Kuo-Yu Chou

A leading-edge 0.9µm pixel CMOS image sensor technology with backside illumination: Future challenges for pixel scaling

This paper presents process breakthroughs that enable a BSI 0.9µm pixel formation and its performance. The technology was developed using 300mm bulk silicon starting wafers with the state-of-the-art tool set for BSI sensor processing. This is the first demonstration of 0.9µm BSI pixel with acceptable optical performance. Further improvements are in the area of crosstalk suppression and color performance enhancement for continuous pixel scaling from 0.9µm.

A 0.66e − rms temporal-readout-noise 3D-stacked CMOS image sensor with conditional correlated multiple sampling (CCMS) technique

A conditional correlated multiple sampling (CCMS) technique for low noise CMOS image sensor (CIS) is proposed to reduce noise and address low frame rate issue caused by the conventional correlated multiple sampling (CMS) technique. An 8Mpixel 3D-stacked CIS with 1.1um pixel pitch is designed and verified. Measurement results show this technique can achieve 0.66e-rms at 36.1 kHz A/D sampling rate per pixel with analog gain at 16 and 5-times multiple sampling. The resulting DNL is within -0.49/+0.45LSB.

Extraction and Estimation of Pinned Photodiode Capacitance in CMOS Image Sensors

The pinned photodiode capacitance extraction method proposed by Goiffon et al. is discussed, and two additional new methods are presented and analyzed; one based on the full well dependence on photon flux and the other based on the full well dependence on transfer-gate off-voltage.

CMOS Image Sensor Random Telegraph Noise Time Constant Extraction From Correlated To Uncorrelated Double Sampling

A new method for on-chip random telegraph noise (RTN) characteristic time constant extraction using the double sampling circuit in an 8.3 Mpixel CMOS image sensor is described. The dependence of the measured RTN on the time difference between the double sampling and the key equation used for time constant extraction are derived from the continuous time RTN model and the discrete event RTN model. Both approaches lead to the same result and describe the data reasonably well. From the detailed study of the noisiest 1000 pixels, we find that about 75% to 85% of them show the signature of a single-trap RTN behavior with three distinct signal levels, and about 96% of the characteristic time constants fall between 1 μs and 500 μs with the median around 10 μs at room temperature.

A 45 nm Stacked CMOS Image Sensor Process Technology for Submicron Pixel

A submicron pixel’s light and dark performance were studied by experiment and simulation. An advanced node technology incorporated with a stacked CMOS image sensor (CIS) is promising in that it may enhance performance. In this work, we demonstrated a low dark current of 3.2 e−/s at 60 °C, an ultra-low read noise of 0.90 e−·rms, a high full well capacity (FWC) of 4100 e−, and blooming of 0.5% in 0.9 μm pixels with a pixel supply voltage of 2.8 V. In addition, the simulation study result of 0.8 μm pixels is discussed.

Statistical Analysis of the Random Telegraph Noise in a 1.1 μm Pixel, 8.3 MP CMOS Image Sensor Using On-Chip Time Constant Extraction Method

A study of the random telegraph noise (RTN) of a 1.1 μm pitch, 8.3 Mpixel CMOS image sensor (CIS) fabricated in a 45 nm backside-illumination (BSI) technology is presented in this paper. A noise decomposition scheme is used to pinpoint the noise source. The long tail of the random noise (RN) distribution is directly linked to the RTN from the pixel source follower (SF). The full 8.3 Mpixels are classified into four categories according to the observed RTN histogram peaks. A theoretical formula describing the RTN as a function of the time difference between the two phases of the correlated double sampling (CDS) is derived and validated by measured data. An on-chip time constant extraction method is developed and applied to the RTN analysis. The effects of readout circuit bandwidth on the settling ratios of the RTN histograms are investigated and successfully accounted for in a simulation using a RTN behavior model.