# Balanced plasmonic-augmented silicon photonic interferometric sensor for biosensing applications

Mahmoud A Elrabiaay, Christia Eleftheriou, George Tsekenis, Omkar Bhalerao, Stephan Suckow, Max Lemme, Pratyusha Das, Laurent Markey, Athanasios Manolis, Dimitris Tsiokos

► To cite this version:

Mahmoud A Elrabiaay, Christia Eleftheriou, George Tsekenis, Omkar Bhalerao, Stephan Suckow, et al.. Balanced plasmonic-augmented silicon photonic interferometric sensor for biosensing applications. Optics Express, 2025, 33 (11), pp.23110. 10.1364/OE.533570. hal-05229750

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https://hal.science/hal-05229750v1

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# Balanced plasmonic-augmented silicon photonic interferometric sensor for biosensing applications

MAHMOUD A. ELRABIAAY,<fnref n="1" /> ID CHRISTIA ELEFTHERIOU,<fnref n="1" /> GEORGE TSEKENIS,<fnref n="1" /> OMKAR BHALERAO,<fnref n="2" />,<fnref n="3" /> STEPHAN SUCKOW,<fnref n="2" /> MAX LEMME,<fnref n="2" />,<fnref n="3" /> ID PRATYUSHA DAS,<fnref n="4" /> LAURENT MARKEY,<fnref n="4" /> ATHANASIOS MANOLIS,<fnref n="1" /> AND DIMITRIS TSIOKOS<fnref n="1" />

**Abstract:** We demonstrate a self-referenced plasmonic augmented photonic Mach-Zehnder interferometer (MZI) for biosensing applications, incorporating a 70 µm long aluminum plasmonic waveguide in both arms of a silicon nitride (Si₃N₄) MZI. Experimental results matched well with numerical simulations, showing extinction ratio (ER) values that exceed 55 dB and bulk sensitivity of 2322 nm/RIU. Wavelength stability measurements revealed 5×10⁻<fnref n="3" /> nm thermal dependance for an ambient temperature fluctuation of 1-degree Celsius which is 50 times better than a conventional hybrid plasmo-photonic asymmetric MZI (aMZI) configuration, highlighting the robustness of the proposed balanced sensor scheme to ambient temperature fluctuations. Moreover, the experimental results show that the proposed sensor has a limit of detection up to 2.6 ×10⁻⁶ RIU. Surface sensitivity of the proposed sensing transducer was evaluated through a C-reactive protein (CRP) surface saturation measurements and found equal to 10.45 nm/RIU for protein binding. Our validated simulation model was then benchmarked against a diverse set of target bio-analytes (proteins, viruses, bacteria, fungi) with surface sensitivity values reaching up to 2306 nm/RIU, indicating the sensor's heterogeneous detection capabilities. The presented self-referencing sensor configuration offers a cost-effective yet scalable and highly sensitive approach for multiplexed on-chip detection of diverse target analytes.

Published by Optica Publishing Group under the terms of the [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/). Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

## 1. Introduction

In the realm of future lab-on-a-chip (LOC) refractive index (RI) based sensors, strict criteria must be met, including ultra-high sensitivity, small sample volumes, stability, multi-channel operation, and CMOS-compatibility [1–4]. Amongst others, these requirements facilitate efficient, high-volume and cost-effective production, especially for disposable testing. In this regard, both silicon photonics and plasmonics have recently earned considerable research attention, aiming to develop robust RI sensors that can partially fulfill these demanding requirements. Typically, photonic integrated RI sensors leverage low-loss slot and strip-based waveguides as sensing transducers. These sensors are able to quantify variations in the ambient RI through diverse configurations, such as Mach-Zehnder interferometers (MZIs) [5–8], mirroring resonators [9], photonic crystal cavities (PhC) [10], subwavelength waveguides [11], or even hybrid designs that combine a dielectric strip waveguide with a metallic Bragg grating and a phase-shifted cavity

<footnote n="1">¹bialoom Ltd., 72 28th Octovriou Avenue, Office 301, Engomi, 2414 Nicosia, Cyprus</footnote>

<footnote n="2">²AMO GmbH, Advanced Microelectronic Center Aachen (AMICA), Otto-Blumenthal-Strasse 25, 52074 Aachen, Germany</footnote>

<footnote n="3">³Chair of Electronic Devices, RWTH Aachen University, Aachen 52062, Germany</footnote>

<footnote n="4">⁴Laboratoire Interdisciplinaire Carnot de Bourgogne CNRS UMR 6303, Université Bourgogne Europe, 21000 Dijon, France</footnote>
[12]. Despite these advancements, the sensitivity of these sensors remains limited, with values reaching up to 1538 nm per refractive index unit (RIU) [4]. In contrast, hybrid plasmo-photonic asymmetric MZI (aMZI) configuration demonstrates exceptional sensitivity, exceeding 40000 nm per refractive index unit (RUI) [1,4,13]. This substantial improvement underscores the hybrid approach's potential to meet the high sensitivity requirements of future lab-on-a-chip RI sensors while using ultra small volumes of sample due to the micro-plasmonic sensor transducers.

The aMZI design employs a hybrid plasmo-photonic waveguide transducer in one arm (sensing arm), which is exposed to the analyte while the other arm (reference arm) is composed of a purely photonic waveguide. The plasmo-photonic (sensing) and the purely-photonic (reference) signals combine at the output of the MZI to produce an interference pattern. Interactions between the analyte and the evanescent field of the plasmonic waveguide in the sensing arm introduce a phase shift in the interference pattern, resulting in a wavelength shift (Δλ) of the MZI transmission spectrum. However, despite the impressive bulk sensitivity achieved by the hybrid plasmo-photonic aMZI design in previous works, it exhibits limited extension ratio (ER) of 25 dB, attributed to the high losses in the sensing arm, coming from the plasmonic waveguide [2,4,14]. To address the ER problem, a solution involving MZI-based variable optical attenuator (VOA) with a thermal phase shifter was introduced. This solution optimally balanced losses between the photonic and plasmo-photonic MZI branches, thereby maximizing the ER and enabling lower optical detection limits [2]. Through this approach, experimental results have shown ER values of more than 40 dB and ultra-high bulk sensitivity values reaching up to 63720 nm/RIU [1,4]. However, VOA adds significant optical and electrical circuit complexity, it increases the overall MZI footprint and introduces stringent power supply requirements. Another challenge in the hybrid plasmo-photonic aMZI sensor scheme is the difference in the thermo-optic coefficients between the propagating modes of the plasmonic waveguide in the sensing arm and the dielectric mode of the photonic waveguide in the reference arm. This difference can lead to thermal dependence of the sensor signal to ambient temperature variations and subsequently limit the sensor optical limit of detection (LoD). A reference sensor is commonly employed on the same chip to mitigate such unwanted effects [14]. However, the performance of this reference sensor highly depends on the fabrication yield, while its incorporation on the same chip either leads to significant wafer space consumption if integrated next to every sensor on the chip or it becomes ineffective if it is used as a common reference for all the sensors across the same chip, compromising scalability over stability and increasing the complexity of layout.

In a notable effort, Xu Sun et al. [15], proposed a more practical solution by introducing a hybrid plasmonic (HP) waveguide in the reference arm as well. This approach not only balances losses between arms, thereby increasing the ER to more than 40 dB, but also effectively addresses stability constraints and layout complexities of the sensor. Nevertheless, while this solution enhances sensor stability and reduces the layout complexity, a peak bulk sensitivity of only 245 nm/RIU was demonstrated. Additionally, in this work a 40 µm long gold plasmonic waveguide is used, limiting mass fabrication of this sensor at low cost. Furthermore, the bio-functionalization of the proposed hollow plasmonic structure poses considerable challenges, thereby limiting the practical use as a biosensor.

In this study, we present a highly sensitive, self-referenced and scalable plasmo-photonic MZI sensor. The proposed configuration aims to attain a substantial ER and LoD while effectively improving sensor signal stability. The proposed sensor design integrates two identical 70 µm long aluminum (Al) plasmonic transducers within a silicon nitride (Si₃N₄) waveguide-based MZI, offering high sensitivity while maintaining CMOS compatibility. Bulk sensitivity was evaluated by means of numerical simulations, reaching values of 2322 nm/RIU. Additionally, experimental results revealed a significant improvement in the sensor signal stability compared to the aMZI approach, with the balanced configuration achieving thermal dependence of only 5×10⁻³ nm/°C which is 50 times better than that of plasmo-photonic aMZI approach. Moreover, the LoD
was calculated experimentally to be $2.6 \times 10^{-6}$ RIU. Surface sensitivity was investigated using C-reactive protein (CRP) detection as an example at which a surface saturation with CRP molecules was achieved within 20 min, revealing surface sensitivity values of 10.45 nm/RIU, closely matching the respective analytical and simulation results. Furthermore, our experimentally validated simulation model is used to demonstrate the capability of the proposed sensor scheme to detect heterogeneous target analytes (proteins, virus, bacteria, fungi) with surface sensitivity values up to 2306 nm/RIU fully exploiting its sensing potentials.

## 2. Design concept and simulations

The proposed plasmo-photonic sensor configuration is depicted in Fig. 1. The sensor layout integrates a 70 µm long Al plasmonic waveguide in each arm of a $Si_3N_4$-based MZI, serving as the sensing and reference transducer, respectively. In this way, power balancing between the reference and sensing arms of the MZI with equal amplitude field interference can be achieved at the output, leading to high ER values. The Aluminum plasmonic waveguides in both arms are fully exposed to the overlying medium and, thus, any bulk changes in the RI originating from the ambient environment or nonspecific adsorption are canceled out at the MZI output. To achieve specific detection of target analytes, one of the plasmonic waveguides (sensing transducer) is chemically modified with biorecognition elements (i.e. antibodies), meaning that local RI changes attributed to binding events result in a differential phase change in the sensing branch, eventually translated to $\Delta\lambda$ of the MZI transmission spectrum at the sensor output.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Conceptual schematic of the proposed dual plasmonic MZI biosensor.</figcaption></figure>

In the following subsections component-level and full circuit-level simulations of the sensor are described, evaluating the bulk sensitivity, surface sensitivity and fabrication tolerances of the proposed device. Ansys-Lumerical simulation software was used to conduct the simulations described in the following subsections [16]. The RI values of silicon (Si), silicon dioxide ($SiO_2$), aluminum (Al), and aluminum oxide ($Al_2O_3$) are predefined in the Lumerical material database using Palik's model [17] while $Si_3N_4$ refractive index is based on Luke's model, specified in [18].

### 2.1. Component simulation

The photonic waveguide platform is based on a 360 nm × 800 nm (thickness × width) $Si_3N_4$ core laying on top of a 3 µm thick $SiO_2$ substrate and cladded with a 6 µm thick Allresist (SX AR LWL 2.0) cladding layer as shown in Fig. 2(a). The RI of the cladding material, as measured by the manufacturer using ellipsometry (636 nm – 1545 nm), is shown in Fig. 2(b). The RI of the cladding layer closely matches that of the conventional $SiO_2$ cladding (1.4438) over the spectral range of interest (1480 to 1630 nm). Eigenmode simulations were performed around 1550 nm wavelength range, with mesh sizes of 4 nm and 3 nm in the x and y axis, respectively. The waveguide dimensions were designed to support a single mode with transverse magnetic
(TM) polarization, exhibiting an effective index ($n_{eff}$) of 1.514. Additionally, the eigenmode simulations show a propagation loss of 0.2 dB/cm at 1550 nm wavelength. Furthermore, a Si$_3$N$_4$ bend ($r = 75$ µm) with insertion loss of 0.6 dB/cm is used in the proposed MZI design.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** (a) schematic diagram of the photonic waveguide with the normalized electric field distribution, (b) measured refractive index of the SX AR LWL 2.0 used as a cladding material, (c) schematic diagram of the MMI splitter, (d) splitting ratio at the output ports of the MMI splitter (the inner plot presents the normalized electric field distribution of splitter), (e) schematic diagram of the plasmonic waveguide with the normalized field distribution of plasmonic mode (E), and (f) broadband transmission (inner plot is the normalized electric field distribution for photonic/plasmonic interface) for the plasmonic waveguide excitation.</figcaption></figure>

To split and combine light evenly between both arms of the MZI, a 1×2 multimode interference (MMI) coupler is employed as a power splitter/combiner. The Si$_3$N$_4$ MMI is 4.1 µm wide and 10.6 µm long. Moreover, to enhance mode matching between the multimode superposition region
and the fundamental TM mode of the Si₃N₄ photonic waveguide, a taper is incorporated at the input and output of the MMI. The tapered waveguide was designed to match the fundamental TM mode of the Si₃N₄ waveguide, featuring widths of 0.8 µm and 1.75 µm, with a tapering length of 10 µm as shown in Fig. 2(c). At the output, the tapered waveguides are followed by an S-shaped bent with a bending radius (r) of 75 µm. Figure 2(d) illustrates the transmission and X-Y normalized electric field distribution derived from the finite difference time domain (FDTD) simulation. The power splitting ratios for TM polarization are evenly distributed, measuring a transmission power of 3.06 dB for both ports 2 and 3 of the MMI.

The proposed sensing transducer is a 70 µm long, 7 µm wide, and 80 nm thick Aluminum stripe inspired from [19]. An approximately 3 nm thick native Al₂O₃ layer is formed naturally on top of the stripe upon its exposure to ambient conditions, acting as a protective layer, preventing further oxidation of Aluminum [1,20]. Eigenmode simulations were conducted using a mesh size of 4 nm and 0.3 nm on x and y axis respectively. The results of these simulations indicate that the plasmonic propagation length (L SSP) of the Al waveguide is 66 µm. This value is comparable to the length of the plasmonic waveguide (70 µm) providing an optimal balance between sensing length and optical losses. The evanescent field decay length of the plasmonic waveguide is 2 µm in aqueous environment, as shown in Fig. 2(e). The plasmonic excitation on the Al waveguide is achieved through a butt coupling mechanism, where the Al stripe is positioned between two 7.5 µm wide Si₃N₄ waveguides, with vertical (VO) and longitudinal (LO) offsets of 400 nm and 500 nm, respectively [3]. FDTD simulation reveal transmission of 2.25 dB at each photonic to plasmonic mode conversion facet as shown in Fig. 2(f).

## 2.2. Circuit-level simulations

Circuit-level simulations were performed to evaluate the transmission spectrum of the MZI using INTERCONNECT. The MZI sensor circuit was implemented by integrating the previously reported elements (section 2.1) as building blocks in the circuit simulator, according to the schematic shown in Fig. 3(a). The frequency-dependent properties of the plasmonic and photonic waveguides were taken into account in these circuit-level simulations, along with the S-parameters for the MMI and transducer. The plasmonic waveguides were deployed in both the upper arm and lower arm of the MZI, acting as the sensing and reference transducer, respectively.

<figure><img src="image_4.png" /><figcaption>Fig. 3. (a) the layout of the proposed sensor. (b) The simulated spectral response of the proposed sensor.</figcaption></figure>

Figure 3(b), illustrates the spectral response of the MZI, exhibiting an insertion loss (IL) of 9.7 dB, that is attributed to the photonic to plasmonic coupling and to the propagation loss of the
plasmonic and photonic waveguides. The free spectral range (FSR) of the proposed MZI is 50 nm and the ER equal to 78 dB at 1546.23 nm.

To calculate the bulk sensitivity (m), we simulated the $n_{eff}$ of the plasmonic mode of the sensing transducer in the upper arm when exposed to various aqueous solutions with different RI ($n_s$). These solutions are deionized (DI) water (RI = 1.3324), 10% (RI = 1.3328) and 100% (RI = 1.3389) phosphate-buffered saline (PBS), and aqueous NaCl solution (RI = 1.3343) [1]. These specific solutions were chosen due to their nearly neutral pH [21], closely resembling the pH variations of blood (pH 7.4) mimicking the experimental biosensing conditions. The $n_{eff}$ of the plasmonic mode in the transducer of reference arm, calculated in DI water (RI = 1.3324), is used as a reference. Figure 4(a). illustrates the calculated spectral shift when exposing the sensing transducer to the aqueous solutions where $n_r$ and $n_s$ are the RI of solutions in reference and sensing transducer, respectively. Results reveal a red shift in the transmission spectrum with increasing RI values. Based on these wavelength shifts, the calculated bulk sensitivity value was 2322 nm/RIU as shown Fig. 4(b).

<figure><img src="image_5.png" /><figcaption>Fig. 4. (a) The simulated spectral response of the proposed sensor for bulk sensitivity (m) measurement where $n_r$ and $n_s$ are the RI of solutions in reference and sensing transducer, respectively. (b) The linear regression of $\Delta\lambda$ versus RI change.</figcaption></figure>

Following the evaluation of the bulk sensitivity, the surface sensitivity (S) calculations were performed to assess the performance of the proposed sensor in bio-sensing applications. Surface sensitivity (S) indicates the $\Delta\lambda$ in the transmission spectrum of the sensor, caused by local RI changes on the sensing surface where binding occurs. Consequently, the surface sensitivity is closely linked to the field distribution of the plasmonic waveguide sensing transducer. Furthermore, it is also related to the bulk sensitivity, and the ratio of the thickness of the bio-layer (t) and the covered volume of the evanescent electric field corresponding to the decay length ($l_d$). Accordingly, S can be defined as [22]:

$$S = m \times \left( 1 - \exp \left( -2 \times \frac{t}{l_d} \right) \right) \quad (1)$$

The surface sensitivity of the proposed sensor relates to the thickness of the bio-layer, as indicated by Eq. (1). When the volume of the bio-layer matches the decay volume of the plasmonic mode, the surface sensitivity approaches near the value of m.

CRP is chosen as a use case for evaluating the surface sensitivity of the proposed sensor scheme in terms of protein binding. CRP is a typical inflammation biomarker commonly used in clinical practice and medical diagnostics to evaluate internal inflammation, infection and disease
severity. The physical structure of CRP has a thickness of 4.5 nm with a RI of 1.45 [23,24]. Based on the surface sensitivity equation, the proposed sensor can achieve a surface sensitivity of approximately 10.45 nm/RIU for protein binding. The resulted Δλ from CRP detection can be calculated analytically using Eq. (2) [22]:

$$ \Delta\lambda = S \times \Delta n \qquad (2) $$

where Δn is the RI difference between CRP medium and the buffer medium. Considering CRP RI equal to 1.45 and the RI of the buffer medium equal to 1.3355, the corresponding Δλ in the transmission spectrum of the sensor is 1.2 nm.

## 2.3. Simulations for CRP binding

Following the analytical evaluation of the surface sensitivity, eigen mode and circuit-level simulations were conducted to compute the Δλ for surface saturation with CRP. This study consists of three main steps. Initially, the frequency-dependent properties of the plasmonic waveguide, such as n<sub>eff</sub>, loss, and n<sub>g</sub>, were determined using an eigenmode solver. To obtain the baseline spectrum, the initial step involved conducting an eigenmode simulation for the reference transducer, incorporating a 4 nm silane layer on top of the plasmonic waveguide. Then another simulation was performed for the sensing transducer where 10 nm antibody layer in addition to the 4 nm of silane was placed above plasmonic waveguide. The refractive indices of the silane and antibody layers are assumed to be 1.338 and 1.38 RIU [25], respectively.

In the second step, the simulation was repeated with an additional CRP layer (Refractive Index: 1.45, Thickness: 4.5 nm) [23] on top of the antibody layer, only for the sensing transducer as in shown in Fig. 5(a). These simulations were performed with a fine mesh of 4 nm and 0.4 nm in the x and y axis, respectively to ensure accurate representation of the functionalization layers. A perfectly matched layer (PML) boundary condition was applied in the simulation.

<figure><img src="image_6.png" /><figcaption>Fig. 5. (a) Schematic visualization of stacked layers of silane, antibody, and CRP, and (b) sensor spectral response with and without CRP layer (inner plot is the spectral response in the wavelength range between1534 nm to 1540 nm).</figcaption></figure>

The third step involved incorporating the frequency properties obtained from the eigenmode simulations into a circuit-level simulation. This allows for a comprehensive analysis of the sensor's performance and dynamic range, enabling the determination of the actual Δλ in response to the saturation of the sensing surface with CRP. Figure 5(b) presents a red Δλ of 1.27 nm for surface saturation, in the case of having the sensing transducer is at the upper arm and the reference transducer is at lower arm of MZI. These results also match the analytical Δλ, calculated from Eq. (2). Furthermore, the Δλ is associated with a decrease in the ER, attributed to increased losses in the sensing transducer due to the presence of antibody and CRP layers.
### 3. Fabrication and functionalization process

#### 3.1. Sensor fabrication

The sensor fabrication is similar to the process flow reported in our previous work [1]. The sensors were fabricated on 6" Si substrates with a 3 µm thick thermally grown SiO₂ layer. Initially, an approximately 360 nm thick Si₃N₄ layer was produced on the wafer using the chemical vapor deposition (CVD) technique. The resulting layer was patterned using i-line lithography and reactive ion etching (RIE) to form the Si₃N₄ waveguide core. Subsequently, a 500 nm deep cavity (VO) was etched in the SiO₂ layer using another i-line lithography and RIE step. An 80 nm thick Al layer was then deposited on the wafer using an electron beam evaporator. The Al layer was patterned using i-line lithography and a lift-off technique to form the plasmonic waveguides inside the SiO₂ cavity. An approximately 6 µm thick layer of the SX AR LWL 2.0 (Allresist) cladding polymer was spin-coated and patterned using i-line lithography. The cladding polymer was open solely at the sensing and spot-size converter areas. In the former approach [1], the deposition of the Si₃N₄ layer was followed by the deposition of a thick Low-Temperature-Oxide (LTO) SiO₂ layer. The entire material stack was then patterned and etched together to create VO, resulting in an etch depth exceeding 1.46 µm. This deep etching results in a very challenging subsequent metal lift-off process. In contrast, the use of the i-line compatible polymer cladding enables single-step patterning of the VO, eliminating the need for a deep cavity-etch step. The incorporation of a SX AR LWL 2.0 (Allresist) cladding has allowed us to simplify the fabrication process in comparison to the former approach. Moreover, the RIs of the polymer and SiO₂ match closely, no adverse impact on optical performance was expected from this material substitution.

For light coupling, spot-size converters (edge couplers) are used to facilitate butt-coupling of light from the laser source to the Si₃N₄ waveguide core. To form the spot-size converters, a 10 µm SU-8 edge couplers layer was spin-coated and patterned using i-line lithography to have 10 × 10 µm² SU-8 Edge couplers. The dismissions of SU-8 edge couplers are closely match the mode field diameter (MFD) of the coupling fiber at 1550 nm wavelength. This coupling approach reduces the IL between the coupling fibers and the sensor chip to be 4.8 dB, which is significantly better than the grating coupler approach in [1], where the insertion loss was 29 dB. Moreover, the use of edge couplers kept the active surface of the chip free of optical interconnects, facilitating and simplifying the attachment of the MFM. Lastly, the fabricated wafer was diced into 2 cm × 2 cm chips using a recipe optimized for producing smooth facets in the SU-8 spot-size converters.

#### 3.2. Microfluidic module fabrication and chip assembly

The microfluidic subsystem was designed to allow reversible mounting of both the sensor chip and the microfluidic module (MFM) and to assure the alignment of the microchannels with the on-chip sensors with ~0.05 mm placement accuracy. This is obtained by using a metallic holder designed to tightly fit into a mechanical clamping system (MEC). We have specifically designed and fabricated the MFM and MEC for this purpose. The 2 × 2 cm diced chip with constant dimensions is placed into a shallow recess machined into a metallic base. In parallel the MFM has also fixed lateral dimensions (20 × 14 mm) and is placed into a metallic centering part which in turn is inserted, exactly fitting into the base part. Microchannels height was fixed to 200 µm while the channel width over the sensor area is 350 µm (cross-section 200 × 350 µm). This conception allows each plasmonic sensing structure to be aligned in the center of its designated channel. Microchannels are molded into polydimethylsiloxane (PDMS) onto a glass window support which itself is supported by a specific metal holder. Glass was chosen for both reasons of transparency and rigidity. The transparency facilitates a visual control of the correct contact of the surface of the MFM with the chip, to avoid potential leaks of liquid.

The MFM is fabricated according to the following steps. A metallic master mold is micro-machined according to the predefined 8-channels design and is submitted to an anti-sticking
surface treatment to ease unmolding. The glass window with drilled inlets and outlets holes is first mounted and glued onto the metallic holder. The glass and metal holder are submitted to O₂ plasma activation in order to promote adhesion of PDMS onto the glass window. PDMS (Sylgard 184 commercial kit) is classically prepared by mixing the prepolymer and curing agent in proportions 10:1. After weighing and degassing, it is poured onto the mold and covered with the O₂-activated metal and glass holder. After curing overnight in a 40°C oven to cross-link PDMS, the MFM is withdrawn from the mold and unwanted PDMS parts are eliminated. Connections for in/out tubing are realized either by punching holes in the PDMS or preferably in this work by integrated directly silicone tubes during molding, provided that the corresponding cylindric tube-holders are inserted into the mold. Silicone tubing with 1.5 mm outer diameter was used.

### 3.3. Surface functionalization

The surface functionalization protocol in this work was adopted from a previously published study that used the same aluminum sensing surface [26]. The initial step of surface functionalization involves a meticulous cleaning process for the biochips to remove any contamination. To succeed that, the chip is immersed in isopropanol for 30 minutes and dried with the use of nitrogen gas (N₂). After the cleaning process, the surface is activated by immersing the chip in a 0.1 M HCl aqueous solution for 10 minutes. Subsequently, it is rinsed with deionized water and dried with N₂. To introduce the required cross-linkers the chip is incubated in a 5% solution of 3-AminoPropyl Triethoxysilane (APTES) in 95% ethanol for 2 hours at room temperature. Following this, the chip is washed with a 95% ethanol aqueous solution and dried using nitrogen gas flow. The surface is then thermally treated in an oven at 110°C for 1 hour, leading to the exposure of reactive amino groups (-NH₂) on the chip surface. Further treatment of these amino-reactive surfaces, take place using 5% v/v glutaraldehyde solution in Phosphate-Buffered Saline (PBS) with a pH of 7.4 for 1 hour at room temperature. Following this treatment, the surface is rinsed with PBS and deionized water and dried using N₂ flow. Glutaraldehyde is employed to create an aldehyde-terminated surface, facilitating the reaction of amine groups by forming imines. Subsequently, the sensing areas are spotted with 100 µg/ml of antibody solution (Anti-CRP) and the chip is incubated in a humid environment for 1 hour at room temperature. Finally, the chip with the antibody-spotted areas undergoes a blocking procedure by rinsing with PBS and blocking with 1% BSA in PBS solution for 1 hour. Figure 6, shows the APTES functionalization of aluminum surfaces and subsequent conjugation of antibodies with the use of the glutaraldehyde crosslinkers.

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Schematic representation of APTES functionalization of aluminum surfaces and subsequent conjugation of antibodies with the use of the homobifunctional crosslinker glutaraldehyde.</figcaption></figure>

## 4. Experimental setup and results

### 4.1. Experimental setup

After completing the functionalization process, the chip is mounted on an evaluation board, providing access for microfluidics (MF) and for fiber-to-chip alignment. Polarized light was
generated using a SANTEC TSL-550 tunable laser source and guided through single-mode
polarization-maintaining fibers (THORLABS P3-1310PM-FC-2), eventually coupled to the
sensor chip via edge coupler structures. On the output side of the chip, light was coupled into a
single mode (SM) fiber and guided to an optical power meter (SANTEC MPM-210). The Sensor
spectrum was acquired by sweeping the wavelength of the tunable laser source from 1480 nm to
1630 nm. Two piezo-controlled micro-positioners (Newport M-652) were employed for precise
alignment of optical fibers with the chip. The microfluidic module (MFM) and the sensor-chip
were mounted into a common mechanical clamping system (MEC). A syringe pump (Cetoni
neMESYS 290N) provided a constant flow to the fluidic channels using Polytetrafluoroethylene
(PTFE) tubes connected to the MFM, while a rotary valve allowed for the selection of input
solution. The clamping setup with the MFM, sensor chip and the mounted fiber coupling system
are shown in Fig. 7(a).

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** (a) Top view of the sensor chip experimental setup and (b) spectrum of the sensor under test.</figcaption></figure>

In this work, the total spectrum spans from 1480 nm to 1580 nm, with a resolution of 10⁻³ nm. To eliminate spectral noise, a moving average filtering algorithm and a finite impulse response filtering algorithm (FIR AL) were applied to the acquired sensor's spectrum response. The moving average filter uses a window size of 2500 points, where each point represents 10⁻³ nm. For the FIR filter, a band-pass filter was employed with normalized frequencies (f_n) based on the number of samples per optical response (10⁵). This band-pass filter had upper and lower limits corresponding to the normalized samples frequency of the sensor's FSR and the edge coupler response, respectively. The programming and data processing were performed using Python, utilizing the SciPy and Pandas libraries.

The wavelength shift was determined by tracking the wavelength that corresponds to the
minimum of resonance dip in sensor spectral output. Initially, a baseline wavelength was recorded
and then the wavelength shift was monitored during experiments.

Figure 7(b), displays the spectrum of the proposed sensor’s characterization. The gray line represents the raw data acquired from the experimental setup, revealing interference ripples originating from the edge couplers. Furthermore, due to the high ER value, the resonance dip exceeds the power limits of the optical power meter. To mitigate this, the smoothing and filtering algorithm is implemented. The smoothed spectrum (red curve) shows an insertion loss (IL) of 14.5 dB, originating from input/output couplers and the plasmonic waveguide losses. The IL of edge coupler is 4.8 dB. The proposed sensor exhibits an ER of 55.5 dB from the fitting curve (red curve), and a free spectral range (FSR) of 51 nm. These characterized values are very comparable to those calculated from the interconnect simulations.
## 4.2. Stability experiment

Signal stability measurements were conducted for assessing the thermal stability of the proposed sensor. More specifically, the experiment took place in ambient conditions, as the two plasmonic transducers (air cladded) are fully exposed to the surrounding temperature variations for 16 min. A temperature sensor, close to the sensor surface, is used to monitor temperature variations. Figure 8(a), presents the acquired signal over time, indicating a $4.65×10^{-3}$ nm of wavelength shift as the ambient temperature fluctuates between 26.4 °C and 25.47 °C. This shift corresponds to a thermal dependance of $-5×10^{-3}$ nm/°C.

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** The sensorgram (a) air stability experiment for the proposed dual plasmonic approach and (b) air stability experiment for VOA sensor configuration from [1], and (c) for CRP surface saturation experiment with the proposed dual plasmonic approach (inset figure shows the Δλ while flowing the buffer above the sensing and reference transducers for 400 sec).</figcaption></figure>

Additionally, to benchmark the robustness of the proposed sensor, a thermal stability experiment is also conducted using an MZI sensor equipped with the same sensing transducer while employing a VOA in the reference arm for power balancing the MZI instead of an identical plasmonic stripe as reported in [1]. It should be noted that both sensors under test exhibit the same FSR values while they are operated without the use of a temperature control unit. The sensorgram presented in Fig. 8(b) shows that the VOA-based sensor follows temperature fluctuations, revealing a thermal dependance of $250×10^{-3}$ nm/°C, which is significantly higher compared to values obtained with the proposed balanced sensor.

The profound reduction in thermal dependency between the proposed sensor and the one with the VOA configuration can be attributed to the fact that in the proposed sensor, the upper and lower arms of the MZI share identical geometry and dimensions, with the exception of a short additional photonic waveguide length in the upper arm (extra length) to achieve the targeted FSR.
In contrast, the VOA configuration sensor features a reference arm composed entirely of photonic waveguide, which introduce a disparity in the thermo-optic coefficients between the sensing arm and the reference arm, leading to the observed thermal stability difference.

The obtained results conclusively demonstrate the immunity of the dual plasmonic MZI sensor approach against ambient temperature fluctuations due to the almost perfectly balanced nature of this configuration suppressing the peak-to-peak amplitude of the wavelength thermal dependance by 50 times, from 250×10⁻³ nm (Fig. 8(b)) down to 4.65×10⁻³ nm (Fig. 8(a)).

The source of this thermal interference, as previously explained, originates from the additional length in the MZI. By reducing this extra length by half, the free spectral range (FSR) is doubled, resulting in a bulk sensitivity of 4583 nm/RIU while maintaining the same Δλ obtain from the thermal dependency and extending the dynamic range of the sensor.

### 4.3. CRP detection in buffer experiment

To demonstrate the functionality of the proposed sensor in detecting protein biomarkers, a detection experiment was conducted using CRP as a use case. The Al surface of the sensing transducer was functionalized and activated according to the protocols described in the functionalization section, while human anti-CRP antibodies (Invitrogen MA1-10320) were immobilized on the modified surface as biorecognition elements. Tris-Buffered Saline with 0.1% Tween and Bovine Serum Albumin (TBST/BSA) sample was prepared as the buffer medium. For the preparation of CRP samples, the stock CRP obtained from Sigma-Aldrich (Sigma-Aldrich C4063) was spiked in the buffer medium to obtain a final CRP concentration of 1 µg/mL.

The obtained sensor response to CRP binding over time (sensorgram) is presented in Fig. 8(c). Initially, the buffer solution is injected into the system (step (I)) to obtain a baseline. The inset figure shows the Δλ while flowing the buffer above the sensing and reference transducers for 400 sec. A maximum system noise of 2×10⁻³ nm was observed, as shown in the inset of Fig. 8(c). Based on these findings, we calculate the optical limit of detection (LoD) of proposed sensor as follows [27]:

$$LoD = \frac{3\sigma}{m} \qquad (3)$$

where σ is considered as the overall system noise. Consequently, the calculated LoD was found equal to 2.6 ×10⁻⁶ RIU.

In Step II, a spiked CRP sample with concentration of 1 µg/ml is flowed for 20 minutes saturating the sensing surface with CRP and resulting in absolute wavelength shift of 1 nm which is closely matching the numerical simulations results. The slight deviation between the simulation and experimental results can be attributed to several factors such as sample concentration, binding kinetics, antibody loading on the surface and antibody orientation [28–30]. All samples were flown through the MFM using a flow rate of 20 µl/min.

To highlight the performance of the proposed sensor and compare it with state-of-the-art compact hybrid plasmo-photonic waveguide transducers from the literature, we introduce a new figure of merit (FoM). This FoM is defined as the bulk sensitivity (nm/RIU) per unit length of the sensing transducer (µm), multiplied by the extinction ratio (dB), and normalized by the thermal dependency (nm/°C). The sensitivity per unit length reflects the need for small sensors allowing increased density of sensors per chip for enhanced multiplexing and smaller sample volume. The inclusion of the extinction ratio (ER) in our FoM emphasizes its impact on sensor performance. As noted in [2], increasing the ER reduces the 3 dB bandwidth, thereby improving the optical LoD. Moreover, the thermal dependency (Td) expresses the sensor’s stability which also affects optical LoD. These factors collectively provide a more comprehensive evaluation of the sensor’s performance. Table 1 presents a comparison between the proposed sensor and state of the art photonic/plasmonic waveguide sensors from the literature, with a specific focus on parameters such as bulk sensitivity, ER, CMOS compatibility, transducer footprint, Td, bulk sensitivity per
transducer length ($\frac{m}{L}$), and FoM. As shown in the comparative table, the proposed sensor stands out having the highest FoM due to its exceptional sensitivity per unit length, ER and lowest T<sub>d</sub> while maintaining the CMOS compatibility platform.

<table><caption>**Table 1.** Comparison between the proposed sensor and state of the art sensors from the literature</caption><thead><tr><th>Ref.</th><th>M (nm/RIU)</th><th>ER (dB)</th><th>CMOS metal</th><th>Transducer footprint [W × L] (μm²)</th><th>T<sub>d</sub> (×10<sup>-3</sup> nm/°C)</th><th>m/L</th><th>FoM [(m×ER)/L×Td]</th></tr></thead><tbody><tr><td>[4]</td><td>2761</td><td>35</td><td>Au (X)</td><td>7 × 70</td><td>250</td><td>39.4</td><td>5.52E + 03</td></tr><tr><td>[1]</td><td>4764</td><td>35</td><td>Al (✓)</td><td>7 × 70</td><td>250</td><td>68</td><td>9.53E + 03</td></tr><tr><td>[15]</td><td>157.4</td><td>40</td><td>Au (X)</td><td>4 × 40</td><td>15</td><td>7.87</td><td>1.05E + 04</td></tr><tr><td>[2]</td><td>1930</td><td>35</td><td>Au (X)</td><td>7 × 70</td><td>125</td><td>27.57</td><td>7.72E + 03</td></tr><tr><td>[31]</td><td>437.3</td><td>30</td><td>Ag (X)</td><td>0.740 × 40</td><td>244</td><td>10.93</td><td>1.34E + 03</td></tr><tr><td>[14]</td><td>1061</td><td>4</td><td>Ag (X)</td><td>2.5 × 40</td><td>-</td><td>26</td><td>-</td></tr><tr><td>This work</td><td>2322</td><td>55.5</td><td>Al (✓)</td><td>7 × 70</td><td>5</td><td>33.17</td><td>3.68E + 05</td></tr></tbody></table>

## 5. Surface sensitivity investigation for diverse target analytes

The robust alignment observed among the $\Delta\lambda$ in the sensor transmission spectrum from simulation, the analytical Eq. (2), and the experimental results provides a solid basis for expanding the detection capabilities of the proposed sensor scheme to a diverse portfolio of heterogeneous target analytes apart from proteins. Equation (1) indicates that surface sensitivity is directly connected with the size of the target analyte. The proposed sensor possesses the capability to efficiently recognize and detect a wide range of target analytes, including proteins (CRP), virus (Covid-19 coronavirus), bacteria (E. coli), and fungi (Candida albicans). This capability is attributed to the relatively substantial decay length of the SPP mode of 2000nm, which matches well with the size of the different target analytes. Table 2 outlines the calculated surface sensitivity values corresponding to each target, considering their maximum physical dimensions based on Eq. (1). For Covid-19 detection, the surface sensitivity is equal to 240 nm/RIU, considering the target as a spherical particle with a diameter of 110 nm [32]. E. coli bacteria, are typically rounded rods with a length of 2000nm and a maximum diameter of 1000 nm [33], exhibit a surface sensitivity in the range of 1470 nm/RIU to 2007nm/RIU depending on their binding orientation. Finally, in the case of Candida albicans, with an average size of 5000 nm [34], the analytically calculated surface sensitivity extends up to 2306 nm as shown in Fig. 9.

<table><caption>**Table 2.** Outlines the surface sensitivity values associated with each pathogen, alongside their physical dimensions and refractive indices.</caption><thead><tr><th>Pathogen</th><th>Thickness (nm)</th><th>Surface sensitivity (nm/RIU)</th></tr></thead><tbody><tr><td>Covid 19 [32]</td><td>110</td><td>241</td></tr><tr><td>E. coli [33]</td><td>2000</td><td>2007</td></tr><tr><td>Candida albicans [34]</td><td>5000</td><td>2306</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Surface sensitivity (S) as a function of bio-layer thickness.</figcaption></figure>

**6. Conclusion**

In this work, we have developed a plasmonic augmented photonic MZI-based sensor with two identical transducers positioned in both arms of the interferometer. ER values of more than 55 dB were experimentally measured, validating the balanced nature of this configuration, effectively improving overall sensor performance. Furthermore, the proposed sensor exhibits extraordinary thermal stability which was validated through experimental data. Additionally, experimental results for CRP detection revealed surface saturation in 20 min with a maximum wavelength shift of 1 nm, closely matching the theoretical and simulation results. In terms of sensitivity, the sensor demonstrates notable bulk sensitivity of 2322 nm/RIU, along with a significant surface sensitivity for diverse target analytes, reaching up to 2306 nm/RIU. In conclusion, the proposed balanced sensor configuration employs ultra-small sensing transducers and exploits an inherent self-referencing scheme, eliminating any requirement for a separate on-chip reference sensor or costly temperature control modules, simplifying diagnostic systems while boosting performance of photonic sensors. Overall, the unique performance credentials offered by the proposed sensor technology introduces a scalable platform for fast, label-free and cost-effective detection of heterogeneous target analytes on a single chip, addressing the unmet needs in numerous applications.

**Funding.** Horizon 2020 Framework Programme (101007448 (GRACED), 101135435 (MultiLab)); Agence Nationale de la Recherche (ANR-21-ESRE-0040, EIPHI Graduate School ANR-17-EURE-0002); European Regional Development Fund (FEDER-FSE Bourgogne Franche-Comté 2021/2027); Centre National de la Recherche Scientifique (RENATECH+).

**Acknowledgment.** We would like to thank Prof. Elefterios Lidorikis, from the department of Materials Science and Engineering at University of Ioannina, for the fruitful discussions.

**Disclosures.** The authors declare no conflicts of interest.

**Data availability.** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

**References**

1. A. Manolis, E. Chatzianagnostou, G. Dabos, et al., "Ultra-sensitive refractive index sensor using CMOS plasmonic transducers on silicon photonic interferometric platform," *Opt. Express* **28**(14), 20992 (2020).

2. A. Manolis, E. Chatzianagnostou, G. Dabos, et al., "Plasmonics co-integrated with silicon nitride photonics for high-sensitivity interferometric biosensing," *Opt. Express* **27**(12), 17102 (2019).

3. E. Chatzianagnostou, D. Ketzaki, G. Dabos, et al., "Design and Optimization of Open-cladded Plasmonic Waveguides for CMOS Integration on Si3N4 Platform," *Plasmonics* **14**(4), 823–838 (2019).

4. E. Chatzianagnostou, A. Manolis, G. Dabos, et al., "Scaling the Sensitivity of Integrated Plasmo-Photonic Interferometric Sensors," *ACS Photonics* **6**(7), 1664–1673 (2019).
5. B. Sepúlveda, J. S. del Río, M. Moreno, et al., "Optical biosensor microsystems based on the integration of highly sensitive Mach–Zehnder interferometer devices," J. Opt. A Pure Appl. Opt. 8(7), S561–S566 (2006).

6. Q. Liu, X. Tu, K. W. Kim, et al., "Highly sensitive Mach–Zehnder interferometer biosensor based on silicon nitride slot waveguide," Sens. Actuators, B 188, 681–688 (2013).

7. A. Psarouli, A. Botsialas, A. Salapatas, et al., "Fast label-free detection of C-reactive protein using broad-band Mach-Zehnder interferometers integrated on silicon chips," Talanta 165, 458–465 (2017).

8. M. J. Goodwin, G. A. J. Besselink, F. Falke, et al., "Highly Sensitive Protein Detection by Asymmetric Mach-Zehnder Interferometry for Biosensing Applications," ACS Appl. Bio Mater. 3(7), 4566–4572 (2020).

9. M. R. Bryan, J. N. Butt, J. Bucukovski, et al., "Biosensing with Silicon Nitride Microring Resonators Integrated with an On-Chip Filter Bank Spectrometer," ACS Sens. 8(2), 739–747 (2023).

10. Q. Qiao, J. Xia, C. Lee, et al., "Applications of Photonic Crystal Nanobeam Cavities for Sensing," Micromachines 9(11), 541 (2018).

11. E. Luan, H. Yun, M. Ma, et al., "Label-free biosensing with a multi-box sub-wavelength phase-shifted Bragg grating waveguide," Biomed. Opt. Express 10(9), 4825 (2019).

12. S. H. Badri, S. SaeidNahaei, and J. S. Kim, "Hybrid plasmonic slot waveguide with a metallic grating for on-chip biosensing applications," Appl. Opt. 60(25), 7828 (2021).

13. Q. Ji, R. Lei, S. Liu, et al., "Highly sensitive based on a Mach-Zehnder interferometer with double-slot hybrid plasmonic waveguide," Optik 270, 169995 (2022).

14. X. Sun, D. Dai, L. Thylén, et al., "High-sensitivity liquid refractive-index sensor based on a Mach-Zehnder interferometer with a double-slot hybrid plasmonic waveguide," Opt. Express 23(20), 25688 (2015).

15. X. Sun, L. Thylén, and L. Wosinski, "Hollow hybrid plasmonic Mach–Zehnder sensor," Opt. Lett. 42(4), 807 (2017).

16. "Lumerical Inc.," (sn.d.).

17. E. D. Palik, Handbook of Optical Constants of Solids (Academic Press, 1985).

18. K. Luke, Y. Okawachi, M. R. E. Lamont, et al., "Broadband mid-infrared frequency comb generation in a Si_3N_4 microresonator," Opt. Lett. 40(21), 4823 (2015).

19. A. Manolis, P. J. Cegielski, L. Markey, et al., "Bringing Plasmonics Into CMOS Photonic Foundries: Aluminum Plasmonics on Si₃N₄ for Biosensing Applications," J. Lightwave Technol. 37(21), 5516–5524 (2019).

20. F. Zhang, J. Martin, and J. Plain, "Long-term stability of plasmonic resonances sustained by evaporated aluminum nanostructures," Opt. Mater. Express 9(1), 85 (2019).

21. R. W. Revie, ed., *Uhlig's Corrosion Handbook* (Wiley, 2011).

22. J. Li, J. Ye, C. Chen, et al., "Revisiting the Surface Sensitivity of Nanoplasmonic Biosensors," ACS Photonics 2(3), 425–431 (2015).

23. J. Wu, L. Liang, M. Zhang, et al., "Single-Molecule Identification of the Conformations of Human C-Reactive Protein and Its Aptamer Complex with Solid-State Nanopores," ACS Appl. Mater. Interfaces 14(10), 12077–12088 (2022).

24. S. Kastner, P. Pritzke, A. Csáki, et al., "The effect of layer thickness and immobilization chemistry on the detection of CRP in LSPR assays," Sci. Rep. 12(1), 836 (2022).

25. M. J. Pollitt, G. Buckton, R. Piper, et al., "Measuring antibody coatings on gold nanoparticles by optical spectroscopy," RSC Adv. 5(31), 24521–24527 (2015).

26. A. Manolis, C. Eleftheriou, M. A. Elrabiaey, et al., "Ultra-fast detection of pathogens and protein biomarkers using a low-cost silicon plasmonic biosensing platform," Sens. Actuators Rep. 8, 100221 (2024).

27. Í. Molina-Fernández, J. Leuermann, A. Ortega-Moñux, et al., "Fundamental limit of detection of photonic biosensors with coherent phase read-out," Opt. Express 27(9), 12616 (2019).

28. S. Sathish and A. Q. Shen, "Toward the Development of Rapid, Specific, and Sensitive Microfluidic Sensors: A Comprehensive Device Blueprint," JACS Au 1(11), 1815–1833 (2021).

29. B. Saha, T. H. Evers, and M. W. J. Prins, "How antibody surface coverage on nanoparticles determines the activity and kinetics of antigen capturing for biosensing," Anal. Chem. 86(16), 8158–8166 (2014).

30. J.-H. Qu, S. Horta, F. Delport, et al., "Expanding a Portfolio of (FO-) SPR Surface Chemistries with the Co(III)-NTA Oriented Immobilization of His 6 -Tagged Bioreceptors for Applications in Complex Matrices," ACS Sens. 5(4), 960–969 (2020).

31. S. Ghosh and B. M. A. Rahman, "Design of on-chip hybrid plasmonic Mach-Zehnder interferometer for temperature and concentration detection of chemical solution," Sens. Actuators, B 279, 490–502 (2019).

32. Z. Varga, A. J. Flammer, P. Steiger, et al., "Electron microscopy of SARS-CoV-2: a challenging task – Authors' reply," Lancet 395(10238), e100 (2020).

33. S. A. Taya, D. N. Alhamss, I. Colak, et al., "Sensitivity enhancement of an optical sensor based on a binary photonic crystal for the detection of Escherichia coli by controlling the central wavelength and the angle of incidence," Opt. Quantum Electron. 54(2), 127 (2022).

34. F. Cottier and R. A. Hall, "Face/Off: The Interchangeable Side of Candida Albicans," Front. Cell. Infect. Microbiol. 9, 1 (2020).