
READ ME File For 'Dataset for Manipulation of random telegraph signals in a silicon nanowire transistor with a triple gate'

Dataset DOI: 10.5258/SOTON/D0555

ReadMe Author: Kouta Ibukuro, University of Southampton

This dataset supports the publication:
AUTHORS: Liu, F. et al (2018). . Nanotechnology. 
TITLE: Manipulation of random telegraph signals in a silicon nanowire transistor with a triple gate
JOURNAL: Nanotechnology
PAPER DOI: https://doi.org/10.1088/1361-6528/aadfa6


This dataset contains:
All figures that appear in the publication in the folder ‘Figures’.
Raw data for measurement for each figure in the folder ‘Raw_data’.

Figures in the publication (Figure 1,2,…7) correspond to the files in the folder ‘Figures’ as follows;

Figure 1; Picture101-eps-converted-to.pdf
Figure 2; Picture102-eps-converted-to.pdf
Figure 3; Picture103-eps-converted-to.pdf
Figure 4; Picture104-eps-converted-to.pdf
Figure 5; Picture105-eps-converted-to.pdf
Figure 6; Picture206.pdf
Figure 7; Picture207.pdf

Figure 2,3,4,5,6,7 were generated based on the experimental data, whose raw data are available in the folder ‘raw_data’.
Below are explanations of how each figure was generated using the raw data.

Figure 2  
(a) The following data were used;
prsw_TG_p15 [(1) ; 03_05_2018 11_58_52].csv
prsw_TG_p14 [(1) ; 03_05_2018 11_57_54].csv
prsw_TG_p12 [(1) ; 03_05_2018 11_56_56].csv
prsw_TG_p10 [(1) ; 03_05_2018 11_55_58].csv
prsw_TG_p08 [(1) ; 03_05_2018 11_54_59].csv
prsw_TG_p06 [(1) ; 03_05_2018 11_54_01].csv
prsw_TG_p04 [(1) ; 03_05_2018 11_53_03].csv
prsw_TG_p02 [(1) ; 03_05_2018 11_52_05].csv
prsw_TG_0_[(1); 03_05_2018 11_28_03].csv
prsw_TG_n02 [(1) ; 03_05_2018 11_50_09].csv
prsw_TG_n04 [(1) ; 03_05_2018 11_49_11].csv
prsw_TG_n06 [(1) ; 03_05_2018 11_48_13].csv
prsw_TG_n08 [(1) ; 03_05_2018 11_47_15].csv
prsw_TG_n10 [(1) ; 03_05_2018 11_46_17].csv
prsw_TG_n12 [(1) ; 03_05_2018 11_45_19].csv
prsw_TG_n14 [(1) ; 03_05_2018 11_44_21].csv
prsw_TG_n15 [(1) ; 03_05_2018 11_43_22].csv

This figure is generated by plotting Drain_I(column G, row 217-end) against Gate_V(column B, row 217-end) for all csv file, superimposed in a single figure.
Matlab was used to generate plots.
Microsoft powerpoint was used to add dotted circle.

(b) The following data was used;

prsw_TG_0_[(1); 03_05_2018 11_28_03].csv

This figure is generated by plotting Drain_I against Gate_V.
Gate_V was 0.25V to 0.4V.
This figure is roughly equivalent in the area in (a) where the dotted circle encloses.
Matlab was used to generate a plot.
Microsoft powerpoint was used to add texts and arrows.

(c) The following data was used;

W4ChipA1X02Y02XX02YY01_TFG_TG0_BFGfloating_5prober.xslx

This figure is generated by plotting Drain_I(column G, row 222-end) against FG_V(column B, row 222-end).
Matlab was used to generate plots.

(d) The following data was used;

W4ChipA1X02Y02XX02YY01_BFG_TG0_TFGfloating_5prober.xslx

This figure is generated by plotting Drain_I(column G, row 222-end) against FG_V(column B, row 222-end).
Matlab was used to generate plots.

Figure 3
(a) The following data was used;

I_V-t Sampling FG 0 TG p040 [(1) ; 04_05_2018 04_38_10].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.

(b) The following data was used;

I_V-t Sampling FG 0 TG p035 [(1) ; 04_05_2018 04_22_42].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.
Matlab was used to generate a plot.


(c) The following data was used;

I_V-t Sampling FG 0 TG p030 [(1) ; 04_05_2018 04_07_17].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.
Matlab was used to generate a plot.

(d) The following data was used;

I_V-t Sampling FG 0 TG p025 [(1) ; 04_05_2018 03_51_40].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.
Matlab was used to generate a plot.

(e) The following data was used;

I_V-t Sampling FG 0 TG p020 [(1) ; 04_05_2018 03_36_18].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.
Matlab was used to generate a plot.

(f) The following data was used;

I_V-t Sampling FG 0 TG p015 [(1) ; 04_05_2018 03_20_53].csv

This figure is generated by plotting Drain_I(column F, row 218-end) against Time (column F, row 218-end) and limiting Time from 400 to 500.
The limitation is introduced to make the RTS events clearer.
If Drain_I is plotted against time from beginning to end, the RTS events are not seen clearly.
Matlab was used to generate a plot.

(g) The following data was used;

I_V-t Sampling FG 0 TG p040 [(1) ; 04_05_2018 04_38_10].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(h) The following data was used;

I_V-t Sampling FG 0 TG p035 [(1) ; 04_05_2018 04_22_42].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(i) The following data was used;

I_V-t Sampling FG 0 TG p030 [(1) ; 04_05_2018 04_07_17].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(j) The following data was used;

I_V-t Sampling FG 0 TG p025 [(1) ; 04_05_2018 03_51_40].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(k) The following data was used;

I_V-t Sampling FG 0 TG p020 [(1) ; 04_05_2018 03_36_18].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(l) The following data was used;

I_V-t Sampling FG 0 TG p015 [(1) ; 04_05_2018 03_20_53].csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(m) This figure is based on (g)-(l) and therefore the same data were used.
This figure is generated by plotting n_high and n_low against Top gate voltage.
The definitions of n_high and n_low are clearly defined in the main text, as the equations (1) and (2).

Figure 4

The following data was used;

I_V-t Sampling FG 0 TG p030 [(1) ; 04_05_2018 04_07_17].csv

This plot was generated by plotting differential drain current against its probability, using Matlab function histcounts.
The definition of differential drain current is clearly defined in the main text, page 5.

Figure 5

(a) This figure is based on Figure 2(g)-(l) and therefore the same data were used.
This figure is generated by plotting tau_high and tau_low against Top gate voltage.
The definitions of tau_high and tau_low are clearly defined in the main text, as the equations (3) and (4).
Matlab was used to generate a plot.

(b) This figure is based on Figure 2(g)-(l) and therefore the same data were used.
This figure is generated by plotting tau_high/tau_low against top gate voltage.
Matlab was used to generate a plot.

Figure 6

(a) The following data were used;

x02y03xx02yy01_timesampling_TG50m_FGn16.csv 
x02y03xx02yy01_timesampling_TG50m_FGn14.csv 
x02y03xx02yy01_timesampling_TG50m_FGn12.csv 
x02y03xx02yy01_timesampling_TG50m_FGn10.csv
x02y03xx02yy01_timesampling_TG50m_FGn08.csv
x02y03xx02yy01_timesampling_TG50m_FGn06.csv
x02y03xx02yy01_timesampling_TG50m_FGn04.csv
x02y03xx02yy01_timesampling_TG50m_FGn02.csv
x02y03xx02yy01_timesampling_TG50m_FG000.csv
x02y03xx02yy01_timesampling_TG50m_FGp02.csv
x02y03xx02yy01_timesampling_TG50m_FGp04.csv


This figure is generated by plotting Drain_I(column G, row 218-end) against Time(column F, row 217-end) for all csv file, superimposed in a single figure, with Time limited from 400s to 900s to focus on RTS events.


(b) The following data was used;

x02y03xx02yy01_timesampling_TG50m_FGp04.csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(c) The following data was used;

x02y03xx02yy01_timesampling_TG50m_FGn04.csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(d) The following data was used;

x02y03xx02yy01_timesampling_TG50m_FGn08.csv

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

(e) The following data was used;

x02y03xx02yy01_timesampling_TG50m_FGn14.csv 

This figure is generated by plotting Probability against Drain_I, using Matlab function ‘histcounts’.

Figure 7

(a) This figure is based on Figure 2(g)-(l) and therefore the same data were used.

This plot was generated by plotting energy level against top gate voltage.
The definition of energy level is clearly defined in the main text as the equation (12).

(e) This figure is based on Figure 6(a) and therefore the same data were used.

This plot was generated by plotting energy level against top gate voltage.
The definition of energy level is clearly defined in the main text as the equation (12).


Date of data collection: 
21st Sep 2017
W4ChipA1X02Y02XX02YY01_TFG_TG0_BFGfloating_5prober.xslx
W4ChipA1X02Y02XX02YY01_BFG_TG0_TFGfloating_5prober.xslx

9th Apr 2018
x02y03xx02yy01_timesampling_TG50m_FGn16.csv 
x02y03xx02yy01_timesampling_TG50m_FGn14.csv 
x02y03xx02yy01_timesampling_TG50m_FGn12.csv 
x02y03xx02yy01_timesampling_TG50m_FGn10.csv
x02y03xx02yy01_timesampling_TG50m_FGn08.csv
x02y03xx02yy01_timesampling_TG50m_FGn06.csv
x02y03xx02yy01_timesampling_TG50m_FGn04.csv
x02y03xx02yy01_timesampling_TG50m_FGn02.csv
x02y03xx02yy01_timesampling_TG50m_FG000.csv
x02y03xx02yy01_timesampling_TG50m_FGp02.csv
x02y03xx02yy01_timesampling_TG50m_FGp04.csv

3rd-4th May 2018
prsw_TG_p15 [(1) ; 03_05_2018 11_58_52].csv
prsw_TG_p14 [(1) ; 03_05_2018 11_57_54].csv
prsw_TG_p12 [(1) ; 03_05_2018 11_56_56].csv
prsw_TG_p10 [(1) ; 03_05_2018 11_55_58].csv
prsw_TG_p08 [(1) ; 03_05_2018 11_54_59].csv
prsw_TG_p06 [(1) ; 03_05_2018 11_54_01].csv
prsw_TG_p04 [(1) ; 03_05_2018 11_53_03].csv
prsw_TG_p02 [(1) ; 03_05_2018 11_52_05].csv
prsw_TG_0_[(1); 03_05_2018 11_28_03].csv
prsw_TG_n02 [(1) ; 03_05_2018 11_50_09].csv
prsw_TG_n04 [(1) ; 03_05_2018 11_49_11].csv
prsw_TG_n06 [(1) ; 03_05_2018 11_48_13].csv
prsw_TG_n08 [(1) ; 03_05_2018 11_47_15].csv
prsw_TG_n10 [(1) ; 03_05_2018 11_46_17].csv
prsw_TG_n12 [(1) ; 03_05_2018 11_45_19].csv
prsw_TG_n14 [(1) ; 03_05_2018 11_44_21].csv
prsw_TG_n15 [(1) ; 03_05_2018 11_43_22].csv
I_V-t Sampling FG 0 TG p040 [(1) ; 04_05_2018 04_38_10].csv
I_V-t Sampling FG 0 TG p035 [(1) ; 04_05_2018 04_22_42].csv
I_V-t Sampling FG 0 TG p030 [(1) ; 04_05_2018 04_07_17].csv
I_V-t Sampling FG 0 TG p025 [(1) ; 04_05_2018 03_51_40].csv
I_V-t Sampling FG 0 TG p020 [(1) ; 04_05_2018 03_36_18].csv
I_V-t Sampling FG 0 TG p015 [(1) ; 04_05_2018 03_20_53].csv


Information about geographic location of data collection: University of Southampton, U.K.

Related projects:
This work is supported by EPSRC Manufacturing Fellowship (EP/M008975/1), Lloyds Register Foundation International Consortium of Nanotechnology, and the Joint Research Project e-SI-Amp (15SIB08). This work is also supported by the European Metrology Programme for Innovation and Research (EMPIR) co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. 

Date that the file was created: [28th September, 2018]