Evidence map›Paper›PMID 36467393›Full record

ArticleIEEE journal of solid-state circuits2022

A 30% Efficient High-Output Voltage Fully Integrated Self-Biased Gate RF Rectifier Topology for Neural Implants.

Ziyue Xu, Adam Khalifa, Ankit Mittal, Mehdi Nasrollahpourmotlaghzanjani, Diptashree Das, Marvin Onabajo, Nian Xiang Sun, Sydney S Cash, Aatmesh Shrivastava

Abstract read
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In one paragraph

Article in IEEE journal of solid-state circuits, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ziyue XuDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Adam KhalifaMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Ankit MittalDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Mehdi NasrollahpourmotlaghzanjaniDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Diptashree DasDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Marvin OnabajoDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Nian Xiang SunDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
Sydney S CashMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Aatmesh ShrivastavaDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.

Funding

Neuroscience Resident Research ProgramUE5NS065743 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI SYDNEY S CASH, Ziv Williams · 2024 to 2026
$2.6M
Novel Implantable Smart Magnetoelectric NanoRFIDs for Large-Scale Neural Magnetic Recording and ModulationUF1NS107694 · NINDS · NORTHEASTERN UNIVERSITY · PI CASH, SYDNEY S, SUN, NIAN XIANG · 2018 to 2018
$1.8M
NINDS NIH HHS UE5 NS065743NINDS NIH HHS UF1 NS107694
6 · The paper itself

Abstract

This paper presents a fully integrated RF energy harvester (EH) with 30% end-to-end power harvesting efficiency (PHE) and supports high output voltage operation, up to 9.3V, with a 1.07 GHz input and under the electrode model for neural applications. The EH is composed of a novel 10-stage self-biased gate (SBG) rectifier with an on-chip matching network. The SBG topology elevates the gate-bias of transistors in a non-linear manner to enable higher conductivity. The design also achieves >20% PHE range of 12-dB. The design was fabricated in 65 nm CMOS technology and occupies an area of 0.0732-mm

Identifiers

PMID36467393
PMCPMC9718487

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.