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High performance indium oxide nanoribbon FETs: mitigating devices signal variation from batch fabrication.

ABSTRACT: Nanostructured field effect transistor (FET) based sensors have emerged as a powerful bioanalytical technology. However, performance variations across multiple devices and between fabrication batches inevitably exist and present a significant challenge holding back the translation of this cutting-edge technology. We report an optimized and cost-effective fabrication process for high-performance indium oxide nanoribbon FET with a steep subthreshold swing of 80 mV per decade. Through systematic electrical characterizations of 57 indium oxide nanoribbon FETs from different batches, we demonstrate an optimal operation point within the subthreshold regime that mitigates the issue of device-to-device performance variation. A non-linear pH sensing of the fabricated indium oxide nanoribbon FETs is also presented.

SUBMITTER: Pham TTT

PROVIDER: S-EPMC9418870 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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High performance indium oxide nanoribbon FETs: mitigating devices signal variation from batch fabrication.

Pham Thuy Thi Thanh TTT Tran Duy Phu DP Thierry Benjamin B

Nanoscale advances 20191105 12

Nanostructured field effect transistor (FET) based sensors have emerged as a powerful bioanalytical technology. However, performance variations across multiple devices and between fabrication batches inevitably exist and present a significant challenge holding back the translation of this cutting-edge technology. We report an optimized and cost-effective fabrication process for high-performance indium oxide nanoribbon FET with a steep subthreshold swing of 80 mV per decade. Through systematic el ...[more]

PMID: 36133115

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Project description:BackgroundLarge sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.ResultsWe describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.ConclusionsResearchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.

Dataset Information

High performance indium oxide nanoribbon FETs: mitigating devices signal variation from batch fabrication.

Publications

High performance indium oxide nanoribbon FETs: mitigating devices signal variation from batch fabrication.

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