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Silicon Nanowire Field Effect Transistor Sensors with Minimal Sensor to Sensor Variations and Enhanced Sensing Characteristics.

著者 Zafar S , D'Emic C , Jagtiani A , Kratschmer E , Miao X , Zhu Y , Mo R , Sosa N , Hamann HF , Shahidi G , Riel H
ACS Nano.2018 Jun 22 ; ():.
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Silicon nanowire field effect transistor (FET) sensors have demonstrated their ability for rapid and label free detection of proteins, nucleotide sequences, and viruses at ultralow concentrations with the potential to be a transformative diagnostic technology. Their nanoscale size gives them their unique ultralow detection ability but also makes their fabrication challenging with large sensor to sensor variations, thus limiting their commercial applications. In this work, a combined approach of nanofabrication, device simulation, materials and electrical characterization is applied towards identifying and improving fabrication steps that induce sensor to sensor variations. An enhanced complementary metal-oxide-semiconductor (CMOS) compatible process for fabricating silicon nanowire FET sensors is demonstrated. Nanowire (30 nm width) FETs with aqueous solution as gates are shown to have the Nernst limit sub-threshold swing SS = 60 mV/decade with ~1.7% variations, whereas literature values for SS are ≥ 80 mV/decade with larger (>10 times) variations. Also, their threshold voltage variations are significantly (~3 times) reduced, compared to literature values. Furthermore, these improved FETs have significantly reduced drain current hysteresis (~0.6 mV) and enhanced on-current to off-current ratios (~10). These improvements resulted in nanowire FET sensors with lowest (~3%) reported sensor to sensor variations, compared to literature studies. Also, these improved nanowire sensors have the highest reported sensitivity and enhanced signal to noise ratio with the lowest reported defect density of 1x10 eVcm in comparison to literature data. In summary, this work brings the nanowire sensor technology a step closer to commercial products for early diagnosis and monitoring of diseases.
PMID: 29932634 [PubMed - as supplied by publisher]
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