Multi-Dimensional Data Processor Marginalization Circuit
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Solution Overview
Problem
Current data processing systems face challenges in accurately correcting errors and characterizing device quality due to a small number of errors resulting from data corruption during transfer, which complicates adjustments and quality assessment.
Innovation Solution
The implementation of a data processing system that includes analog to digital converter circuits, a multi-dimensional system marginalization circuit applying a marginalization algorithm to digital samples, and a processing circuit using a multi-dimensional data processing algorithm to yield a data output, incorporating noise injection and equalization techniques to enhance error correction and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data processing algorithms are applied to recover originally written data, then data transfer reliability is improved, but the error rate becomes too small to accurately characterize device quality
Solution Approach 1:
The patent converts the harmful effect of data corruption into a beneficial testing mechanism by intentionally injecting controlled noise into the data stream. This artificial corruption creates measurable errors that allow accurate characterization of device quality and error correction capability, transforming the problem of too-few-errors into a useful test condition.
Solution Approach 2:
The patent changes the error rate parameter from its natural low state to an elevated state through controlled noise injection. By adjusting the amount and type of injected noise, the system can operate at different error rates to thoroughly characterize device performance under various stress conditions, enabling precise measurement of error correction capabilities.
2Measurement precision
If error correction adjustments are made to correct data errors, then data accuracy is improved, but it becomes difficult to characterize the quality of the device
Solution Approach 1:
The patent segments the error correction process into distinct functional blocks including analog-to-digital conversion, equalization, deinterleaving, Viterbi decoding, and LDPC decoding. This segmentation allows each component to be independently tested and characterized, simplifying the overall complexity by breaking down the error correction system into manageable, measurable units.
Solution Approach 2:
The patent introduces a noise injection mechanism as an intermediary between the data source and the error correction processing. This intermediary allows controlled introduction of errors that can be precisely measured and used to characterize device quality without requiring complex natural error scenarios, thereby simplifying the testing and characterization process.
3Measurement precision
If noise injection is used to raise the error rate to a meaningful level, then device quality characterization is improved, but system complexity increases
Solution Approach 1:
The patent merges the noise injection function with the existing data processing pipeline by integrating it into the equalization stage. The injected noise is combined with the equalized signal and then processed through the existing deinterleaver and decoder components, eliminating the need for separate parallel processing paths and reducing overall system complexity while still enabling accurate error rate measurement.
Data Source
AI summary
Systems, methods, devices, circuits for data processing, and more particularly to data processing including operational marginalization capability.