Solid-State Imaging Device PUF Key Generation
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Solution Overview
Problem
Conventional solid-state imaging devices lack effective methods to generate true random numbers with high randomness at a low cost, which is essential for securely preventing image falsification and forgery, especially in IoT applications where data integrity is critical.
Innovation Solution
A solid-state imaging device incorporating a fuzzy extractor with a true random number generator that utilizes pixel and reading part variations to generate a unique key, allowing for high randomness at a low cost by processing column output signals and eliminating errors in key regeneration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encryption techniques are used to protect digital signals, then security protection is provided, but immediate signals from sensor chips remain unprotected and additional circuits are required
Solution Approach 1:
The patent combines the PUF function with the existing pixel array structure of the CMOS image sensor. The pixel transistors themselves are utilized to generate unique identification information through their inherent manufacturing variations, merging the security function with the imaging function without requiring separate security circuits.
Solution Approach 2:
The pixel transistors in the CMOS image sensor serve dual purposes: they perform their primary function of converting light to electrical signals for imaging, and simultaneously function as the basis for generating unique security identifiers through their manufacturing variations. This multi-functionality eliminates the need for dedicated security circuits.
2Device complexity
If PUF technology is implemented using pixel variations, then unique device identification is achieved without extra circuits, but errors may occur in bit determination due to environmental conditions
Solution Approach 1:
The patent performs preliminary measurements of pixel transistor characteristics under multiple different environmental conditions (temperature, voltage, noise) before final bit determination. By collecting data in advance under varying conditions and analyzing the stability of threshold voltage relationships, the system can identify bits that remain consistent despite environmental changes, thereby improving reliability.
3Reliability
If multiple measurements are performed under different environmental conditions, then reliability of bit determination is improved, but measurement time and processing complexity increase
Solution Approach 1:
The patent performs measurements under multiple environmental conditions (excessive action) to ensure high reliability of bit determination. By deliberately measuring under more conditions than the minimum single condition, the system gathers sufficient data to identify stable bits that withstand environmental variations, accepting the time cost as necessary for achieving high reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables reliable prevention of image falsification and forgery by generating true random numbers with high randomness at a low cost, enhancing security in IoT applications without the need for additional circuits.
Implementation Method 1
a true random number generator which generates a true random number using a read-out signal read from a column signal processing part
Data Source
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AI summary
A fuzzy extractor includes an initial key generating part including a true random number generator, and a key regenerating part. The true random number generator generates a true random number using a read-out signal read from the reading part or a pixel signal read from the pixels of the pixel part in a true random number generation mode. The initial key generating part generates helper data and an initial key based on the true random number generated by the true random number generator and variation information acquired as a response when the initial key is generated. The key regenerating part generates, when a key is regenerated, a unique key based on helper data acquired when the initial key is generated and variation information acquired as a response including an error when the key is regenerated.