Soft Decision Error Correction for Noisy PUF Data Extraction
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Solution Overview
Problem
Physically unclonable functions (PUFs) face high error rates due to external variations like temperature and voltage, making existing error correction codes inadequate, especially in resource-constrained systems such as smart cards, where error rates of up to 25% are common.
Innovation Solution
A cryptographic system that combines a normalizer, hard decision decoder, reliability information extractor, data arranger, and soft decision decoder to extract a reliable data string from a noisy PUF, using error correcting codes to correct errors and improve decoding reliability through soft-decision error correction without requiring multiple power cycles or additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If powerful error correcting codes are used to correct high error rates in PUF data, then the reliability of data extraction is improved, but the device complexity and resource requirements increase
Solution Approach 1:
The error correction process is divided into multiple stages: first hard decision decoding to correct a portion of errors, then soft decision decoding to correct remaining errors. This segmentation allows the system to handle high error rates without requiring a single overly complex error correction code, thereby reducing overall device complexity while maintaining data extraction reliability.
Solution Approach 2:
The system performs hard decision decoding first to correct a sufficient portion of errors to make the data usable, then applies soft decision decoding as a supplementary step to correct remaining errors. This partial action approach ensures reliability without requiring the full complexity of powerful error correcting codes to handle all errors from the outset.
2Reliability
If multiple power cycles are used to generate soft data for error correction, then the reliability of decoding is improved, but the loss of time and reduced productivity occur
Solution Approach 1:
The system performs hard decision decoding first as a preliminary step to correct a portion of errors and generate intermediate corrected data. This preliminary action provides sufficient reliability for many applications without requiring the time-consuming multiple power cycles, thus reducing time loss while maintaining acceptable decoding reliability.
Solution Approach 2:
The system skips the time-consuming multiple power cycle process by using hard decision decoding to quickly correct errors. For applications where maximum reliability is not critical, this allows the system to rush through the error correction process rapidly, significantly reducing time loss and improving productivity.
3Device complexity
If hard decision decoding is used to correct errors in PUF data, then the device complexity is reduced, but the false rejection rate increases
Solution Approach 1:
The system merges hard decision decoding and soft decision decoding into a unified error correction pipeline. Hard decision decoding handles the bulk of error correction with simple logic, while soft decision decoding is applied subsequently to correct remaining errors and reduce the false rejection rate. This combination maintains relative simplicity while significantly improving reliability.
Solution Approach 2:
The hard decision decoding serves as an intermediary step that preprocesses the noisy PUF data before it undergoes soft decision decoding. This intermediary action reduces the error burden on the soft decision decoder, allowing it to operate more effectively with lower complexity while achieving lower false rejection rates.
Data Source
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Figure 2c~2d
AI summary
A cryptographic system for reproducibly establishing a reliable data string, such as a cryptographic key, from a noisy physically unclonable function (PUF, 110) is provided. The system comprises a hard decision decoder(150) to decode a first multiple of error correctable data words to obtain a second multiple of corrected and decoded data words and a reliability information extractor (180) to determine reliability information, e.g. soft decision information, that is indicative of a reliability of corrected and decoded data words. The system further comprises a soft decision decoder (160) configured to use the reliability information to decode at least one further correctable data word. Error correcting a PUF using reliability information decreases the false rejection rate.