An Embedded Architecture for DDR5 DFE Calibration Based on Channel Stimulus Inversion

The increase in performance promised by the recent generation of double data rate (DDR) memory, DDR5, is conditioned by addressing its signal integrity challenges. The DDR5 standard specifies a 4-tap decision feedback equalizer (DFE) at the memory receiver to deal with these challenges. Although adaptive equalization is a mature field, known methods for DFE calibration are limited by the DDR5 interface complexity and the equalization requirements mandated by its specification. In this article, we propose a novel approach based on linear inversion of channel stimulus that leverages specific architectural details of DDR5 and can tune memory devices deterministically at runtime. In addition to using few hardware resources relative to a modern memory controller, by operating at very low latency, this new approach facilitates periodic equalization when the DFE is offline, thus avoiding DFE error propagation during training inherent to adaptive techniques.

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Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2024-12-04
DOI
https://doi.org/10.1109/tvlsi.2024.3505835
Primary Topic
Advanced Electrical Measurement Techniques
Type
article
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article

An Embedded Architecture for DDR5 DFE Calibration Based on Channel Stimulus Inversion

Nicola Nicolici, Mitchell Cooke
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Advanced Electrical Measurement Techniques
article

An Embedded Architecture for DDR5 DFE Calibration Based on Channel Stimulus Inversion

Nicola Nicolici, Mitchell Cooke
article en

Abstract

The increase in performance promised by the recent generation of double data rate (DDR) memory, DDR5, is conditioned by addressing its signal integrity challenges. The DDR5 standard specifies a 4-tap decision feedback equalizer (DFE) at the memory receiver to deal with these challenges. Although adaptive equalization is a mature field, known methods for DFE calibration are limited by the DDR5 interface complexity and the equalization requirements mandated by its specification. In this article, we propose a novel approach based on linear inversion of channel stimulus that leverages specific architectural details of DDR5 and can tune memory devices deterministically at runtime. In addition to using few hardware resources relative to a modern memory controller, by operating at very low latency, this new approach facilitates periodic equalization when the DFE is offline, thus avoiding DFE error propagation during training inherent to adaptive techniques.

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(3)
McMaster University (CA)
Affordable and clean energy
Openalex Percentile: Top 80%
Advanced Electrical Measurement Techniques
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