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EMCCD vs. sCMOS for Microscopic Imaging

James R. Joubert and Deepak K Sharma, Photometrics

CCD sensors have been the staple for scientific imaging for decades.1 The addition of electron multiplication to CCDs also has extended their utility to applications where higher sensitivity is needed because of extremely low light conditions.2 CMOS sensors, which in the past have displayed limited performance, seem to have improved in quality recently, to the point of potentially competing with CCDs and electron-multiplying CCDs (EMCCDs) in the scientific imaging arena.

Here we investigate scientific-grade CMOS and EMCCD performance under low-light microscopy conditions by comparing signal-to-noise performance for the two under typical operating conditions. EMCCD sensors have the advantage over recently emerging scientific-grade CMOS sensors in low-light-microscopy imaging applications.

A useful attribute for evaluating sensor performance, particularly under low-light imaging conditions where the sensor may be operating near its detection limit, is signal-to-noise ratio (SNR). SNR data has been graphed as a function of photons, as photons per square micron and as integration time and has shown EMCCDs as being more sensitive than scientific-grade CMOS sensors under low-light conditions.

Simplified comparison

A calculation of SNR can be made based on its definition (signal (S) divided by total noise), while using the variables of signal (incident photon number multiplied by the quantum efficiency (QE) of the device), read noise (RN), dark noise (DN) and shot noise (SN). Equation 1 uses these variables in the standard signal-to-noise equation.

The equation also contains variables that are present in both CCDs and scientific-grade CMOS cameras and, thus, can be applied to both. However, for an EMCCD sensor, another noise source must be taken into account. Electron multiplication, the EMCCD’s major advantage, dramatically improves its already low read noise, but noise in the electron multiplication gain contributes an excess noise factor (ENF) of ;1.4, as shown in Equation 2.2

Standard scientific-grade CCD cameras exhibit Gaussian noise. However, spurious events also can occur in EMCCDs and can slightly skew the noise. These events are small enough to be typically ignored in SNR equations but, nonetheless, were taken into account in this paper’s SNR calculations (as part of the dark noise component).


Scientific-grade CMOS sensors also display additional random telegraph noise, which significantly skews the noise distribution to higher values.3 As a result, the skewed noise distribution of a CMOS sensor can have as many as a third of its pixels outside the normal Gaussian distribution. However, to simplify calculations, a normal Gaussian noise distribution was assumed for the scientific-grade CMOS calculations in this article. Consequently, the scientific-grade CMOS noise would be higher, and its SNR values would be even worse, in reality.

That being said, because signal is dependent upon the number of photons, a range of SNR values can be calculated as the number of photons added to a detector is incrementally increased. Using EMCCD attributes from Photometrics and scientific-grade CMOS data from marketing literature, such calculations were made, and the values graphed in Figure 1, which demonstrates that the EMCCD outperforms the scientific-grade CMOS in terms of SNR at the lowest light levels, up to a crossover point at ;10 photons.


Figure 1.
Oversimplified graph comparing EMCCD and scientific-grade CMOS for various numbers of incoming photons. Signal-to-noise ratios were calculated using Equations 1 and 2 and combined technical values from marketing literature and Photometrics EMCCD datasheets. The EMCCD shows higher performance over the CMOS at extremely low light levels up to a 10-photon crossover. Note: Dark current was assumed as zero for both, as integration time has not been taken into account. Images courtesy of Photometrics.


Camera and sensor manufacturers often use data such as the SNR versus the number of photons to compare camera performance. However, there is a significant problem in using the number of photons (a relatively abstract variable) when it comes to comparing data for microscopy applications. The graph in Figure 1 allows comparison of one sensor with another if it is assumed that the number of photons hitting each pixel would be the same for both detectors.

However, in the arena of light microscopy imaging, this is simply not the case (Figure 2); e.g., EMCCD and scientific-grade CMOS sensors have different pixel sizes, and an image projected equally upon both sensors would project various numbers of photons per pixel for each camera. In other words, each smaller 6.5-μm scientific-grade CMOS pixel would capture a smaller fraction of an image than each larger EMCCD pixel. The effect is demonstrated in Figure 2. The smaller scientific-grade CMOS pixels thus would each receive fewer incident photons and have a resulting lower signal than the EMCCD. When such data is input into the standard signal-to-noise equation, the lower signal also produces a lower SNR.


Figure 2.
Comparison of light captured using EMCCD and scientific-grade CMOS sensors. Both sensors are attached to the same microscope, projecting the same image size on both sensors. However, the amount of light captured per pixel will be less for the scientific-grade CMOS with smaller pixels, compared with the EMCCD with larger pixels.


A more accurate graph of SNR data would take the pixel area into account when calculating SNR. To do this, the number of photons per square micron – i.e., photon density – can be used. Photons per square micron is a measure of how the light image is spread as it is projected onto a microscope’s imaging port. It should be identical to whichever sensor is attached to that microscope’s imaging port, assuming identical optics, which is usually the case in experimental light microscopy.

The number of photons hitting a pixel now can be calculated by multiplying the photons per square micron by the square micron pixel area in a sensor. That number then can be used to calculate the signal and the SNR, as shown in Figure 3. Note that this more realistic consideration of the effect of pixel size eliminates the crossover completely, with the EMCCD showing a higher SNR at all photon numbers.


Figure 3.
Graph comparing scientific-grade CMOS and EMCCD sensors as a function of incident photons per square micron. Specifications from scientific-grade CMOS marketing literature and EMCCD datasheets were used in the calculations. Graphing versus photons per square micron accounts for increased signal collection with increasing pixel size. Accordingly, all three EMCCD sensors show higher performance than the scientific-grade CMOS sensor.


This more experimentally applicable parameter – photon density (used in Figure 3) – is nonetheless still essentially fixed for a given optical system in terms of collection efficiency, magnification and throughput of the optics used because these parameters are not easily adjusted without changing the optical system itself.

Illumination source intensity is potentially adjustable, but adjustment can be difficult and generally only unidirectional – reduction from a maximized intensity. Therefore, after accounting for actual pixel size and appropriating the correct number of photons per pixel, the next real parameter that researchers would typically use to optimize their images would be exposure time. Furthermore, when such a comparison is made in terms of SNR, it is also important to take dark current into account and thus use all parameters in SNR Equation 1.

Under realistic conditions

For a true comparison between scientific-grade CMOS and EMCCD technologies, more realistic experimental conditions must be considered. In the following study, integration time, a parameter commonly adjusted in experimental applications to optimize signal, is used. To translate this parameter into a signal and then an SNR, other variables are assumed in a realistic system.

In this case, the fluorescence from a sample of enhanced cyan fluorescent protein (ECFP) is calculated as it is collected through a 60x objective and projected onto a scientific-grade CMOS and three Photometrics EMCCDs (back-illuminated Evolve 512 and Cascade II 1024 and the front-illuminated Cascade 1K). Note that the Evolve 512 and Cascade II 1024 EMCCDs have QEs greater than 90 percent, while the Cascade 1K’s QE is only 62 percent.4,5,6

ECFP’s molecular brightness, the collection solid angle of the objective, the optical throughput, the pixel size and other realistic parameters are used to calculate signal and then SNR as a function of integration time. A comparison of the three Photometrics EMCCDs with the scientific-grade CMOS in rolling- and global-shutter-readout modes is graphed in Figure 4, which shows that the scientific-grade CMOS sensor’s SNR numbers never would reach the two back-illuminated EMCCDs’ SNR values, even at extensive integration times. This difference is the result of the superior dark current, the read noise and the quantum efficiency, as well as the larger pixel size of the EMCCDs. Also, the Cascade 1K shows favorable results compared with the scientific-grade CMOS, with a higher SNR over the entire region. And, as a result of its associated large dark noise, the global shutter mode of the scientific-grade CMOS produces a tremendously lower SNR than all three EMCCDs, over all exposure times.


Figure 4.
Graphed SNRs as a function of integration time for scientific-grade CMOS and three Photometrics EMCCDs. The SNR values were calculated based on scientific-grade CMOS literature and on Evolve 512, Cascade II 1024 and Cascade 1K EMCCD datasheets.4,5,6 The scientific-grade CMOS operated in either mode shows lower performance than even that of the front-illuminated Cascade 1K EMCCD.

Microscopic Imaging

One final comparison of EMCCDs to scientific-grade CMOS sensors is made in Figure 5. Here, a deeply cooled scientific-grade CMOS with lower dark current is compared with the same three Photometrics EMCCDs and the uncooled scientific-grade CMOS used in Figure 4. The results are similar to those in Figure 4, again showing higher SNR values for the EMCCDs than the cooled and uncooled scientific-grade CMOS sensors.


Figure 5.
Graph comparing deeply cooled and uncooled scientific-grade CMOS sensors and three Photometrics EMCCD sensors versus integration time. SNRs were calculated using marketing literature and EMCCD datasheets. The cooled scientific-grade CMOS shows similar numbers to the uncooled scientific-grade CMOS because its lower dark noise is offset by its slightly higher read noise. The EMCCDs outperform both in rapid imaging.


The reason for the similar results is that, although the deeply cooled scientific-grade CMOS sensor has associated noise and a lower dark current than the scientific-grade CMOS sensor, its read noise is slightly higher, producing nearly overlapping curves for the two scientific-grade CMOS sensors. Indeed, the uncooled scientific-grade CMOS actually shows slightly improved performance compared with the deeply cooled scientific-grade CMOS at shorter integration times – the result of having a lower quoted read noise than the cooled one. Therefore, in the low-light-imaging regime, the cooled and uncooled CMOS sensors deliver similar performance, which is below that of the three EMCCDs studied.

Visualizing SNR differences

Although Figures 3, 4 and 5 demonstrate the improved performance of EMCCDs over scientific-grade CMOS sensors under low-light conditions, it is easier to visualize the advantage by comparing biological images whose quality reflects their differing SNRs. Figure 6 shows images of fluorescently labeled bovine pulmonary artery endothelial cells that would be seen using the four cameras graphed in Figure 5 at 46-ms exposures and their associated SNR values after normalized scaling and cropping to the same view. A comparison of the images shows the improved appearance that accompanies the higher SNR values achieved by EMCCDs over the scientific-grade CMOS at low light.


Figure 6.
Comparison of biological images that would be visualized with three EMCCDs and a scientific-grade CMOS sensor with given SNRs. It is apparent that, under these low-light imaging conditions, EMCCD images would have higher SNRs, with higher quality and easier visualization. At higher light, the difference between Evolve 512 and CMOS images would be even greater. (See Figure 4.) Front-illuminated EMCCDs with midrange quantum efficiency, such as the Cascade 1K, and high-quantum-efficiency back-illuminated EMCCDs, such as the Evolve 512 and Cascade II 1024, all outperformed the cooled and uncooled scientific-grade CMOS sensors under low-light experimental circumstances. The data even indicate little real advantage from cooling the sCMOS cameras.


The advantage of the EMCCDs’ higher SNR values over the scientific-grade CMOS was further illustrated by an associated improvement in image appearance. It is evident through this study that in choosing an optimum sensor for an application, how the sensors would perform within the optical system under realistic experimental conditions should be considered.

Meet the authors

James R. Joubert is an applications scientist at Photometrics in Tucson, Ariz.; e-mail: jjoubert@photometrics.com. Deepak K. Sharma is director of product management at Photometrics; e-mail: dsharma@photometrics.com.

References

1. J.R. Janesick (2001). Scientific charge-coupled devices. SPIE Press, Bellingham, Wash., pp. 3-22.

2. J.B. Pawley (2006). Handbook of biological confocal microscopy, 3rd ed. Springer, New York, pp. 76-79.

3. P. Martin-Gonthier et al (2010). Custom transistor layout design techniques for random telegraph signal noise reduction in CMOS image sensors. Electronics Letters, Vol. 46, pp. 1323-1324.

4. Photometrics Evolve 512 datasheet (http://www.photometrics.com/products/datasheets/evolve_512.pdf).

5. Photometrics Cascade II 1024 datasheet (http://www.photometrics.com/products/datasheets/CasII1024.pdf).

6. Photometrics Cascade 1K datasheet (http://www.photometrics.com/products/datasheets/1K.pdf).


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