Memory Receiver Background Training Equalization
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Solution Overview
Problem
The increasing data rate in semiconductor memory devices leads to signal degradation due to interference, requiring an optimized equalization coefficient for proper signal restoration, but existing methods often necessitate separate training modes and sequences, which can degrade system performance.
Innovation Solution
A receiver with a flag generator, equalizer, and equalization controller performs background self-training using normal write data to update the equalization coefficient in real time, optimizing it without external control or separate training sequences, thus minimizing performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate training mode and sequence are used to optimize the equalization coefficient, then the signal characteristics can be improved, but the system performance is degraded due to the additional training overhead
Solution Approach 1:
The patent merges the training operation with the normal data operation by performing background training simultaneously with data reception. The receiver processes normal data signals while concurrently updating the equalization coefficient through training operations, eliminating the need for separate training modes and sequences.
Solution Approach 2:
The training operation continues continuously during normal data reception rather than being performed in a separate discrete phase. The equalization coefficient is updated in real-time as data is received, maintaining continuous optimization without interrupting or pausing the data transfer process.
2Measurement precision
If the equalization coefficient is updated using traditional training methods, then the equalization accuracy can be improved, but the operation complexity increases due to external control requirements
Solution Approach 1:
The receiver performs self-training by automatically updating its own equalization coefficient without external control. The training operation is initiated and executed internally by the receiver using the received data signals, eliminating the need for external training sequences and control mechanisms.
Solution Approach 2:
Instead of using separate training sequences to optimize equalization, the patent inverts the approach by using normal data reception itself as the training medium. The received data signals serve dual purposes: as actual data to be processed and as training signals to optimize the equalization coefficient.
3Productivity
If high communication speed is used to increase data exchange, then the productivity is improved, but the signal quality is degraded due to channel distortion
Solution Approach 1:
The receiver implements feedback by continuously monitoring the received signal quality and using this information to adjust the equalization coefficient. The training operation uses the actual received signals to generate feedback that optimizes the equalization parameters, creating a closed-loop system that adapts to channel conditions.
Solution Approach 2:
The equalization coefficient is made dynamic and adaptable rather than fixed. The coefficient is continuously updated in real-time based on the received data signals and channel conditions, allowing the system to adapt to changing communication environments and maintain signal quality at high speeds.
Data Source
AI summary
A receiver included in a memory device includes a flag generator circuit, an equalizer circuit and an equalization controller circuit. The flag generator circuit is configured to, during a normal operation mode, generates a flag signal without an external command. The equalizer circuit is configured to, during the normal operation mode, receive an input data signal through a channel, generate an equalized signal by equalizing the input data signal based on an equalization coefficient, and generate a data sample signal including a plurality of data bits based on the equalized signal. The equalization controller circuit is configured to, during the normal operation mode, determine an amount of change in the equalization coefficient based on the flag signal, the equalized signal and the data sample signal, and perform a training operation in which the equalization coefficient is updated in real time based on the amount of change in the equalization coefficient.