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Record W3199475090 · doi:10.1109/jssc.2021.3109167

A 112-Gb/s PAM-4 Low-Power Nine-Tap Sliding-Block DFE in a 7-nm FinFET Wireline Receiver

2021· article· en· W3199475090 on OpenAlex
James Bailey, Hossein Shakiba, Ehud Nir, Grigory Marderfeld, Peter Krotnev, Marc-Andre LaCroix, David Cassan, Davide Tonietto

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsWirelineComputer scienceDigital signal processingDigital subscriber lineElectronic engineeringComputer hardwareEngineeringTelecommunicationsWireless

Abstract

fetched live from OpenAlex

Practical realization of decision feedback equalizers (DFEs) has to date been limited to at most two taps in 100-Gb/s long-reach (LR) wireline applications due to significant power, area, and timing costs. This article presents a systolic many-tap low-complexity sliding-block decision feedback equalizer (SB-DFE) that overcomes the implementation challenges of conventional DFEs with no performance loss. A nine-tap configuration is demonstrated in a 112-Gb/s analog-to-digital converter (ADC)-digital signal processing (DSP) four-level pulse amplitude modulation (PAM-4) LR wireline receiver implemented in 7-nm FinFET. The architecture partitions the received signal into overlapping but computationally independent blocks thereby breaking the feedback loop of the DFE and allowing logic pipelining. Unlike existing feedback-breaking techniques, the computational overhead of the SB-DFE can be made arbitrarily small for any tap count—indeed, we show the practicality of SB-DFE implementations exceeding 30 taps. Optimized pipeline cuts are employed to minimize the latency through the SB-DFE while maintaining timing margin. The nine-tap SB-DFE is paired with a five-precursor tap feedforward equalizer (FFE) and compared to a two-tap-DFE 15-tap-FFE reference DSP implemented in the same receiver. A bit error rate of 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−12</sup> is measured over a 36-dB loss channel—at least an order-of-magnitude reduction compared to the reference DSP. Power is reduced by 0.33 pJ/b. DSP gate area is reduced by 30%. Noise tolerance is improved by 0.2-mV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMS</sub> . Error-free operation is demonstrated on an RS(544,514) KP4 forward error correction (FEC)-encoded link even when the DFE tap values are manually stressed. Techniques for further reduction in complexity are described.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.252
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it