High dynamic range image sensor architectures

Digital photographers continuously demand more performance from their equipment. Digital camera performance is defined by a set of parameters including dynamic range, noise, frame rate, resolution, and color. Amongst these parameters dynamic range is becoming increasingly more important. This is true because the human eye typically has a wider dynamic range than a digital camera. In this paper we define dynamic range as the ratio of the maximum to the minimum signal that can be detected. At the heart of all digital cameras is either a CCD or a CMOS image sensor (CIS). The dynamic range of the sensor typically limits the dynamic range of the camera. In this paper we review five CIS architectures that are designed to improved dynamic range. We start by reviewing standard CCD and CIS architectures and then present a simple sensor model. Using this model we show how signal to noise ratio (SNR) can be used to evaluate different wide dynamic range (WDR) sensor architectures. Then we sequentially review five different wide dynamic range techniques. The first WDR technique is multiple gains, and the second technique is non-linear pixel response. The third technique is variable exposure, and the forth technique is well capacity recycling. The fifth and final technique is time to saturation. For each of these techniques we present the pixel level circuitry and its advantages and disadvantages. Furthermore, all of these techniques are compared based on SNR and implementation complex. We discuss how implementation complexity affects signal processing in a digital camera, and other parameters in the sensor such as quantum efficiency and read noise. We conclude with a few summary comments.