A 4-nm 1.15 TB/s HBM3 Interface With Resistor-Tuned Offset Calibration and In Situ Margin Detection
This article presents a high-speed all-digital third-generation high-bandwidth memory (HBM3) interface that achieves reliable memory access at a rate of 9.0 Gb/s/pin at 0.66 and 0.30 V supply voltages. To enhance the access reliability, the interface uses resistor-tuned offset calibration and in sit...
Gespeichert in:
Veröffentlicht in: | IEEE journal of solid-state circuits 2024-01, Vol.59 (1), p.1-12 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | This article presents a high-speed all-digital third-generation high-bandwidth memory (HBM3) interface that achieves reliable memory access at a rate of 9.0 Gb/s/pin at 0.66 and 0.30 V supply voltages. To enhance the access reliability, the interface uses resistor-tuned offset calibration and in situ margin detection techniques; furthermore, a supply noise adaptation algorithm, coupled with a high-accuracy digital delay sensor, significantly enhances voltage stability and mitigates the degradation of the valid window margin (VWM) under supply voltage variations. Additionally, the use of stacked-I/O and folded-PHY concepts in the HBM3 interface results in an optimal area, enabling seamless alignment with the HBM3 channel structure and effectively minimizing the length of the channels. To demonstrate the effectiveness of the suggested interface, a HBM3 system was implemented with a 4-nm fin field-effect transistor (FinFET) technology. This implementation showcases the outstanding energy efficiency of the HBM3 interface, 0.29 pJ/bit, with an improved supply of noise-tolerance while occupying a small area. This work highlights the promising potential of the proposed all-digital HBM3 interface for enabling high-speed memory access in high-performance computing (HPC)/artificial intelligence (AI) computing systems. |
---|---|
ISSN: | 0018-9200 1558-173X |
DOI: | 10.1109/JSSC.2023.3330485 |