Utilizing the superior etch stop quality of HfO2 in the front end of line wafer scale integration of silicon nanowire biosensors

Jayakumar, Ganesh; Hellström, Per‐Erik; Östling, Mikael

doi:10.1016/j.mee.2019.03.006

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The SiNW pixel sensors were fabricated on a boron doped (1×10 15 cm −3 ) 4″ (100 mm) silicon on insulator (SOI) wafer using the STL process [21,23,[31][32][33]. The detailed steps in the fabrication of the SiNW pixel sensor are shown in figure A1, available online at stacks.iop.org/NANO/30/ 225502/mmedia in the supplementary section.…”

Section: Fabricationmentioning

confidence: 99%

“…Fabrication method, substrate, device type CMOS integration Application Na Lu et al [8] TD, SOI, SiNW No miRNAs Agarwal et al [10] TD, SOI, SiNW No ss-DNA Gao et al [12] TD, SOI, SiNW No ss-DNA Kong et al [14] TD, SOI, SiNW No Cardiac troponin I (cTnI) Nguyen et al [15] BU, Bulk, SiNN-FET No DNA Zhang et al [16] TD, SOI, SiNW No miRNAs Pui et al [17] TD, SOI, SiNW No Pro-inflammatory cytokines Chen et al [18] TD, SOI, SiNW No ss-DNA Knopfmacher et al [19] TD, SOI, SiNW No pH Tarasov et al [20] TD, SOI, SiNW No pH, ions measurements were performed to demonstrate the high throughput and reproducibility of the STL fabrication process for SiNW pixel manufacturing. Further, as it is important to preserve the integrity of the SiNW sensors before and after opening access to the test site, the electrical evaluation of the SiNW pixel sensors was performed at two steps [23,32]. Figure A1(K) shows the electrical setup used to characterize the SiNW before opening access to the test site.…”

Section: Referencementioning

confidence: 99%

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Pixel-based biosensor for enhanced control: silicon nanowires monolithically integrated with field-effect transistors in fully depleted silicon on insulator technology

Jayakumar

Östling

2019

Nanotechnology

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Silicon nanowires (SiNWs) are a widely used technology for sensing applications. Complementary metal-oxide-semiconductor (CMOS) integration of SiNWs advances lab-on-chip (LOC) technology and offers opportunities for read-out circuit integration, selective and multiplexed detection. In this work, we propose novel scalable pixel-based biosensors exploiting the integration of SiNWs with CMOS in fully-depleted silicon-on-insulator technology. A detailed description of the wafer-scale fabrication of SiNW pixels using the CMOS compatible sidewall-transfer-lithography as an alternative to widely investigated time inefficient e-beam lithography is presented. Each 60 nm wide SiNWs sensor is monolithically connected to a control transistor and novel on-chip fluid-gate forming an individual pixel that can be operated in two modes: biasing transistor frontgate (VG) or substrate backgate (VBG). We also present the first electrical results of single N and P-type SiNW pixels. In frontgate mode, N and P-type SiNW pixels exhibit subthreshold slope (SS) ≈ 70–80 mV/dec and Ion/Ioff ≈ 105. The N-type and P-type pixels have an average threshold voltage, Vth of −1.7 V and 0.85 V respectively. In the backgate mode, N and P-type SiNW pixels exhibit SS ≈ 100–150 mV/dec and Ion/Ioff ≈ 106. The N and P-type pixels have an average Vth of 5 V and −2.5 V respectively. Further, the influence of the backgate and frontgate voltage on the switching characteristics of the SiNW pixels is also studied. In the frontgate mode, the Vth of the SiNW pixels can be tuned at 0.2 V for 1 V change in VBG for N-type or at −0.2 V for −1 V change in VBG for P-type pixels. In the backgate mode, it is found that for stable operation of the pixels, the VG of the N and P-type transistors must be in the range 0.5–2.5 V and 0 V to −2.5 V respectively.

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Section: Fabricationmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Pixel-based biosensor for enhanced control: silicon nanowires monolithically integrated with field-effect transistors in fully depleted silicon on insulator technology

Jayakumar

Östling

2019

Nanotechnology

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Analytical Investigation of a Split Double Gate Graded Channel Field Effect Transistor for Biosensing Applications

Singh

Dutta

2022

Silicon

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Machine Learning-Based Modeling of pH-Sensitive Silicon Nanowire (SiNW) for Ion Sensitive Field Effect Transistor (ISFET)

Ayadi,

Lale,

Hajji

et al. 2024

Sensors

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The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR). The proposed ML algorithms are trained and validated using experimental measurements of the SiNW-ISFET sensor. The results obtained show a better predictive ability of extra tree regression (ETR) compared to other techniques, with a low RMSE of 1 × 10−3 mA and an R2 value of 0.9999725. This prediction study corrects the problems associated with SiNW -ISFET sensors.

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Utilizing the superior etch stop quality of HfO2 in the front end of line wafer scale integration of silicon nanowire biosensors

Cited by 3 publications

References 23 publications

Pixel-based biosensor for enhanced control: silicon nanowires monolithically integrated with field-effect transistors in fully depleted silicon on insulator technology

Pixel-based biosensor for enhanced control: silicon nanowires monolithically integrated with field-effect transistors in fully depleted silicon on insulator technology

Analytical Investigation of a Split Double Gate Graded Channel Field Effect Transistor for Biosensing Applications

Machine Learning-Based Modeling of pH-Sensitive Silicon Nanowire (SiNW) for Ion Sensitive Field Effect Transistor (ISFET)

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