Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 129

Signal Classification Algorithm for the Detection of Abrupt Jumps in CMOS

Nanoelectrode Biosensor Applications

Q Zhu1,2

, T Merelle3,4

, F Widdershoven3, S Van Huffel

1,2,

1 Department of Electrical Engineering - ESAT, SCD - SISTA, Katholieke Universiteit Leuven, Leuven,

Belgium; 2 IBBT Future Health Department, Leuven, Belgium;

3 NXP Semiconductors, Leuven, Belgium; 4 Now at Global Foundries, Dresden, Germany.

Abstract

This paper presents a signal classification algorithm, in order to detect three types of abrupt jumps observed in the signal streams of CMOS nanoelectrode biosensors: an upward event, a dropping event and a random telegraph noise (RTN) event. It is a computationally efficient algorithm, suited for fast signal processing. Although the original purpose of the algorithm was to help improving the nanoelectrode manufacturing process, and to aid the interpretation of the experimental data, it suits various other engineering applications. It is tested on a number of biosensor data sets, acquired with different nanoelectrode materials under different experimental conditions. Good sensitivity and specificity of the algorithm for automatic detection and classification of the 3 different event types is verified by the experimental results.

Keywords Abrupt Jumps, Capacitance, Classification

Algorithm, CMOS Biosensor, Event Detection, FemtoFarad (fF), Frame Rate (FR), Nanoelectrode, Upward/Dropping/Random Telegraph Noise (RTN) Event

1 Introduction

CMOS nanoelectrode biosensors [1] are very

promising for healthcare applications, e.g., personalized

medicine, point-of-care diagnostics, etc. This is because

of a number of advantages of the on-chip electronics,

such as portability, real-time, label-free, miniaturization,

ease of use, and low-cost. The corresponding signal

processing algorithms need to be computationally very

efficient.

One of the major challenges for the CMOS biosensor

data processing algorithms is the automatic detection

and classification of features in a stream of signals, e.g.,

abrupt jumps. When reference liquid (i.e., not

containing biomolecules) is injected, a stationary signal

with constant variability/spread, and without abrupt

jumps and drifts, is expected. In practice, three types of

noise events often occur: an upward (jumping) event, a

dropping event, and a random telegraph noise (RTN)

event (see Fig. 1). The three event types are expected to

have different physical causes, and thus provide

potential feedback for improving the manufacturing

process and the data interpretation.

Existing methods of abrupt jump detection are

usually based on more advanced approaches, such as

wavelet transform and kernel density [2, 3], which

typically are computationally intensive. This paper

presents an efficient algorithm for detection and

classification of upward, dropping, and RTN events in

signal streams from the nanoelectrodes of CMOS

biosensors. Other information like the time stamp and

jump amplitude of an event can also be extracted.

2 Methods

The signal classification algorithm is a two-stage

(a) Drop (b) RTN (c) Upward

Figure 1: Examples of the three event types. (Horizontal axis: time in frame numbers (FN); Vertical axis: capacitance

signal in femtoFarad (fF). The frame rate was 5 Hz.)

Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 130

analysis. In the first stage, a cut-off value of capacitance,

at which point jumps are most likely to occur, is derived

based on the histogram of the capacitances of the

selected signal stream. In the second stage, an

automated algorithm is implemented, based on the cut-

off value, to detect the existence of jumping events.

The detailed steps of the signal classification

algorithm are described as follows:

At stage one:

1. Classification between noisy and normal signals

– if the histogram of the capacitance values of a

signal stream is unimodal, classify as a normal

event; otherwise (if the histogram is bimodal or

multimodal), classify as a noise event and go to 2;

2. Find the cut-off value of capacitance, based on

the histogram: after ignoring the extreme values

(in capacitance) on both the left and right tails of

the histogram, find the interval Eid (represented

by a bar of the histogram) that shows the least

number of observations (Fig. 2). Define the

middle point c1 (Fig. 3) of the interval Eid as the

cut-off value;

At stage two:

3. Split the signal stream into two groups through

value c1 (Fig. 3);

4. Drift correction: as drift can hinder the detection

of jumping events, it needs to be corrected. Drift

correction is done separately for the two groups.

For each group, use the Frame Number (FN)

(shown as the horizontal axis in Fig. 3) as the

covariate, fit a linear regression to the

capacitance (vertical axis in Fig. 3) values of the

signal stream. The fitted regression line captures

the drift pattern of the signal stream. After

subtracting the fitted regression line from the

signal, the drift is corrected;

5. Check the Grouped Mean Condition: if the

Grouped Mean Condition is satisfied (Fig. 3),

i.e.: 1 1 2 2-2 +2 , go to 6; else classify as a

normal event;

6. Check the Frame Number (FN) Condition:

compare absolute value of the difference in the

means of frame numbers for the two groups

(after rescaling with respect to their standard

deviation), with the student T statistic, i.e, if

2 2

( 1) ( 2)

1) ( 2)0.95, # 2

FN FN

FN FNt FN

（

, go to 7;

else classify as an RTN event;

7. Compare the grouped means: if

( 1) ( 2)FN FN , then classify as an upward

event; otherwise, classify as a dropping event.

0.018 0.02 0.022 0.024 0.026 0.028 0.03 0.032 0.034 0.0360

200

400

600

800

1000

1200

Eid

Figure 2: Graphical representation of Stage 1, of the

signal classification Algorithm.

3 Results

The biosensor used to test the algorithm consists of 256

rows by 256 columns of nanoelectrodes, making up 1

frame of 65,536 electrodes. Each electrode generates a

signal stream to be classified. Applying the algorithm to

each signal stream of one minute length takes less than

0.005s on a state of the art setup of a desktop computer,

which leads to less than 0.1 hour to process these

signals for a certain biosensor chip.

3.1 Corrosion resistance comparison of

two different electrode surface coating

methods

Au2 Au1

DIW

drops 6 227

upward 12 123

RTN 1583 1897

PBS

drops 44 937

upward 4 1153

RTN 380 1460

Table 1: Number of electrodes with the event types, for

the four measurements, detected by the signal

classification algorithm.

Gold coating of the sensor’s copper nanoelectrodes

is done with 2 different procedures, referred to as Au1

and Au2. Au2 protects the underlying copper better

(a) Drop (b) RTN (c) Upward

Figure 3: Steps 3 and 5 of the algorithm, concerning the three event types.

0 500 1000 1500 2000 2500 30000

0.2

0.4

0.6

0.8

1

1.2

1.4

c1

2,

2

1,

1

0 500 1000 1500 2000 2500 30000

0.2

0.4

0.6

0.8

1

c1

2,

2

1,

1

0 500 1000 1500 2000 2500 30000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

c1

1,

1

2,

2

Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 131

against corrosion from the injected liquid than the

previously used Au1. Hence, it is expected that less

noise events are detected when using the Au2 coating

technique.

To compare the corrosion resistance of the two

coating methods, their performance was tested in two

different liquids: de-ionized water (DIW) and phosphate

buffered saline (PBS). PBS, which contains 0.15

mol/liter (150mM) chloride ions, is known to be far

more corrosive to copper than DIW. Therefore, more

noise events are expected for PBS than for DIW,

especially in combination with the worse coating

method Au1 (which is more porous than Au2). For this

reason, the signal classification algorithm can be tested,

based on four types of measurements: Au2 DIW, Au2

PBS (150mM), Au1 DIW, and Au1 PBS (150mM). Ten

minutes of stable signals for each of the four

measurements were considered.

Table 1 shows the number of events detected by the

algorithm. From Table 1, the number of noise events is

clearly less when using Au2 compared with Au1,

especially the upward and dropping events. The upward

events can be explained by pinholes in electrodes that

have an inhomogeneous surface composition. Such

electrodes are susceptible to sudden corrosion at weak

spots at boundaries between different crystallographic

phases (refer to Figure 4). This leads to a sudden

increase in the effective surface area, and consequently,

in the electrode capacitance (which increases with

increasing surface area). Dropping events can be

explained by electrodes that are completely dissolved

during a major corrosion event, while RTN may be

generated by the transistors in the sensor chip.

Figure 4: SEM (scanning electron microscope) picture

taken with a backscattered electron detector (for optimal

chemical contrast). The two electrodes on the left have

inhomogeneous surfaces. Brighter regions are gold-rich;

darker regions are copper-rich.

The finding is in line with the expectation that Au2

coating is much more robust against corrosion than Au1

coating. This is not only proved by the much fewer

events for the Au2, compared with the Au1

measurements, but also by the relatively constant

upward/dropping events for PBS and DIW

measurements with Au2. In other words, when the new

Au2 is used, the corrosion effect is no longer sensitive

to the compositions of the injected liquid. On the other

hand, when using Au1, a significantly larger amount of

upward/dropping events was detected for PBS than for

DIW, since PBS is more corrosive.

Figure 5 shows the mappings of the (jumping)

events for the four measurements on the electrode array

of the biosensor chip. Strong column-dependent

(vertical) effects of the RTN events are observed,

caused by transistors in the peripheral read-out circuits.

All electrodes on the same column share the same read

out circuit. This proves the robustness of the algorithm.

From Figure 5d, which shows the events detected for

the Au1 PBS measurement, the upward and dropping

events scatter across the array. These events are very

likely to be corrosion related ones.

The false positive rates of event detection, for Au2

DIW, Au2 PBS, Au1 DIW, and Au1 PBS, are

respectively 1.06%, 0.23%, 0.45% and 0.45%, which

are quite negligible. These false positive events are all

detected as RTN events, the type of which is the most

difficult to detect among the three types. There is no

misclassification among the three event types.

3.2 Corrosion resistance for different

fluid compositions

In this section we study the corrosion resistance of

nanoelectrodes coated with the Au2 method to fluids of

different composition. The experiment consists of 2

sub-experiments, one with DRY (in air) and DIW, and

the other with DIW and three steps of increasing

concentrations of PBS: 10mM, 50mM and 150mM,

respectively. For each step, 8 minutes of relatively

stable signals were recorded for the application of the

electrode classification algorithm.

Table 2 shows the average amplitudes of jumping

events for different experimental steps. As expected, the

robustness against corrosion of the Au2 coating is not

only reflected by a low number of upward/dropping

events (as discussed in Section 3.1), but also by a

relatively constant amplitude of the jumping events for

different fluid compositions – Table 2 shows that the

amplitude is quite constant for all the steps, except for a

slight increase at the highest 2PBS concentrations.

DRY DIW

Exp1 0.0057 0.0053

DIW 10mM 50mM 150mM

Exp2 0.0055 0.0050 0.0067 0.0070

Table 2: Average amplitudes (in fF) of jumping events

for the six experimental steps.

Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 132

50 100 150 200 250

50

100

150

200

250

Column

Row

AuCuDIW(180211)

50 100 150 200 250

50

100

150

200

250

Column

Row

AuelessDIW(070910)

(a) Au2 DIW (b) Au1 DIW

50 100 150 200 250

50

100

150

200

250

Column

Row

AuCuPBS(110411)

50 100 150 200 250

50

100

150

200

250

Column

Row

AuelessPBS(160910)

(c) Au2 PBS (d) Au1 PBS

Figure 5: Mapping of dropping events (blue), upward events (green), and RTN events (magenta) on the electrode array of

the biosensor chip, for the four measurements.

4 Conclusions

The signal classification algorithm gives precious

insights in the proportion of electrodes damaged by

corrosion during an experiment as well as on the

amplitude of these events. Apart from these factors,

dropping events which are spatially correlated, i.e.,

clustered on the biosensor array, and detected around

the same time instant, can point to, e.g., an air-bubble

during the experiment. The algorithm was developed as

a tool for the improvement of the nanoelectrode coating

process, by extracting corrosion-induced features from

noisy signals, in the presence of air-bubbles or dirt

particles. It suits fast signal processing applications,

with good sensitivity and specificity.

The algorithm is methodologically straightforward,

and can be immediately applied to various alternative

engineering tasks, for classifying different jumping

events in signal streams.

Acknowledgements

The research was supported by NXP Semiconductors;

Research Council KUL: GOA MaNet; Belgian Federal

Science Policy Office: IUAP P6/04 (DYSCO,

`Dynamical systems, control and optimization', 2007-

2011).

References

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Merelle, H. Suy, F. Jedema, R. Hoofman, C. Tak, A. Sedzin,

B. Cobelens, E. Sterckx, R. van der Werf, K. Verheyden, M.

Kengen, F. Swartjes, and F. Frederix. CMOS biosensor

platform. Technical Digest International Electron Devices

Meeting IEDM (2010), 816-819.

[2] M. Chabert, J. Y. Tourneret, and F. Castanie. Additive

and multiplicative abrupt jump detection using the continuous

wavelet transform. IEEE Trans. on Acoustics, Speech, and

Signal Processing (1996), 3002-3005.

[3] D. N. Nikovski, and A. Jain. Fast Adaptive Algorithms

for Abrupt Change Detection. Machine Learning (2010),

79:283-306.

Address for correspondence:

Qi Zhu Department of Electrical Engineering - ESAT, SCD - SISTA, Katholieke Universiteit Leuven, Leuven, Belgium IBBT Future Health Department, Leuven, Belgium Address: Kasteelpark Arenberg 10, bus 2446

B-3001 Leuven, Belgium Qi.Zhu@esat.kuleuven.be or Sabine.VanHuffel@esat.kuleuven.be