PPubMed23 Nov 2017Cited 20×

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 †

Calvin Yi-Ping Chao, Honyih Tu, Thomas Meng-Hsiu Wu, Kuo-Yu Chou, Shang-Fu Yeh, Chin Yin, Chih-Lin Lee

Abstract

'Shang-Fu Yeh' 'Chin Yin' 'Chih-Lin Lee'] 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.

A figure from 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 † — fig. from the paper

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The study statistically analyzes RTN in a 1.1 µm, 8.3 MP CMOS image sensor, showing the pixel source follower is the dominant noise source and provides an on‑chip time‑constant extraction method.

What’s new

i.Established a noise source decomposition scheme to isolate RTN contributions
ii.Demonstrated that the pixel source follower is the primary source of RTN
iii.Derived an analytic formula linking RTN behavior to correlated double sampling time differences
iv.Developed an on-chip method for extracting RTN emission and capture time constants
v.Performed pixel‑by‑pixel time‑constant extraction for the 1000 noisiest pixels

How they did it

The authors measured random noise histograms across the full 8.3‑MP array while varying the correlated double sampling (CDS) time difference from 0 to 25 µs. They applied a noise decomposition scheme to separate random noise from RTN and used an on‑chip extraction technique to obtain emission and capture time constants for individual pixels. The results were validated with simulations based on the derived analytical formula.

What they found

Long‑tail noise distribution reaches up to ~27 e⁻ rms, dominated by RTN
>95% of pixels show a single‑peak low‑noise histogram; 2‑ or 3‑peak pixels dominate the high‑noise region (RN > 10 e⁻ rms)
Extracted RTN time constants span 1 µs to 500 µs
Among the 1000 noisiest pixels, 80% exhibit 3 peaks and 15% exhibit 1 peak
Read noise at 8X gain is <2 e⁻ rms (Table 1)

Caveats

✕Physical locations and energy levels of individual traps are not fully understood
✕Switched‑biasing techniques to reduce RTN are not universally effective across all traps
✕Further investigation needed on how trap characteristics vary with temperature and device scaling
✕Comparison of RTN performance across different technology nodes remains an open study

in plain words

Random telegraph noise is like a tiny flickering that shows up in some pixels of a camera sensor. The researchers looked at millions of pixels, found that the flicker mainly comes from a tiny transistor called the source follower, and created a way to measure how fast each flicker switches on and off. This helps make clearer pictures, especially as sensors get smaller.

why it matters

Understanding and mitigating RTN is crucial for improving image quality in modern high‑resolution sensors, especially as pixel sizes shrink and applications demand low‑noise performance across many fields such as photography, scientific imaging, and autonomous systems.

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CMOS image sensor random telegraph noise correlated double sampling time constant extraction backside‑illuminated technology

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