Introduction to Digital Camera Technology

Independent of the type of sensor one can consider the pixel as the basic unit. The main element of a pixel in turn is the photo-sensitive photodiode with silicon coupled to an electron storage well (Fig. 1). Silicon is responsible for generating the electrons which then can be collected, moved, and finally converted into a digital signal. Additional components of a pixel include electrical control circuits and pigment layers to exclude unwanted or destructive wavelengths.

Unfortunately noise is a fundamental of physics that affects all signals. The impact and the type of dominant noise vary for different sensor types. Generally one can classify camera noise into three major classes according to their source:

Dark noise - also known as dark current - is a fundamental noise present in the sensor. Dark noise is caused by thermal energy in the silicon randomly generating electrons in pixels. Dark noise builds up in pixels with exposure time. It is expressed in electrons per pixel per second (e-/px/sec). It is less of a concern for fast applications with short exposure times. When it comes to long exposure times e.g. one second or more for weak fluorescent signals, this noise type can become a major issue. Dark noise is reduced by cooling the sensor, halving the dark current with every 8 degrees of cooling (Fig. 3).

Finally it is important to mention that optical noise from the sample, auto fluorescence or poor staining is often the dominant noise source in an image. The use of advanced sensors cannot help you to overcome a poorly prepared sample.

The full-well capacity is largely dependent on the physical size of the pixel. It refers to the charge storage capacity of a single pixel. This is the maximum number of electrons it can collect before saturation. Reaching the full-well capacity can be compared to a bucket filled with water (Fig. 5).

A characteristic directly connected to full-well capacity is the dynamic range. This describes the sensor’s ability to simultaneously record low and high intensity signals. In a practical sense this means that the weaker signal is not lost in the noise and the brightest signal does not saturate the sensor. Expressed in a mathematical term the dynamic range is defined as the full-well capacity (FWC) divided by the camera noise.

It is often described in decibel units (dB):

The dynamic range improves if the full-well capacity is higher and the camera noise is lower. In a first approximation one can say, that the following parameters therefore affect the dynamic range:

• Pixel size (Full-well capacity)
• Temperature (Dark noise)
• Readout rate (Readout noise)

For fluorescence applications a large dynamic range is a major benefit to document bright fluorescence signals against a dark background (Fig. 7), especially when quantifying signals.

In an ideal world one would assume that 100 photons are capable to generate 100 electrons. When interacting with a sensor, photons may be absorbed, reflected or even pass straight through. The ability of a sensor to absorb and convert light of a certain wavelength into electrons is known as its quantum efficiency (QE).

The quantum efficiency of a sensor is affected by a number of factors including:

• Fill factor
• Addition / performance of microlenses
• Anti-reflective coating
• Sensor format (back- or front-illuminated)

The quantum efficiency is always a function of the wavelength of the incoming light. Silicon detectors most commonly used in scientific imaging are able to detect wavelengths just beyond the range of visible light (~400 to 1000 nm). By looking at a QE curve you can see how efficient a particular sensor is at converting a particular wavelength into signal (Fig. 9).

The majority of camera sensors are front-illuminated where incident light enters from the front of the pixel, having to pass semi-opaque layers containing the pixels circuitry, before hitting the photo-sensitive silicon (Fig. 1). These layers cause some light loss, so front-illuminated sensors typically have maximum QE’s around 50-60%. As the electronics on the surface of sensors are only able to generate a localized electrical field they’re not able to manipulate the charge that forms deeper in the silicon wafer (Fig. 10).

In the case of a back-illuminated sensor, light directly hits the photo-sensitive silicon from the “back” without having to pass through the pixel circuitry, offering maximum QE’s approaching 95%. To manufacture back-illuminated sensors, also known as back-thinned, this additional silicon is ground away - an expensive process - to create an incredibly thin silicon layer where all of the charge can be manipulated by the pixels’ electronics.

The imaging speed of a digital camera is measured in frame rates indicated as frames per second (fps). This is the number of images (frames) the camera can acquire in one second. Many factors can affect the maximum achievable frame rate of a camera. At a given exposure time the following parameters need to be considered:

• Pixel count
• Pixel readout rate
• Computer interface (USB 2.0/ USB 3.0/ CamLink etc.)

The easiest way to increase frame rates is by reducing the number of pixels being read out by switching to a smaller region of interest (ROI). As the frame rates increase, the number of photons hitting the sensor will decrease, so depending on the sample type there comes a point at which additional sensitivity is required. One trick that can be employed to boost speed and reduce noise is “on-chip binning”.

During binning instead of reading out the data from every pixel separately, the data from several adjacent pixels is combined on chip in the serial register and read out as a “Super-Pixel”. In this manner the data from 2x2, 3x3 or 4x4 and more pixels can be combined (Fig. 12).

Most of the above described features and parameters are generic for all types of imaging sensors in microscopy. However - based on historical developments and technical improvements the microscopist can select between different types of sensors and camera, respectively. They differ in the principle architecture (e.g. CCD vs. CMOS), ability to enhance signals (e.g. EMCCDs vs. CCDs) and quality of images (e.g. CMOS vs. sCMOS).

CCD sensors - Charged Coupled Device: Cameras based on this sensor type are the workhorses for brightfield and fluorescence imaging. Characteristically the charge generated in the pixels is moved over from one pixel to the other across the surface into the serial register (Fig. 14). From the serial register charges are passed one by one to the read out electronics where the signal is converted into a voltage, amplified, quantified, and digitized. So all the data within a CCD sensor is usually read out through a single output node.

EMCCD sensors - Electron Multiplying CCD: EMCCD sensors are basically CCD sensors with the addition of an EM gain register between the sensor and the read out electronics. The EM gain register amplifies the signal before it encounters the read out electronics. In addition to this EMCCD cameras employ back thinned sensor technologies typical offering peak QE >90%. These types of cameras are used for extreme low light applications and can be single photon sensitive. The price of these cameras is typically significantly higher than for regular CCD-based cameras.

CMOS - Complementary Metal Oxide Semiconductor: Originally used in cell phones and low end cameras, CMOS technology has improved significantly in recent years and has become an important imaging device for standard brightfield application in microscopy. The major difference compared to CCDs is the intra pixel electronics and time saving sensor read out principle with thousands of read out nodes compared to the single read out node used in traditional CCD sensors.

sCMOS - scientific CMOS: Introduced a couple of years ago this type of sensor overcomes common drawbacks of CMOS sensors like high noise level. This type of sensor is used for high-end fluorescence imaging which benefits from the fast frame rates, high dynamic range and low noise.