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. 2018 Oct 9;18(10):3370.
doi: 10.3390/s18103370.

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

Saghi Forouhi  1   2 , Rasoul Dehghani  3 , Ebrahim Ghafar-Zadeh  4
Affiliations

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

Saghi Forouhi et al. Sensors (Basel). .

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.

Keywords: CBCM; CMOS; bioengineering; capacitive sensor; dynamic range.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Conceptual view of wire-bonded electrodes used for microelectromechanical systems (MEMS) applications, (b) conceptual view of on-chip electrodes used for Laboratory on a Chip (LoC) applications.
Figure 2
Figure 2
Core of the charge-based capacitance measurement (CBCM).
Figure 3
Figure 3
A core-CBCM capacitance-to-voltage converter (CVC) that integrates the currents iS and iR before subtraction (adapted from [61]).
Figure 4
Figure 4
A core-CBCM CVC that subtracts the currents iS and iR before integration (adapted from [58]).
Figure 5
Figure 5
Fully differential core-CBCM CVC adapted from [60,64].
Figure 6
Figure 6
A core-CBCM capacitance-to-frequency converter that integrates all of the exponential CBCM currents in the analog domain and converts them to two frequencies (adapted from [66]).
Figure 7
Figure 7
Block diagram of the proposed capacitive sensor.
Figure 8
Figure 8
(a) Basic design of the CCO [75], (b) CCO input current, (c) CCO output voltage, (d) CCO averaged input current based on the mean value theorem.
Figure 9
Figure 9
Frequency of the output pulses of the CCO and its envelope (by exaggeration in the sizes of the unit blocks).
Figure 10
Figure 10
Complete current-controlled oscillator proposed in [75].
Figure 11
Figure 11
Proposed 16-bit LFSR-based reverse/forward counter/register.
Figure 12
Figure 12
(a) Fast static D flip-flop, (b) Fast XOR gates based on a differential cascade voltage switch with a pass-gate (DCVSPG) [78].
Figure 13
Figure 13
Model of interdigitated electrodes.
Figure 14
Figure 14
Different waveforms of the proposed sensor.
Figure 15
Figure 15
(a) CCO output frequency vs DC input current, (b) Error between the polynomial fitted line and the simulation results.
Figure 16
Figure 16
(a) Variations of the CBCM output current (iX) versus time, (b) Variations of the output frequency of the CCO (fCCO) versus time, (c) Variations of the CCO output voltage pulses (vCCO) for the CBCM output current as the CCO input (iCCO = iX) for ΔC = 60 fF (in order to show the compact high frequency pulses more clearly and to lower their frequencies, Ibias is omitted from the input of the CCO).
Figure 17
Figure 17
(a) Input current of the CCO, (b) CCO output frequency versus five different ΔCs.
Figure 18
Figure 18
(a) Integration of iCCO over Tint versus ΔCs, (b) Integration of fCCO over Tint versus ΔCs.
Figure 19
Figure 19
(a) Number of pulses versus capacitance changes ΔC (fF), (b) Error between the simulation results for 15 different values of ΔC and the polynomial fitted line (at three different temperatures of 15 °C, 27 °C and 45 °C).
Figure 20
Figure 20
(a) Fifteen measured number of pulses versus capacitance changes ΔC (fF) at 27 °C, and the interpolated curve and the polynomial fitted line to these 15 points. (b) The error between the measured numbers of pulses related to the other values of ΔC after pre-distortion, and the polynomial fitted line.
Figure 21
Figure 21
The effect of mismatch error on the capacitive sensor output after the elimination of offset.
Figure 22
Figure 22
Layout of the proposed capacitive sensor, along with sensing and reference electrodes.
Figure 23
Figure 23
Sensing capacitance versus fibroblast cell confluency percentage.
Figure 24
Figure 24
A 10 × 10 array of the proposed capacitive sensor.
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