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ORCA-Quest Questions & Answers
What makes the ORCA-Quest camera unique?
The ORCA-Quest quantitative CMOS (qCMOS) camera with photon number resolving functionality is the leap in scientific camera evolution that transforms imaging into imagining (Fig. 1). With ultra-quiet, highly refined electronics, this camera is more than an image capture device; it is a precision instrument that unlocks the ability to investigate new photonic questions because it offers the quality and quantitative performance to detect meaningful data previously lost in the noise. The ORCA-Quest is the world's first camera to incorporate the qCMOS image sensor and to be able to resolve the number of photoelectrons. It features extremely low noise, high quantum efficiency, large area, and high-speed readout.
Figure 1. ORCA-Quest quantitative CMOS (qCMOS) camera
What is photon number resolving?
First, let’s be clear: as with any digital imaging device, what is being detected and measured are photoelectrons. Resolving individual photo(electro)ns has primarily been the domain of point detectors such as photomultiplier tubes (PMTs) and avalanche photodiodes (APDs). Photon counting is a measurement technique that relies on the properties of these detectors to indicate whether a photon (or two) has been detected with some level of certainty. But they cannot “count” photons much beyond the threshold of a binary yes or no. In Photon Number Resolving Mode, the ORCA-Quest quantitative CMOS (qCMOS) camera outputs actual counts of photoelectrons per pixel up to a max of 200 photoelectrons (Fig. 2). Single-photon sensitivity in a camera is not achieved through a single specification. To count these photoelectrons, the camera noise must be sufficiently smaller than the amount of photoelectron signal. Conventional sCMOS cameras achieve a small readout noise, but still larger than the photoelectron signal, making it difficult to count photoelectrons.
Figure 2. Comparison of photon number resolving capability
What is camera noise?
Simplistically, read noise is pixel variation in the conversion of a charge to a digital signal. Each pixel’s photoelectron charge must be detected, converted to voltage, amplified, and digitized. Each of these steps has error associated with it. Read noise is specified as electrons rms to capture in one number the most meaningful spec. But in a camera with 9.4 megapixels, the pixel-to-pixel variation in read noise across the sensor and/or in a single pixel over time can impact image quality and data analysis.
The ORCA-Quest quantitative CMOS (qCMOS) camera pushes that boundary even further and, as can be seen in Fig. 3, has a very narrow read noise distribution, minimizing the salt and pepper visual effect of noisy pixels that can also wreak havoc on computational techniques such as precision localization super-resolution. To detect weak light with a high signal-to-noise ratio, every aspect of the ORCA-Quest, from its sensor structure to its electronics, has been optimally designed. Not only the camera development but also the custom sensor development has been done with the latest qCMOS technology, resulting in extremely low noise performance of 0.27 electrons rms.
Figure 3. Readout noise comparison
How does back-thinning work?
A traditional, front-illuminated digital camera is constructed in a fashion similar to the human eye, with a lens at the front and photodetectors at the back. This traditional orientation of the sensor places the active area of the digital camera image sensor on its front surface. The matrix and its wiring reflect a portion of the input photons, and the active area can only receive part of the incoming photons. The reflection reduces the signal that is available to be captured. A back-illuminated sensor contains the same elements, but arranges the wiring behind the active area by flipping the silicon wafer during manufacturing, then thinning its reverse side so that light can hit the active area without passing through the wiring layer. This change can improve the chance of an input photon being captured from about 60% to over 90%.
Back-thinning CCDs for enhanced quantum efficiency (QE) has been done for decades, and EM-CCDs are an example of back-thinned technology becoming commonplace. There are two tradeoffs around back-thinning that are often underestimated: etaloning and impaired resolution as measured by the modulation transfer function (MTF). As with any transformative new product, clever new features take the headlines. But often it is the minimization of age-old nagging issues that frees up the technology for greatness.
What is etaloning?
Etaloning is a phenomenon that occurs when the incident light interferes with the reflected light from the back surface of the silicon and causes varying sensitivity. This is dependent on both the spatial and the spectral. In the case of an EM-CCD camera, it appears as a fringe pattern even with uniform monochrome light input, mostly in the IR. The quantitative CMOS (qCMOS) camera shows minimal etaloning compared to EM-CCD cameras (Fig. 4).
Figure 4. Etaloning comparison
What is MTF?
The modulation transfer function (MTF) is a way to characterize resolution and performance. The MTF indicates how much of the object's contrast is captured in the image as a function of spatial frequency. Resolution is typically considered as the overall number of pixels and pixel size, but pixel structure can also play a role in functional resolution. Every pixel is expected to collect light only from an optically specified area. But if the incoming photons from that area create charge in an adjacent pixel, then there is crosstalk among the pixels and deterioration of resolution. By creating a deep trench isolation structure in the pixel design of the ORCA-Quest quantitative CMOS camera, crosstalk is minimized. This improvement is measured by calculating how many patterned lines of contrasting light and dark can be resolved in a given area. Compared to back-illuminated sCMOS and EM-CCD cameras, the ORCA-Quest shows noticeable improvement in MTF that will produce greater sharpness in images at all magnifications (Fig. 5).
Figure 5. MTF (modulation transfer function) comparison
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Meet the engineer
Brad Coyle has been working in imaging for over 15 years. He joined Hamamatsu 6 years ago and is currently an OEM Camera Product Manager. His expertise includes camera and sensor technologies, and advanced imaging applications. In his spare time, he enjoys playing soccer, cooking, and hiking with his family.
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