Some assume that the famous "Moore's Law" also applies to image sensors. Gordon Moore (founder of Fairchild Semiconductor, now part of Anson Mae) pointed out that the number of transistors on an integrated circuit (IC) doubles every two years. In order to fit twice as many transistors onto a single device, shrinking the transistors is the main way to do it. This trend has been going on for decades, but the rate of increase in transistor numbers has slowed in recent years. The increase in transistor density also led to a decrease in the cost of the unit transistor, so many electronic systems became more and more versatile without rising in price.

 

But image sensors are different because important parts do not expand as transistors get smaller. Specifically, optical components of image sensors such as photodiodes (which convert incident light into an electrical signal) and some analog components (which convert an electrical signal into a digital image) cannot be amplified as easily as digital logic components. In sensors, image capture is primarily analog, with digital circuits converting digital data from each pixel into images that can be stored, displayed or used for AI machine vision.

 

If the number of pixels doubles every two years while the lens size remains the same, the pixels will get smaller, resulting in fewer photons being received. (Think of a rainy bucket. Smaller buckets collect fewer drops.) As a result, sensors will have to get better at unit area sensitivity and noise reduction to produce the same quality images in low-light conditions. Not only does it make no sense to increase the number of pixels unless it is necessary for the application scenario of the product, but it also forces the increase of bandwidth and storage space, which increases the cost of other parts of the system.

 

 

Pixel size alone is not enough to determine pixel performance. We can't assume that larger pixels necessarily lead to better image quality. Although pixel performance in different lighting conditions is important, and larger pixels give you more area to collect light, this does not necessarily improve image quality. Several other factors are also important, including resolution and pixel noise metrics.

 

If increasing resolution has more impact on the application than reducing a single pixel, then a sensor with a smaller pixel may outperform a sensor with a larger pixel for the same optical area. It is important to ensure that the number of photons received is sufficient to form a high quality image, so pixel sensitivity (photoelectric conversion efficiency) and the application environment are important.

 

Pixel size is a consideration when choosing a sensor for an application. But its importance can be overstated; It is only one of many parameters, and the others should be given the same serious consideration. When selecting a sensor, the designer must consider all the requirements of the target application and then find the ideal balance of speed, sensitivity, and image quality to achieve the right design solution.

 

 

In many applications, extending the dynamic range as far as possible is valuable to help render shadows and highlights correctly in the final image, but this can be challenging for image sensors. Some companies are using a technique called "size pixel" to solve the problem of creating more capacity for photodiodes, in order to collect electrons before the diodes "saturate".

 

In the size pixel method, the sensor area dedicated to a single pixel is divided into two parts: the larger photodiode covers most of the area, and the smaller photodiode uses the remaining area. Larger photodiodes collect more photons and saturate easily in bright lighting conditions. Smaller photodiodes can be exposed longer without saturation because of the smaller area available to collect photons. They can be visually likened to using buckets and water bottles to collect raindrops. Buckets are usually wider at the top than at the bottom, so they collect raindrops very efficiently and fill up faster than water bottles. Water bottles, on the other hand, have a small opening and a wider body, making them slower to collect. Using larger pixels in low light conditions and smaller pixels in bright light conditions results in extended dynamic range.

 

                                                                        

 

ON Semiconductor solves this problem by adding an area to a single pixel into which the extra signal or charge can spill. Imagine we use a bucket to catch raindrops, and then have a larger basin to catch the overflow from the bucket. The "bucket" signal is easy to read and has high accuracy, so we can achieve good low light performance, and the larger basin contains all the signals that overflow, thus extending the dynamic range. In this way, the entire pixel area is used for low light conditions and does not saturate under bright light conditions. Saturation can reduce image quality, for example, color distortion and sharpness. Anson Beauty's Super exposure technology provides better image quality in high dynamic range scenes and is suitable for human vision and machine vision applications.