研究目的
To develop a new figure of merit (FoM) for comparing the power efficiency of different image sensors based on pixel array size, frame rate, and total power consumption, applicable to various read-out configurations.
研究成果
The new power efficiency FoM effectively normalizes power consumption using pixel array size, frame rate, and a nonlinear factor n=1.5, enabling comparison of CISs across different configurations. It highlights improvements in power efficiency over time, with key breakthroughs such as subranging ADCs, advanced technology use, multistage pipelined ADCs, and 3D-stacked CIS technology. The FoM serves as a valuable tool for driving future power efficiency enhancements in image sensor design.
研究不足
The FoM has limitations including: difficulty in modeling Single Slope (SS)-ADC due to complexity in estimating gate count and power-frequency relationship; challenges in modeling output buffer subcircuits dependent on external load capacitance; potential exclusion of off-chip circuit power consumption if not specified; and not accounting for power from Image Signal Processor blocks for higher-order image processing.
1：Experimental Design and Method Selection:
The methodology involves developing a theoretical model for a generic CMOS image sensor (CIS) with subcircuit blocks (e.g., CDS, ADC, OUT) and deriving a FoM that normalizes power consumption with respect to pixel array size, frame rate, and a nonlinear factor n. Analytical modeling is used to estimate power consumption for different subcircuits.
2：Sample Selection and Data Sources:
The study benchmarks CIS designs from published literature spanning 1999 to 2018, using specifications such as frame rate, pixel array dimensions, and power consumption data from these studies.
3：List of Experimental Equipment and Materials:
No specific equipment or materials are listed; the work is theoretical and based on analytical models and literature data.
4：Experimental Procedures and Operational Workflow:
Steps include modeling subcircuit blocks (e.g., deriving equations for power consumption), calculating FoM variations for different n values, and applying the FoM to historical CIS data to analyze trends and validate the model.
5：Data Analysis Methods:
Numerical calculations are performed to determine the optimal nonlinear factor n (found to be 1.5) by minimizing variation in FoM across different frame rates and image formats. Statistical analysis involves computing variation ranges and averages.
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