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Noise Analysis & Image Sensor Fabrication

Source:Shenzhen Kai Mo Rui Electronic Technology Co. LTD2026-07-09

What are 1/f Noise and Random Telegraph Noise? Why are measurements of these two noise types critical during image sensor production?

 

1/f noise, also referred to as pink noise or flicker noise, is a noise signal whose power spectral density is inversely proportional to frequency, hence the notation 1/f.

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As illustrated in the corresponding diagram, 1/f noise attenuates as frequency rises. Within a finite bandwidth, 1/f noise decays by 3 dB per octave, meaning it is barely observable in the high-frequency range. By contrast, white noise carries identical energy per hertz across the entire frequency spectrum.

 

Accordingly, 1/f noise is classified as low-frequency noise. From the perspective of human visual perception and image quality, low-frequency noise is highly perceptible and detrimental to viewing experience. In comparison, high-frequency noise can marginally enhance perceived sharpness. For this reason, image sensor manufacturers must rigorously suppress 1/f noise during design and production.

 

Random Telegraph Noise, formally named Random Telegraph Signal (RTS) Noise, features signal waveforms that toggle between distinct discrete levels over the time axis, resembling traditional telegraph pulses, thus earning its name.

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Unlike uniform noise distributed evenly across every pixel, RTS noise varies drastically pixel by pixel, introducing severe spatial non-uniformity that severely impairs image quality.

 

The attached figure demonstrates that pixels 1, 2 and 3 display noticeable brightness discrepancies induced by RTS noise of different amplitudes.

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RTS noise is tightly coupled to semiconductor fabrication processes: pixels with microscopic process defects will exhibit elevated RTS noise levels.
Pixel 3 shows distinctly different temporal signal fluctuation compared with other pixels, mimicking the up-and-down switching pattern of telegraph signals.
Pixels contaminated with prominent RTS noise severely degrade final image output. During sensor characterization and testing, engineers need to quantify the total number of RTS-affected pixels, pinpoint their exact coordinates on the sensor array, and record their temporal fluctuation characteristics. These findings drive process optimization in image sensor manufacturing and effectively improve overall image performance of finished products.


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