Evidence map›Paper›PMID 30304843›Full record

ArticleSensors (Basel, Switzerland)2018

Toward High Throughput Core-CBCM CMOS Capacitive Sensors for Life Science Applications: A Novel Current-Mode for High Dynamic Range Circuitry.

Saghi Forouhi, Rasoul Dehghani, Ebrahim Ghafar-Zadeh

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. From Sensing Mechanisms to System Co-Design: Graphene-Based Sensors and Readout Circuits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

3 authors.

Saghi ForouhiDepartment of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran. s.forouhi@ec.iut.ac.ir.
Rasoul DehghaniDepartment of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran. dehghani@cc.iut.ac.ir.
Ebrahim Ghafar-ZadehBiologically Inspired Sensors and Actuators (BioSA), Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada. egz@cse.yorku.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a novel charge-based Complementary Metal Oxide Semiconductor (CMOS) capacitive sensor for life science applications. Charge-based capacitance measurement (CBCM) has significantly attracted the attention of researchers for the design and implementation of high-precision CMOS capacitive biosensors. A conventional core-CBCM capacitive sensor consists of a capacitance-to-voltage converter (CVC), followed by a voltage-to-digital converter. In spite of their high accuracy and low complexity, their input dynamic range (IDR) limits the advantages of core-CBCM capacitive sensors for most biological applications, including cellular monitoring. In this paper, after a brief review of core-CBCM capacitive sensors, we address this challenge by proposing a new current-mode core-CBCM design. In this design, we combine CBCM and current-controlled oscillator (CCO) structures to improve the IDR of the capacitive readout circuit. Using a 0.18 μm CMOS process, we demonstrate and discuss the Cadence simulation results to demonstrate the high performance of the proposed circuitry. Based on these results, the proposed circuit offers an IDR ranging from 873 aF to 70 fF with a resolution of about 10 aF. This CMOS capacitive sensor with such a wide IDR can be employed for monitoring cellular and molecular activities that are suitable for biological research and clinical purposes.

Indexed as

bioengineeringcapacitive sensorCBCMCMOSdynamic range

Identifiers

PMID30304843
PMCPMC6210700

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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.