High-quality dielectric/Ge interface and low gate leakage current are crucial issues for high-performance nanoscaled Ge-based complementary metal–oxide–semiconductor(CMOS) device. In this paper, the interfacial and ...High-quality dielectric/Ge interface and low gate leakage current are crucial issues for high-performance nanoscaled Ge-based complementary metal–oxide–semiconductor(CMOS) device. In this paper, the interfacial and electrical properties of high-k Hf Gd ON/La Ta ON stacked gate dielectric Ge metal–oxide–semiconductor(MOS) capacitors with different gadolinium(Gd) contents are investigated. Experimental results show that when the controlling Gd content is a suitable value(e.g., 13.16%), excellent device performances can be achieved: low interface-state density(6.93 × 10^11 cm^-2·e V-1), small flatband voltage(0.25 V), good capacitance–voltage behavior, small frequency dispersion, and low gate leakage current(2.29× 10^-6 A/cm^2 at Vg = Vfb + 1 V). These could be attributed to the repair of oxygen vacancies, the increase of conduction band offset, and the suppression of germanate and suboxide Ge Ox at/near the high k/Ge interface by doping suitable Gd into Hf ON.展开更多
Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future pandemics.We propose an optical method for directly imaging unlabel...Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future pandemics.We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification.An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity.Pairing these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural network.The trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 vs.the other viruses with 96%accuracy.The inference time for each image is 60 ms,on a common graphic-processing unit.This approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per patient.As the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath condensates.The necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2018YFB2200500)the National Natural Science Foundation of China(Grant Nos.61851406 and 61274112)
文摘High-quality dielectric/Ge interface and low gate leakage current are crucial issues for high-performance nanoscaled Ge-based complementary metal–oxide–semiconductor(CMOS) device. In this paper, the interfacial and electrical properties of high-k Hf Gd ON/La Ta ON stacked gate dielectric Ge metal–oxide–semiconductor(MOS) capacitors with different gadolinium(Gd) contents are investigated. Experimental results show that when the controlling Gd content is a suitable value(e.g., 13.16%), excellent device performances can be achieved: low interface-state density(6.93 × 10^11 cm^-2·e V-1), small flatband voltage(0.25 V), good capacitance–voltage behavior, small frequency dispersion, and low gate leakage current(2.29× 10^-6 A/cm^2 at Vg = Vfb + 1 V). These could be attributed to the repair of oxygen vacancies, the increase of conduction band offset, and the suppression of germanate and suboxide Ge Ox at/near the high k/Ge interface by doping suitable Gd into Hf ON.
基金This research is supported by National Institute of Biomedical Imaging and Bioengineering(NIBIB)supplemental grant#3R01 CA238191-02S1,National Institutes of Health(R01GM129709)National Science Foundation(0939511,1450962,1353368)(awarded to G.P.)+3 种基金EPA/USDA 2017-39591-27313(awarded to T.H.N.)National Science Foundation NSF-DMR 2004719(awarded to H.J.K.)R.B.and E.V.acknowledge the support of NSF Rapid Response Research(RAPID)grant(Award 2028431)the support of Jump Applied Research through Community Health through Engineering and Simulation(ARCHES)endowment through the Health Care Engineering Systems Center at UIUC.
文摘Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future pandemics.We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification.An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity.Pairing these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural network.The trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 vs.the other viruses with 96%accuracy.The inference time for each image is 60 ms,on a common graphic-processing unit.This approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per patient.As the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath condensates.The necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.
