty sure that in the future imaging can and will become only bigger.

Welcome 2017 !  Looking forward to another great imaging year, with the IISW in Japan !

Wishing all my readers a Merry Christmas and a Happy New Year.  “See” you soon.

Albert, 23-12-2016.

The Signal-to-Noise Ratio quantifies the performance of a sensor in response to a particular exposure.  It quantifies the ratio of the sensor’s output signal versus the noise present in the output signal, and can be expressed as :

SNR = 20·log(S_out/?_out)

With :

• SNR : signal-to-noise ratio [dB],
• S_out : output signal of the sensor [DN, V, e^–],
• ?_out : noise present in the output signal [DN,V, e^–].

Notice that :

• the output signal and the noise level need to be expressed in the same way : in digital numbers (DN), in Volts (V) or in number of electrons (e^–),
• the specification of the SNR only makes sense if also the input signal is clearly specified. Without input signal, there is not output signal,
• the noise is the total temporal noise of all parts, included in the pixel itself as well as the readout chain of the pixel. For some applications the photon shot noise is included in ?_out as well, for others it is not (see further).

A few important remarks w.r.t. signal-to-noise ratio :

• the signal-to-noise ratio specified for an imager is a single number that is valid for all pixels. Because the pixels are analog in nature, they all differ (a little bit) from each other.  About 50 % of the pixels will have a lower signal-to-noise ratio than the specified value and about 50 % of the pixels will have a higher signal-to-noise ratio than the specified value,
• that single number does not have any information about the dominant noise source, neither about the column noise, row noise and/or pixel noise,
• the fixed-pattern noise is not included in the definition of SNR. The argumentation very often heard is that fixed-pattern noise can be easily corrected, but any correction or cancellation of fixed-pattern noise may increase the level of the temporal noise and will reduce the signal-to-noise ratio,
• in the case the sensor is used for video applications, very often the photon shot noise is omitted in the total noise ?_out, and actually the SNR listed in the data sheets is much higher than what the reality will bring. If the sensor is used for still applications, mostly the photon shot noise is included in the total noise ?_out,
• in a photon-shot noise limited operation of the sensor, the noise ?_out is by definition equal to the photon shot noise, and the maximum SNR that can be delivered by the sensor will be :

SNR_max = 20·log(S_sat/?S_sat) = 20·log(?S_sat) = 10·log(S_sat)

With :

• SNR_max : maximum signal-to-noise ratio [dB],
• S_sat : saturation output signal of the sensor [e^–],
• the various noise sources present in a sensor do (strongly) depend on temperature, so will the SNR. There is not a single noise source that is becoming better (= lower noise) at higher temperatures.  But in most data sheets the SNR is specified at room temperature.  Be aware that sensors that are not cooled or temperature stabilized, will run at a higher temperature than room temperature due to the self-heating of the sensor in the camera.  This effect will automatically reduce the SNR below the specified numbers in the data sheet.

In conclusion : the SNR specified and its value found in data sheets can never be reached in real imaging situations by all pixels because it is an average number, the fixed-pattern noise is not taken into account, the self-heating of the sensor lowers the SNR, and moreover, in many video applications the photon shot noise is omitted.

Albert, 16-12-2016.

The Dynamic Range (DR) of an imager gives an indication of the imager’s ability to resolve details in dark areas as well as details in light areas in the same image.  It indicates what is the largest signal that can be detected versus the smallest signal that can be detected.

Mathematically it is defined as :

DR = 20·log(S_sat/?_read)

with :

• DR : dynamic range [dB],
• S_sat : saturation signal of the sensor [DN or V or e^–],
• ?_read : noise in dark [DN or V or e^–].

Notice that :

• the saturation level and the noise in dark need to be expressed in the same way : in digital numbers (DN), in Volts (V) or in number of electrons (e^–),
• the noise in dark is the total temporal noise contribution of all electronic parts that are included in the readout chain, starting in the pixel.

A few important remarks w.r.t. dynamic range :

• the dynamic range specified for an imager mostly is a single number that should be valid for all pixels. Because the pixels are analog in nature, they all differ (a little bit) from each other, and in principle about 50 % of the pixels will have a lower dynamic range than the specified value and about 50 % of the pixels will have a higher dynamic range than the specified value,
• the noise in dark does not contain any noise related to the exposure of the imager, for instance dark current shot noise. So in reality the noise present in the output signal will always be higher than the one used in the calculation of the dynamic range because of the presence of dark-current shot noise.  Moreover, dark-current related noise source are strongly dependent on the integration time,
• in normal operation of an imager, its so-called junction-temperature of the sensor is always higher than room temperature at which the dynamic range is specified. Temperature has a serious impact on the noise level and consequently on the dynamic range,
• fixed-pattern noise is not included in the definition of DR. The argumentation is that fixed-pattern noise can be easily cancelled, but any correction or cancellation of fixed-pattern noise may increase the level of the temporal noise and will reduce the dynamic range again.

In conclusion : the DR specified and its value found in data sheets is a number that only has a theoretical value, it can never be reached by all pixels in real imaging situations, because it is an average number for all pixels.  Very often it can even not be reached by any of the pixels, because it does not take into account any exposure, neither any temperature effects or fixed-pattern noise issues.

Albert, 02-12-2016.