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Analog to digital converter, incremental delta sigma ADC, extended counting, self-biased amplifier, amplifier sharing, CMOS image sensor

I. Introduction

CMOS image sensors (CISs) have been widely used in various applications such as mobile computing, security, and surveillance. As the applications of a CIS expands, demand for a small-size, high-resolution, and low-power analog to digital converter (ADC) is increasing for use in pixel column readouts. An incremental delta sigma ADC is considered an appropriate candidate for such applications.

For a fixed order of modulators, the signal to noise ratio (SNR) of incremental delta sigma ADCs can be improved either by increasing the oversampling ratio (OSR) or the quantizer resolution. However, the former requires the ADC to run with a higher clock frequency and the latter complicates the ADC design due to the use of a multi-bit digital to analog converter (DAC). Both methods eventually lead to an increase in the power consumption and the size of the ADC. It is known that the required power consumption or the size of an ADC quadruples for every 6dB increase in SNR.

In order to be employed in a column parallel ADC architecture(1), the ADC power and size should be limited to fit within a small pixel pitch. An extended counting technique which reuses the residual voltage of an ADC to increase the ADC resolution (2) is a good candidate as the column ADC of a CIS. In (3) and (4), the extended counting technique was successfully applied in an incremental delta-sigma ADC for an array application such as a biosensor array. However, the ADCs still occupied a large chip area, and they would not be suitable for a column parallel ADC architecture.

In this work, the extended counting technique is applied in a modified form to minimize the circuit components and the control signal for a second-order incremental delta-sigma ADC. The proposed incremental delta sigma ADC has a feedback loop that consists of the two-bit ADC’s MSB. Further, the ADC combines the LSB of the final conversion with the decimation filter output. To further reduce the chip size and power consumption of the ADC, a self-biased amplifier is shared between the two adjacent stages in the modulator.

The extended-counting incremental delta-sigma ADC, which occupies a die area of 0.0026 $mm^2$, has been fabricated in a 0.18-${\mathrm{\mu}}$m CIS process. The ADC achieves a 65 dB signal-to-noise and distortion ratio (SNDR) and consumes a total current of 25 ${\mathrm{\mu}}$A from a 1.8 V supply, resulting in a figure of merit (FOM) of 101 fJ/conversion step.

The remainder of this paper is organized as follows. Section II introduces the proposed extended-counting incremental delta-sigma ADC, and the implementation details of the ADC are explained in section III. Measurement results and conclusions are provided in section IV and V, respectively.

II. Proposed Extended - Counting

Incremental Delta - Sigma ADC

A linearized model of the extended-counting incremental delta-sigma ADC is shown in Fig. 1. It consists of two integrators, a two-bit flash ADC which is used as an internal quantizer, and a decimation filter. The modulator architecture is the same as the one proposed in (5) except that, instead of a single-bit quantizer, a 2-bit quantizer is used to implement the proposed extended-counting technique in this work. A Sinc2 filter consisting of a counter and an accumulator is used as a decimation filter.

As shown in Fig. 1, the modulator works as a conventional delta-sigma modulator utilizing a single-bit quantizer. Only the most significant bit (MSB) of the 2-bit quantizer output is fed back to the input node of the first integrator. The least significant bit (LSB) of the quantizer output, which is generated by using the last input sample, is later added to the output of the decimation filter.

Fig. 1. Simplified block diagram of the proposed ADC.

../../Resources/ieie/JSTS.2019.19.6.594/fig1.png

The transfer function from the modulator input VIN(z) to the output of the decimation filter $Y_1$(z) can be calculated as follows by using the linear model shown in Fig. 1.

(1)
$Y_{1}(z)=\frac{k_{1} B(z) z^{-2}}{A(z)\left(1-z^{-1}\right)^{2}} V_{I N}(z)+\frac{z^{-2}}{A(z)} E_{1}(z)$

(2)
$A(z)=1-\left(2-a_{1} k_{1}\right) z^{-1}+\left(1-a_{1} k_{1}+a_{1} a_{2} k_{1}\right) z^{-2}$

(3)
$B(z)=a_{1} k_{1} z^{-1}+\left(a_{1} a_{2}-a_{1}\right) k_{1} z^{-2}$

where A(z) and B(z) are the denominator and the numerator of the signal transfer function. These values are determined by the integrator gains ($a_1$ and $a_2$), respectively. $k_1$ is the quantizer gain and is determined by behavioral simulations. $E_1$(z) is the quantization error of the single-bit quantizer. Note that, even though the two-bit quantizer is used in the modulator, only the MSB of the quantizer output is used for the signal feedback. Hence, the effective resolution of the quantizer is one-bit. The transfer function from VIN(z) to Y2(z) can be calculated as follows

(4)
$Y_{2}(z)=k_{2} z^{-2}\left[\frac{A(z) B(z)-k_{1}(B(z))^{2}}{\left(1-z^{-1}\right)^{2} A(z)}+\frac{k_{1}\left(1-z^{-1}\right)^{2} B(z)}{\left(1-z^{-1}\right)^{2} A(z)}\right] V_{L N}(z) \\-k_{2} z^{-2}\left[\frac{B(z)+\left(1-z^{-1}\right)^{2}}{A(z)}\right] E_{1}(z)+E_{2}(z)$

where $k_2$ is the gain of the 2-bit quantizer.

The final output $D_{OUT}$(z) can be calculated by adding $Y_1$(z) with Y2(z) as follows

(5)
$\begin{aligned} D_{O U T}(z) &=Y_{1}(z)+Y_{2}(z) \\ &=S T F(z) V_{I N}(z)+N T F(z) E_{1}(z)+E_{2}(z) \end{aligned}$

(6)
$\begin{aligned} S T F(z)=& \frac{k_{2}z^{-2}\left[A(z) B(z)-k_{1}(B(z))^{2}+k_{1}\left(1-z^{-1}\right)^{2} B(z)\right]}{A(z)\left(1-z^{-1}\right)^{2}} \\ &+\frac{k_{1} B(z) z^{-2}}{A(z)\left(1-z^{-1}\right)^{2}} \end{aligned}$

(7)
$N T F(z)=\frac{z^{-2}-k_{2} z^{-2}\left[B(z)+\left(1-z^{-1}\right)^{2}\right]}{A(z)}$

In (7), $E_1$(z) can be removed by setting $a_1$, $a_2$, and $k_2$ equal to 1/3, 1/2, and 1, respectively. This leaves only $E_2$(z) which is theoretically four times smaller than $E_1$(z). However, $E_1$(z) cannot be totally removed in a real implementation mainly due to a low DC gain of the amplifier, and because of capacitor mismatches between the capacitors used in the integrators. The measurement results show that the SNDR and the effective number of bits (ENOB) are improved by 3 dB and 0.5-bit, respectively.

Table 1. Comparison of hardware complexity

Number of

quantizer bit

Single bit

Conventional

Two-bit

This work

DWA

X

2 Flip-flops

2-bit Adder

6 MUXs

X

Decimation

Filter

1st

stage

7 Flip-flops

8 Flip-flops

8 bit Adder

6 Flip-flops

2nd

stage

12 Flip-flops

12 bit Adder

12 Flip-flops

12 bit Adder

11 Flip-flops

11 bit Adder

Feedback DAC

1 DAC Structure

3 DAC Structures

1 DAC Structures

Number of Comparator

1

3

3

Fig. 2. Circuit diagram of the second-order delta-sigma modulator with two-bit quantizer and its clock signals.

../../Resources/ieie/JSTS.2019.19.6.594/fig2.png

It should be mentioned that the hardware complexity of the ADC has to be carefully considered because the width of the ADC cannot exceed two times the pixel pitch in the column-parallel architecture. This is the main reason for the difficulty in using a multi-bit quantizer in the delta-sigma ADC for a CIS. Table 1 compares the number of circuit components related to the quantizers for a single-bit and two-bit conventional ADCs and the circuit components for this work. In the conventional two-bit quantizer, as shown in Table 1, the required circuit components for the DWA and decimation filter are more than double those for the single-bit quantizer. However, the number of circuit components required in this work, though it uses a two-bit quantizer, is almost identical to that necessary for the single-bit quantizer.

III. Circuit Implementation and Operation

Fig. 2 shows a simplified circuit diagram and clock signals of the modulator. It consists of two integrators and three comparators with a two-bit encoder working as a quantizer. The upper side of the switched-capacitor circuit in the Fig. 2 functions as the first integrator and the lower side is the second integrator. The two integrators are realized in a single-ended configuration because the pixel generates an output voltage in a single-ended fashion. Furthermore, an amplifier is shared by the integrators to reduce chip area and power consumption. As shown in Fig. 3, a self-biased amplifier with switches added for amplifier sharing (5) is adopted in this work. To increase the gain of the amplifier, the sizes of the input PMOS and NMOS transistors are enlarged. This enlargement increases the parasitic capacitance associated with the input transistor. The input parasitic capacitance was carefully calculated by using a simulator, and was taken into account when the filter coefficients of the modulator were realized to suppress quantization noise.

The operation of the modulator is as follows. The main clock and the ADC enable signal (EN) are externally applied the reset signal (RST) which is low and non-overlapping clocks ($\phi_{1}$ and $\phi_{2}$) are generated by an internal clock generator. The pixel output is sampled by the sampling capacitor ($C_1S$) of the first integrator when $\phi_{1}$ is high, and transferred to the feedback capacitor ($C_1F$) when $\phi_{2}$ is high. Next, the output of the first integrator is processed by the second integrator in the same manner as did the first integrator using $\phi_{1}$ and $\phi_{2}$. The second integrator output is finally converted into a digital code by using the quantizer. Once the conversion process is finished, the reset signal (RST) toggles and the ADC is reset to an initial state for the conversion of the next pixel signal.

Fig. 3. Self-biased amplifier for amplifier sharing.

../../Resources/ieie/JSTS.2019.19.6.594/fig3.png

Fig. 4. Die micrograph and ADC layout.

../../Resources/ieie/JSTS.2019.19.6.594/fig4.png

Fig. 5. Measured dynamic performance

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IV. Measurement Results

A 384 ${\times}$ 200-pixel CIS employing the proposed ADC was fabricated in a 0.18-${\mathrm{\mu}}$m 1P4M CIS process. The die micrograph of the CIS, which occupies 5.5225 $mm^2$ (2.35mm ${\times}$ 2.35 mm), is shown in Fig. 4. The ADC takes up a die area of 0.0026 $mm^2$ (8.8 ${\mathrm{\mu}}$m ${\times}$ 300 ${\mathrm{\mu}}$m). A 2-shared four-transistor active pixel architecture is used. The ADCs for even columns and odd columns are placed at the upper and lower sides of the pixel array.

Fig. 5 shows the measured dynamic performance and the FFT plot of the ADC with a sinusoidal input frequency and an amplitude of 1.67 kHz and 0.75 $V_{P-P}$% (peak-to-peak voltage), respectively. The ADC was tested with and without the application of the extended counting for an OSR of 64. The measured FFT result of the ADC with extended counting is plotted using black lines, and the result without the use of extended counting is plotted as gray lines. The measured SNDR and ENOB, which are calculated by the formula (SNDR {--} 1.76)/6.02, are 62 dB and 10-bit, respectively, without the application of extended counting. The SNDR, and hence, the ENOB improve by 3dB and 0.5-bit, respectively, by the application of extended counting. As shown in Fig. 6, the measured differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.49 / ${-}$0.22 and +0.61 / ${-}$0.64 LSB, respectively. The ADC draws a current of 25 ${\mathrm{\mu}}$A from a 1.8 V supply, resulting in a power dissipation of 45 ${\mathrm{\mu}}$W.

It is the amplifier that consumes most of the ADC power. Table 1 compares the results from the state-of-the-art incremental delta-sigma ADCs with a CIS fabricated using similar technologies. Two popular FOMs, which are referred to as ${FoM}_{W}$ and ${FoM}_{A}$ in the Table 2, are used to make a fair comparison of the ADC performance. The FOMs of this work are the smallest among the FOMs of the ADCs fabricated with the same technology. Furthermore, they are comparable to the FOMs of the ADCs fabricated using the advanced processes.

Fig. 6. Measured static performance (a) differential nonlinearity (DNL), (b) integral nonlinearity (INL).

../../Resources/ieie/JSTS.2019.19.6.594/fig6.png

Fig. 7. Captured images using the CIS

../../Resources/ieie/JSTS.2019.19.6.594/fig7.png

Table 2. Performance comparison

Reference

[8]

[7]

[6]

[5]

[1]

This work

Technology

0.18-$\mu$m

0.13-$\mu$m

0.13-$\mu$m

0.18-$\mu$m

0.13-$\mu$m

0.18-$\mu$m

Architecture

EC

EC

Incremental

Incremental

Incremental

EC

Sampling frequency [MHz]

N/A

N/A

N/A

20

48

20

Signal bandwidth [kHz]

50

187.6

80

156.25

218

156.25

Supply voltage [V]

1.8

2.8

1.5

1.8

1.2

1.8

SNDR[dB] / ENOB [bit]

63 / 10.2

63.7 / 10.3

72(SNR) / 11.7

57.7 / 9.3

66 / 10.7

65 / 10.5

Power consumption [uW]

13

54.6

151

29.5

40

45

Active area [$mm^2$]

0.038

0.00105

0.18

0.0018656

0.0027

0.00264

${FoM}_{W}$ [fJ/Conv.step]*

111

116

283

151

56

101

${FoM}_{A}$ [fJ· $mm^2$/Conv.step]**

4.218

0.122

50.94

0.281

0.151

0.266

* ${FoM}_{W}$ = power / (2BW${\cdot}$${2}^{ENOB}$) ** ${FoM}_{A}$ = ${FoM}_{W}$·Area

V. Conclusion

A low-power and small-size extended-counting incremental delta-sigma ADC for a CIS has been presented. A new extended counting method is applied to a second-order incremental delta-sigma ADC without increasing hardware complexity and power consumption. To further reduce the ADC size and power consumption, a self-biased amplifier was shared between the adjacent integrators.

A CIS employing the proposed ADC has been fabricated in a 0.18-${\mathrm{\mu}}$m CIS process and occupies a die area of 5.5225 $mm^2$. The ADC itself occupies an active area of 0.0026 $mm^2$ and the measured SNDR and ENOB of the ADC running at a sampling clock of 20 MHz are improved, respectively, from 62 dB and 10-bit to 65 dB and 10.5-bit by using extended counting. The measured DNL and INL are +0.49 / ${-}$0.22 and +0.61 / ${-}$0.64 LSB, respectively. The ADC draws a current of 25${\mathrm{\mu}}$A from a 1.8 V supply and achieves ${FoM}_{W}$ and ${FoM}_{A}$ of 101 fJ/Conv-step and 0.266 fJ·$mm^2$/Conv-step, respectively (6-8).

ACKNOWLEDGMENTS

This research was supported by the Commercialization Promotion Agency for R&D Outcomes(COMPA) funded by the Ministry of Science and ICT(MSIT). [2019K000345] and this research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018-0-01433) supervised by the IITP(Institute for Information & communications Technology Promotion)

REFERENCES

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Chae Y., et al. , 2011, A 2.1 M Pixels, 120 Frame/s CMOS Image Sensor With Column-Parallel ΔΣ ADC Architecture, IEEE Journal of Solid-State Circuits, Vol. 46, No. 1, pp. 236-247DOI
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3 
Agah A., Vleugels K., Griffin P. B., Ronaghi M., Plummer J. D., Wooley B. A., 2010, A High-Resolution Low-Power Incremental ΣΔ ADC With Extended Range for Biosensor Arrays, IEEE Journal of Solid-State Circuits, Vol. 45, No. 6, pp. 1099-1110DOI
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Jaejin Y., Yongsuk C., Jeongjin R., Gunhee H., Youngcheol C., Seogheon H., 2014, A Current Regulator for Inverter-Based Massively Column-Parallel ΔΣ ADCs, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 61, No. 4, pp. 224-228DOI
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Author

Woo-Tae Kim
../../Resources/ieie/JSTS.2019.19.6.594/au1.png

Woo-Tae Kim received the B.S. degree in electronic engineering from Dongguk University, Seoul, South Korea, in 2012, and the M.S degree from the School of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea, in 2014.

He is currently pursuing the Ph.D. degree with the School of Electrical Engineering and Computer Science.

His research interests include CMOS image sensors and analog-to-digital converters

Byung-Seok Lee
../../Resources/ieie/JSTS.2019.19.6.594/au2.png

Byung-Geun Lee received the B.S. degree in electrical engineering from Korea University, Seoul, South Korea, in 2000, the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Texas at Austin, in 2004 and 2007, respectively.

From 2008 to 2010, he was a Senior Design Engineer at Qualcomm Inc., San Diego, CA, USA, where he had been involved in the development of various mixed-signal ICs. Since 2010, he has been with the Gwangju Institute of Science and Technology.

He is currently an Associate Professor with the School of Electrical Engineering and Computer Science.

His research interests include high-speed data converters, CMOS image sensors, and neuromorphic system design.