CN107300629B - Scan probe scaling method - Google Patents
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- CN107300629B CN107300629B CN201710641861.1A CN201710641861A CN107300629B CN 107300629 B CN107300629 B CN 107300629B CN 201710641861 A CN201710641861 A CN 201710641861A CN 107300629 B CN107300629 B CN 107300629B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
- G01Q40/00—Calibration, e.g. of probes
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Abstract
The present invention relates to a kind of scanning probe scaling methods, demarcate for the scanning probe to scanning tunneling microscope.The described method includes: obtaining the shape appearance figure of calibration sample by the scanning probe；Judgement is standardized to the shape appearance figure by data categorization module；If the data categorization module judges that the shape appearance figure is qualified, the energy spectrum diagram of the calibration sample is obtained by the scanning probe；Judgement is standardized to the energy spectrum diagram by the data categorization module；If the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe calibration is qualified.The above method, can quick, high-precision detection scanning probe it is whether qualified, realize probe calibration.The probe scaling method analyzes and determines whether scanning probe is qualified using data categorization module, reduces the scanning probe calibration process problem that consuming time is long, calibration difficulty is big.
Description
Technical field
The present invention relates to detection device technical fields, more particularly to a kind of scanning probe scaling method.
Background technique
During the progress of nanoscale science and technology and continuous development, two class scientific instrument play important promotion and make
With.A kind of scientific instrument are the electron microscopes using electron beam as probe, such as transmission electron microscope and scanning electron microscopy
Mirror.Another kind of scientific instrument are the scanning probe microscopies using solid needle point as probe, such as scanning tunneling microscope and atom
Force microscope.
Scanning tunneling microscope is to detect sample to be tested using quantum-mechanical tunneling effect.Specifically, in vacuum
Making alive on the scanning probe of middle close proximity and sample to be tested can pass through between them and distance has exponent relation and scans
The needle point of probe and the directly proportional tunnelling current of the density of electronic states of sample to be tested.When known to the property of scanning probe tip,
The tunnelling current information can delicately reflect the information such as pattern, the electronic state of sample surfaces very much.Scanning tunneling microscope master
High-precision scanning is carried out in sample surfaces dependent on scanning probe tip, so demarcating to scanning probe tip performance
There is vital meaning to accurate experimental result is obtained with processing.
Currently, the calibration and detection for scanning tunneling microscope scanning probe tip are completed using manual operation mostly.
It is big manually to demarcate data volume to be treated, it is often necessary to experimenter's long time on duty.In addition, all being needed before experiment every time
Such staking-out work is carried out, the waste of huge burden and time, energy can be caused to experimenter.
Summary of the invention
Based on this, it is necessary to need to handle mass data, consumption when demarcating scanning probe tip for traditional scheme
Take a large amount of manpowers, the problem of material resources, provides a kind of efficient scanning probe scaling method.
A kind of scanning probe scaling method, is demarcated, including following for the scanning probe to scanning tunneling microscope
Step:
The shape appearance figure of calibration sample is obtained by the scanning probe；
Judgement is standardized to the shape appearance figure by data categorization module；
If the data categorization module judges that the shape appearance figure is qualified, the calibration sample is obtained by the scanning probe
The energy spectrum diagram of product；
Judgement is standardized to the energy spectrum diagram by the data categorization module；And
If the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe calibration is qualified；
Wherein, the data categorization module is obtained by the machine learning method of support vector machines；
The method for obtaining the data categorization module by the machine learning method of support vector machines, specifically includes:
The image information for selecting multiple groups to be learned；
Characteristic information is extracted from described image information, different classifying rules, institute are established for different characteristic informations
The establishment process for stating classifying rules is to be coordinately transformed to described image information；
By the way that different weight ratios is arranged between the different classifying rules, to form the data categorization module.
The data categorization module can also be obtained by the machine learning method of neural network in one of the embodiments,
?；
The method for obtaining the data categorization module by the machine learning method of neural network, specifically includes:
The image information for selecting multiple groups to be learned；
After described image information is carried out classification storage, multitiered network is established for the characteristic information of described image information
Structure；
The function pair to the multitiered network structure setting function corresponding relationship, in each layer network structure
It should be related to middle setting parameter；
Adjust the parameter in each layer network structure constantly to form the data categorization module.
The energy spectrum diagram that the calibration sample is obtained by the scanning probe in one of the embodiments, before
Further include:
Measurement point is selected on the shape appearance figure；
In the measurement point, fixation measuring position changes the needle point bias of the scanning probe, measures the calibration sample
Differential conductance curve.
The scanning probe scaling method in one of the embodiments, further include:
If the data categorization module judges that the shape appearance figure is unqualified or the data categorization module judges the energy
Spectrogram is unqualified, then is modified to the scanning probe；And
After being modified to the scanning probe, further return is described obtains calibration sample by the scanning probe
The step of shape appearance figure.
Described the step of obtaining the shape appearance figure of calibration sample by the scanning probe, is specific in one of the embodiments,
Include:
The scanning probe is controlled by the scanning tunneling microscope to be horizontally moved with fixed step-length；
The scanning probe obtains the density of electronic states value on calibration sample every bit position in moving process,
To constitute the shape appearance figure.
The scanning probe scaling method in one of the embodiments, further include: the shape appearance figure is normalized
Processing；
It is described the shape appearance figure to be normalized to carry out background process to the shape appearance figure, filtering out the shape
The noise of looks figure carries out intensive treatment to the characteristic information of the shape appearance figure.
After in one of the embodiments, described by the shape appearance figure of the scanning probe acquisition calibration sample, institute
State scanning probe scaling method further include:
Binary conversion treatment is carried out to the shape appearance figure, finds the impurity point edge in the shape appearance figure；
Judge the long axis of the impurity point edge and the ratio of short axle；
If the ratio is not equal to 1, the scanning probe is modified.
If the data categorization module judges that the energy spectrum diagram is qualified in one of the embodiments, the scanning probe
Scaling method further include:
Judge whether the scanning probe is stable by the data categorization module；
If the scanning probe is unstable, the scanning probe is modified.
In one of the embodiments, after judging that the scanning needle point is stablized, further includes:
The needle point for controlling the scanning probe completes primary forward/backward movement；
If it is described scanning probe needle point during advance, electric current be more than range after, it is described scanning probe needle
Also no longer change when the sharp withdraw of the needle, remain the state more than range, then shows that the needle point of the scanning probe has been adsorbed onto institute
State certain atoms on calibration sample；At this time, it may be necessary to be handled the scanning probe to remove certain atoms, then demarcate institute
State scanning probe；
If the needle point of the scanning probe is in completing primary forward/backward action process, some immediate current is unexpected
Reduce, then shows that some atom falls off on the needle point of the scanning probe；At this time, it may be necessary to be carried out to the scanning probe
Processing can satisfy test request with the needle point for guaranteeing the scanning probe, and demarcate the scanning probe again.
Described the step of being modified to the scanning probe, specifically includes in one of the embodiments:
The control system carried by scanning electron microscope is to the scanning probe making alive, so that the scanning probe
Needle point ream one or more atoms or suck one or more atoms；
Or be inserted into the scanning probe into the calibration sample to be measured, so that the needle point of the scanning probe reams one
A or multiple atoms suck one or more atoms.
Scanning probe scaling method provided by the invention obtains the shape appearance figure and power spectrum of calibration sample by scanning probe
Figure judges whether shape appearance figure and the energy spectrum diagram are qualified by the way that data categorization module synthesis is described, being capable of quick, high-precision inspection
Survey whether the scanning probe is qualified, realizes the calibration of the scanning probe.The scanning probe scaling method, using the number
Analyze and determine whether the scanning probe is qualified according to categorization module, reduces the consuming time of the scanning probe calibration process
Problem long, calibration difficulty is big.
Detailed description of the invention
Fig. 1 is the flow chart of scanning probe scaling method provided by one embodiment of the present invention；
Fig. 2 is the flow chart for the scanning probe scaling method that another embodiment of the present invention provides；
Fig. 3 is the shape appearance figure for gold (111) surface qualification that scanning probe provided by one embodiment of the present invention is tested；
Fig. 4 is the underproof shape appearance figure in gold (111) surface that scanning probe provided by one embodiment of the present invention is tested；
Fig. 5 a is the shape appearance figure that the test in scanning probe scaling method provided by one embodiment of the present invention obtains；
Fig. 5 b is the energy spectrum diagram that the test of scanning probe scaling method provided by one embodiment of the present invention obtains；
Fig. 5 c is the test synthesis analysis chart in scanning probe scaling method provided by one embodiment of the present invention；
Fig. 6 is the picture provided by one embodiment of the present invention through qualified scanning probe scanning qualification；
Fig. 7 is provided by one embodiment of the present invention through the underproof picture of underproof scanning probe scanning；
Fig. 8 is the flow chart for the scanning probe scaling method that further embodiment of the present invention provides.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, right with reference to the accompanying drawings and embodiments
Scanning probe scaling method of the invention is further described.It should be appreciated that specific embodiment described herein only to
It explains the present invention, is not intended to limit the present invention.
Fig. 1 please be participate in, a kind of scanning probe scaling method is provided, is carried out for the scanning probe to scanning tunneling microscope
Calibration, comprising the following steps:
S100 obtains the shape appearance figure of calibration sample by the scanning probe.
The shape appearance figure of calibration sample, that is, the shape appearance figure obtained are obtained in step S100 by scanning probe.The scanning is visited
Needle can be the probe used when scanning tunneling microscope test sample.The calibration sample can be the to be scanned of known properties
Film, bulk or other.The shape appearance figure, which can be, controls scanning probe from a panel region by scanning tunneling microscope
Start on one angle.Every time when the scanning probe is implemented to scan, control the scanning probe pinpoint movement it is fixed away from
From scanning a figure every fixed range.The shape appearance figure can be each pixel that the scanning probe scanning is crossed
Current information.
S200 is standardized judgement to the shape appearance figure by data categorization module.
In step S200, the data categorization module can be the classifier established by the method for various machine learning.
The data categorization module or the classifier can be according to the specific shape appearance figure training and a certain specific judgements of study
Standard.Specifically, the shape appearance figure obtained in step S100 is input to the data categorization module or the classifier.Institute
A certain specific judgment criteria that data categorization module or the classifier learn before is stated to the calibration sample
The shape appearance figure is standardized judgement.It is appreciated that it can be concluded that whether the shape appearance figure is qualified after standardization judgement.
Whether He Ge result may be a probability value to the shape appearance figure obtained after normalized judgement, and be directed to the shape appearance figure
The a certain specific judgment criteria can be machine learning method obtain.
S300, if the data categorization module judges that the shape appearance figure is qualified, by described in scanning probe acquisition
The energy spectrum diagram of calibration sample.
After the data categorization module judges that the shape appearance figure is qualified, then can by the scanning tunneling microscope into
One step controls the energy spectrum diagram that the scanning probe obtains the calibration sample.The acquisition of the energy spectrum diagram of the calibration sample needs
It to be carried out after the shape appearance figure of the calibration sample is determined qualification.In the energy spectrum diagram for obtaining the calibration sample
When, the first reconnaissance in the shape appearance figure of the calibration sample is needed, is then tested again.
Power spectrum refers to that counting rate just can be obtained with the distribution curve of particle energy in impulse amplitude after energy calibration.Different energy
Density of electronic states information under amount, referred to as a kind of power spectrum of solid.According to quantum mechanics, tunnelling current is close with electronic state when tunnelling
It spends proportional.And by measuring tunnelling current under different voltages, using conductance G=I/V, available voltage is horizontal axis, electricity
Lead the curve for the longitudinal axis.Differential is taken to the curve, what is obtained is exactly differential conductance curve.In solids, the energy of electronics is main
It concentrates in some specific ranges, some particular ranges that the energy of electronics is concentrated are known as band structure.In band structure,
Electronic state number in unit-energy interval, referred to as density of electronic states.Density of electronic states, which represents, accommodates electronics under the energy
Number.Differential conductance curve in this way is just directly proportional with density of electronic states, can be measured admittedly by measuring differential conductance curve
The power spectrum of body.For example, being used as the calibration sample using golden (111) in test process.The power spectrum of the calibration sample golden (111)
Be it is known, whether the needle point that the scanning probe can be demarcated with the power spectrum of known golden (111) qualified.
S400 is standardized judgement to the energy spectrum diagram by the data categorization module.
In step S400, the data categorization module equally can be the classification established by the method for various machine learning
Device.The data categorization module or the classifier can be according to the specific energy spectrum diagram training and study is a certain specifically sentences
Disconnected standard.Specifically, the energy spectrum diagram obtained in step S300 is input to the data categorization module or the classifier.
The power spectrum of the judgment criteria that the data categorization module or the classifier learn before to the calibration sample
Figure is standardized judgement.It is appreciated that the data categorization module or the classifier herein is to the shape appearance figure and institute
The analytical judgment for stating energy spectrum diagram can use different judgment criterias.The data categorization module or the classifier can be directed to
The shape appearance figure and the energy spectrum diagram learn to different judgment criterias.That is, the data categorization module or described point
Class device can train identify whether the shape appearance figure and the energy spectrum diagram are qualified, to further determine that the scanning probe is
No qualification.Whether He Ge result may be a probability for the shape appearance figure obtained after normalized judgement and the energy spectrum diagram
Value.
S500, if the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe calibration is qualified.
In the step S500, if the data categorization module judges that the energy spectrum diagram is qualified, the scanning is also indicated that
The shape appearance figure and the energy spectrum diagram that probe is tested are qualified, can determine that the scanning probe calibration is qualified at this time.Afterwards
In continuous experiment, test experiments can be carried out to new test sample by the scanning probe.
Scanning tunneling microscope needs worked and measured in ultrahigh vacuum, avoid molecular density fluctuation in air,
Thermal fluctuation bring tunnelling signal fluctuation.The needle point of the scanning probe is easy to be aoxidized by the oxygen in air simultaneously, oxygen
Tunneling resistance can greatly be increased by changing layer, seriously affect experimental performance.Therefore the scanning probe cannot be from when measuring sample
Open ultrahigh vacuum.That is, " in situ measurement " needs carry out in ultrahigh vacuum, otherwise the fluctuation in air can direct interference
Measurement data.Scanning probe scaling method described in the present embodiment is carried out in ultrahigh vacuum.It is appreciated that the scanning
Probe can be tungsten or other materials.The needle point of the scanning probe handles very sharp by specially treated means, top
Often only one atom.In addition, in the entire scanning probe calibration process, the scanning probe and the calibration sample
In always have certain slight distance.The scanning probe measurement is the tunnelling current for penetrating vacuum.Due to the scanning
The needle point of probe is very small, for realizing atomic level resolution ratio, may only one on the needle point of the scanning probe
Atom.The needle point scale of the scanning probe may be 0.1 nanometer.The needle point of scanning probe can not be directly evaluated in existing scheme
Quality.And the scanning probe scaling method provided in the present embodiment being capable of high-precision composite calibration needs acquisition and analysis
Mass data, these repeated labors are analyzed and are obtained corresponding conclusion by the data categorization module.Therefore, of the invention
The scanning probe scaling method provided, can quick, high-precision detection scanning probe it is whether qualified, realization probe mark
It is fixed.The scanning probe scaling method reduces the scanning probe calibration process problem that consuming time is long, calibration difficulty is big.
Referring to Fig. 2, in one embodiment, the scanning probe scaling method further include:
S600, if the data categorization module judges that the shape appearance figure is unqualified or the data categorization module judges institute
It is unqualified to state energy spectrum diagram, then the scanning probe is modified；And
S700 after being modified to the scanning probe, is further returned described by scanning probe acquisition calibration
The step of shape appearance figure of sample.
Amendment to the scanning probe may include carrying out bulk processing to the scanning probe and to the scanning probe
Carefully handled.If the scanning probe carries out bulk processing not over the test of the shape appearance figure, to scanning probe.
Since the correcting mode of the scanning probe is the control scanning probe distance very short to calibration sample advance, then move back
It returns in situ.The scanning probe works normally distance and moves forward several nanometers of meetings of zero point and its close to the calibration sample table
Face, the Van der Waals between the calibration sample atom and the scanning probe atom can enhance rapidly, to pull institute
The needle point pattern that scanning probe adjusts the scanning probe is stated, makes the needle point of the scanning probe that needle point be kept to only have one as possible
The state of atom.For example, retracting the needle point advance 0.4nm of the scanning probe again, the bias of holding -4V moves forward and backward
All continue 1s.
If the scanning probe has only passed through the test of the shape appearance figure, not over the test of the energy spectrum diagram,
Then the scanning probe is carefully handled.It is thick relative to carrying out to the scanning probe that thin processing is carried out to the scanning probe
Processing, only the needle point advance amount of the scanning probe and bias are of different sizes.Specifically, the needle point of the scanning probe advances
The setting value of amount and bias size is different because of the calibration sample.For example, carrying out bulk processing to the scanning probe can use
0.6nm, -6V, and carefully handle using 0.4nm, -4V.The needle point advance amount of the scanning probe can be visited for the scanning
The step value that needle moves every time.Bias above-mentioned is voltage of the needle point of the scanning probe relative to the calibration sample.It can
It is connected respectively by the two poles of the earth of the same controllable voltage source so that the needle point of the scanning probe is arranged with the calibration sample, in this way may be used
Easily to change the size of bias.
If the scanning probe had both passed through the test of the shape appearance figure, further through the test of the energy spectrum diagram, then
The scanning probe staking-out work is completed, and can be scanned test to new test sample with the scanning probe.Described
Corresponding notifications experimenter can also be arranged in data categorization module can start to be scanned new test sample
Experiment.
When not completing scanning probe calibration, i.e., the described scanning probe does not pass through calibration, can set systematic
The tips quality standard of the scanning probe.Such as: if 10 continuous measurement figures (including the shape appearance figure or the power spectrum
Figure) in there is no an opening and closing lattice, then measured again using coarse adjustment piezoelectric ceramics traveling 200nm.If continuous moving does not all have twice
It can obtain qualified measurement figure, then it is assumed that the tips quality of the scanning probe is problematic, needs to carry out the scanning needle point
Bulk processing.If not terminating current calibration by the test of the shape appearance figure, in the needle of current region processing scanning probe
Then the needle point for scanning probe is removed the region (region may be destroyed) by point.After being modified to scanning probe
Starting point is come back to, scanning probe knit stitch restarts to demarcate.If not by the test of the energy spectrum diagram, in completion pair
Described the step of judgement is standardized to the shape appearance figure by data categorization module is jumped back to after the amendment of the scanning probe,
Judge whether the shape appearance figure is qualified.
In the present embodiment, the step of being modified to the scanning probe is increased, the scanning probe is existed
During in-situ test without departing from existing equipment, do not change test environment in the case where, complete the amendment to the scanning probe.
This step also makes the scanning probe scaling method more quickly and intelligent.
In one embodiment, in the scanning probe calibration process, the calibration sample of use is of high quality, institute
State that the property of calibration sample is also appreciated that it is clear that the quality approximation that therefore scanning tunneling microscope collects data can be straight
The reversed tips quality for reflecting the scanning probe.It, can be with using coarse adjustment piezoelectric ceramics in the case where the calibration sample is motionless
Move the needle point of the scanning probe in the range of 300nm × 300nm.And when carrying out shape appearance figure acquisition every time, it needs
Fine tuning piezoelectric ceramics is used, therefore the size of two-dimensional appearance data described in one group is 40nm × 40nm.With control coarse adjustment piezoelectricity
Ceramics scan the pinpoint movement 40nm of probe since on an angle in a piece of region 300nm × 300nm described in every secondary control,
A figure is scanned every 40nm.Such as can advance in such a way that "the" shape scans in entire area, every time first along the side X
To movement, make a move to boundary along Y-direction, then moved to X opposite direction.
In one embodiment, it needs to select on the shape appearance figure before carrying out energy spectrum diagram test to the calibration sample
Measurement point.In measurement point, fixation measuring position changes the needle point bias of the scanning probe, measures the micro- of the calibration sample
Divide conductance plots.What the differential conductance curve of the calibration sample can directly be measured by lock-in amplifier, reflect the mark
The spectral information of random sample product.It needs in the difference of the differential conductance curve in two regions, the peak value near searching -460mV bias.
After the scanning probe scanning, which goes out, corresponds to the differential conductance curve in two regions, it will be fitted using computer program to two
A spike is analyzed, to judge ability of the needle point for spectral measurement of the scanning probe.
If the needle point of the scanning probe has passed through the test of shape appearance figure described above and the test of the energy spectrum diagram,
Show that the needle point of the scanning probe is functional.The scanning probe can formally start to test, and can pass through program setting
Experimenter is notified to start to test.
In one embodiment, the S100 has by the step of shape appearance figure for scanning probe acquisition calibration sample
Body includes:
The scanning probe is controlled by the scanning tunneling microscope to be horizontally moved with fixed step-length；
The scanning probe obtains the density of electronic states value on calibration sample every bit position in moving process,
To constitute the shape appearance figure.
The detailed process of the scanning probe scaling method are as follows: the needle point of control scanning tunneling microscope scanning probe, by
Gradually close to the calibration sample.The needle point of the scanning probe reaches the position apart from the calibration sample number angstrom, may be implemented
Sensitive tunnelling.After the needle point of the scanning probe reaches the position that can be tested, pass through two pieces of the control direction X/Y pressure
Voltage on electroceramics, control piezoelectric ceramics are flexible to make the needle point of the scanning probe with fixed step-length carry out level shifting
It is dynamic.In moving process, the bias of the needle point of the fixed scanning probe, in the size of every bit measurement tunnelling current, the electric current
Value can reflect the density of electronic states value of the point under this bias, so that the corresponding point that obtains on sample is to the needle point for scanning probe
The information such as distance and the space of points charge density.The scanning probe during the scanning process, for each pixel, i.e., one
(x, y) point measures a current value.A shape appearance figure is constituted according to these current values, can analyze from these data
The information such as the surface topography of sample and property, density of electronic states out.
In one embodiment, the scanning probe scaling method further includes that the shape appearance figure is normalized.
The concrete mode of the normalized is that the background process, noise for filtering out the shape appearance figure, right is carried out to the shape appearance figure
The characteristic information of the shape appearance figure carries out intensive treatment.Background process is gone to can be understood as filtering out since the calibration sample is being surveyed
The shape appearance figure has out-of-flatness, irregular situation caused by putting out-of-flatness or other reasons during examination.By going to carry on the back
Scape processing filters out the test error due to test process bring to the calibration sample.Filtering out noise processed can be understood as pair
Uncertain, insignificant some noise characteristics of the shape appearance figure are filtered out, and determine result to the shape appearance figure to increase
Accuracy.Characteristic information intensive treatment can be understood as doing the characteristic information of the shape appearance figure reinforcement or prominent processing with side
Just the classification of the shape appearance figure is integrated.Background process is carried out to the shape appearance figure, filters out noise processed and characteristic information
Intensive treatment can carry out side by side, can also only carry out a processing.Specifically, can be with to the normalized of the shape appearance figure
Depending on the specific test case of the shape appearance figure.The characteristic information of the shape appearance figure after normalized more has
It is body, more obvious, it is more convenient the data categorization module and carries out classification discrimination.
It, can be using golden (111) surface as the calibration sample in the scanning probe scaling method.Gold is with structure cell
One vertex is that origin makees rectangular coordinate system in space, makes x, and y, z-axis positive direction is Chong Die with three sides of structure cell, and set structure cell side length as
1, gold (111) face, that is, x=1, the plane of 3 points of determinations of y=1, z=1.The property of golden (111) has been well known.Golden conduct
Extraordinary metal, charge is very free, and tunnelling property is convenient for measurement.Golden (111) surface has distinct electronic structure special
Sign, measures convenient for application scanning tunnel microscope.Such as in calibration sample surface gold atom in order to reach energy most
Low state will form the staggered structure of two kinds of arrangement modes (referred to as hcp and fcc).Two kinds of arrangement modes hcp and fcc are as fish
Bone is equally alternately arranged.Frontier district density of electronic states between two kinds of regions is higher, brighter in the image scanned.
Two regions hcp and fcc are separated by these bright borders.Hcp and two regions fcc are all darker.The region hcp is wider, the region fcc compared with
It is narrow.When being scanned using scanning tunneling microscope fixed-bias transistor circuit, hcp and two region tunnelling currents of fcc are different, are scanning
The shape appearance figure on can show the same striped of the staggered Fishbone of light and shade.In the shape appearance figure, the scanning probe is swept
The quality of the striped described as measure it is described scanning probe needle point whether a He Ge standard.Fig. 3 and Fig. 4 are please referred to,
Fig. 3 is the shape appearance figure of golden (111) surface qualification, and wide black is hcp, and narrow black is fcc, here it can also be seen that turning round existing
As.The size also shown in Fig. 3 in p-wire Fig. 3 is 40nm × 40nm.And it is illustrated in figure 4 underproof golden (111) table
Face data.Two regions hcp and fcc can not be shown in Fig. 4.
The step of obtaining the energy spectrum diagram of sample by the scanning probe specifically: be judged as described in qualification
It arbitrarily crosses in shape appearance figure.Choose have the scribing line of different in width striped as p-wire (p-wire as shown in Figure 3, can be with
Understand that the p-wire can arbitrarily be chosen, two regions hcp and fcc can be found).It can be distinguished simultaneously on the p-wire
Measure the width in two regions hcp and fcc.The multiple groups adjacent area hcp and fcc of different size can be obtained on the p-wire
Domain.Measure the spacing of the central point in the adjacent region hcp and fcc of each group of different in width.The region hcp central point and
The central point in the region fcc is as test point, and in two above test point, the lower section scanning probe goes to detect respectively.Pass through change
Scanning probe bias added by above-mentioned test point, can measure differential conductance curve, i.e., the described energy spectrum diagram.It will be at two
The curve that region measures subtracts each other, and a peaks characteristic can be measured near -460mV bias.Specifically, in the region hcp
First group of V-I value is measured at heart point, the center in the narrow region fcc measures second group of V-I value.By first group of V-I value
Processing, which is combined, with second group of V-I value obtains third group V-I value.If the third group V-I value can obtain spike,
It is qualified that the needle point of the scanning probe is demarcated.If the third group V-I is worth less than spike, to the needle of the scanning probe
Point is modified.
It please refers to Fig. 5 a, Fig. 5 b, the shape appearance figure measured in one embodiment is shown in Fig. 5 c, Fig. 5 a.In Fig. 5 a
Also show the test point in the region hcp and the test point in the region fcc.Figure 5b shows that the test point in the region hcp is (deep in Fig. 5 b
The power spectrum of black) and the test point (power spectrum of light/dark balance in Fig. 5 b) in the region fcc at two power spectrums acquiring respectively.Fig. 5 c is figure
The difference of two power spectrums in 5b, it can clearly be seen that -480mV nearby has a peak value.Comprehensive analysis Fig. 5 b and Fig. 5 c can be obtained
This time the scanning probe calibration is qualified out.It generally will appear reconstruct (turning phenomenon) in the scanning process of the shape appearance figure,
This generally every 280nm that reconstructs will appear turn, be easily absorbing some small impurity particles in corner；When scanning probe
Needle point state it is good when, picture of these particles in the shape appearance figure is lesser round bright spot.If these impurity
Particle deviates round or formation ghost image etc. and all shows that the tips quality of the scanning probe is bad.
In one embodiment, after the shape appearance figure that sample is obtained by scanning probe, the scanning probe scaling method
Further include:
Binary conversion treatment is carried out to the shape appearance figure, finds the impurity point edge in the shape appearance figure；
Judge the long axis of the impurity point edge and the ratio of short axle；
If the ratio is not equal to 1, the scanning probe is modified.
It is appreciated that whether qualification can also be judged the shape appearance figure by impure point present in the shape appearance figure.
Fig. 6 and Fig. 7 are please referred to, Fig. 6 is the picture through the qualified scanning probe scanning qualification, can be seen that impure point is in Fig. 6
It is circular.In Fig. 6, binary conversion treatment is carried out to the shape appearance figure, finds the impurity point edge in the shape appearance figure.Impure point side
The long axis of edge and the ratio of short axle can illustrate that the scanning probe is qualified close to 1 or equal to 1.As shown in fig. 7, for through not
The picture that the qualified scanning probe scanning arrives.Impure point deviates round in Fig. 7, illustrates that the needle point of the scanning probe has partially
Difference, the long axis of impurity point edge and the ratio of short axle differ greatly with 1.Test picture shown in Fig. 7 is unqualified.
In one embodiment, it is obtained by the machine learning method of the machine learning method of support vector machines or neural network
Obtain the data categorization module.
The quality of the needle point of the scanning probe generally uses and good data comparison is judged in history, present invention benefit
It is handled with machine learning.Specifically, the shape appearance figure that can will be scanned, sends Matlab program to and carries out automatically
Change data analysis.The program is designed using machine learning techniques (convolution artificial neural network, support vector machines etc.), and utilization is huge
Scanning tunneling microscope technology be trained, the quality of good data in history can be identified with higher accuracy, thus
Generate the evaluation of the tips quality to the scanning probe.
The automation of data analysis is realized using the method for machine learning, and is realized using LabVIEW program to experiment
The automatic control of process, thus significant increase conventional efficient.In the Matlab program that the calculating of program is generally noted above,
LabVIEW program realizes automation, unattended control.It is appreciated that there are many ways to machine learning, and not only limit
The machine learning method of support vector machines or neural network mentioned in the present invention.It can also be appreciated that the scanning
In probe scaling method, automatic control system can be write using LabVIEW language.The instrument control platform and language of mainstream at present
Speech is LabVIEW, because its company National Instruments provides corresponding program for various scientific instrument and connects
Mouthful.It is theoretically authorized, these instruments can be accessed with other language, various program languages such as Python, C++, C# are
Automatic control system can be write, wherein database preparation is realized using machine learning, to realize the needle point of scanning probe
Automatic Calibration.
In one embodiment, the data categorization module can be a classifier functions.Utilize past experiment number
According to, good data or the test data of known sample in history, for example, the test data in golden (111) face, using artificial neural network
Network, support vector machines scheduling algorithm carry out machine learning to historical data, learn a classifier functions out.If the scanning is visited
The needle point of needle will then control the needle point of the scanning probe in the less block of a piece of impurity by the test of the shape appearance figure
The measurement of hcp and fcc regional center position progress energy spectrum diagram.It can be from the pattern using the machine learning program of pattern-recognition
Both regions are identified in figure and provide the coordinate of their centers.By marking p-wire in the shape appearance figure, find
Test point obtains the energy spectrum diagram by the scanning probe.The data categorization module is completed by the energy spectrum diagram to institute
State the evaluation of the tips quality of scanning probe.Evaluating the scanning probe is " qualification " or " unqualified ".By training and learn
The data categorization module practised can voluntarily judge whether the shape appearance figure inputted from now on and the energy spectrum diagram are qualified.Institute
It states after data categorization module provides " qualification " or the judging result of " unqualified ", uses different steps pair further according to judging result
The scanning probe carries out corresponding operation.
In one embodiment, the data categorization module is obtained by the machine learning method of neural network, it is specific to wrap
It includes:
The image information for selecting multiple groups to be learned；
After described image information is carried out classification storage, multitiered network is established for the characteristic information of described image information
Structure；
The function pair to the multitiered network structure setting function corresponding relationship, in each layer network structure
It should be related to middle setting parameter；
Adjust the parameter in each layer network structure constantly to form the data categorization module.
The image information for selecting multiple groups to be learned is as training data.The training data is inputted into the data classification mould
Block is stored.The training data of input is normalized in the data categorization module.For example, shape appearance figure inputs
One matrix, is normalized its brightness.Input data is regarded as a point of higher dimensional space, a vector (tensor), it can
Each component is sent to this layer of corresponding neuron respectively according to its footnote, weight multiplication is done according to weight matrix w, then plus
Upper offset vector b, that is, being y=wTX+b operation, obtains mapping value.
After carrying out storage and normalized to multiple groups image information to be learned, for described to be learned
The characteristic information of image information establishes multitiered network structure.For obtained multilayer neural network, every time by a upper neuron
The result iteration of layer, such as x2=y1, until reaching the last one hidden neuron layer.Parameter is mapped as 0 using decision function
A number between~1, for classifying, such as 1 represents qualification, and 0 represents unqualified, and it is exactly qualified at this moment mapping out the number come
Probability.
The training data can be the corresponding true classification of each data point clearly known, then will really classify
It is compared with the classification calculated above come out, obtains the accuracy p under the situation.Then, whole network is iterated, and is utilized
The methods of least square, maximum likelihood, gradient decline, constantly change the weight matrix of each layer of neuron, until enabling accuracy p
Obtain maximum.This just completes the training to the entire data categorization module.
After the completion of the training of the data categorization module, available a series of weight matrix w1, w2... wn.Power
Weight matrix w1, w2... wnRespectively correspond each layer of neural network.It is, the data categorization module includes the weight
Matrix w1, w2... wnWith corresponding a series of neural network structure.Using the data categorization module to training data
Classification closest to legitimate reading.
In one embodiment, the data categorization module is obtained by the machine learning method of support vector machines, specifically
Include:
The image information for selecting multiple groups to be learned；
Characteristic information is extracted from described image information, different classifying rules, institute are established for different characteristic informations
The establishment process for stating classifying rules is coordinately transformed described image information；
Different weight ratios is set between the different classifying rules, to form the data categorization module.This reality
It applies in example, the data categorization module is established using the machine learning method of support vector machines.Pass through the image information from multiple groups
Then middle extraction characteristic information establishes different classifying rules for different characteristic informations.According to test needs (survey herein
The quality requirement that examination needs can be image information to be judged is different), different power is set between different classifying rules
Compare again, to form the data categorization module.It is appreciated that the data categorization module, which can train, generates a variety of classification gauges
Then.Such as: data categorization module described in by way of self study can form a kind of classifying rules for shape appearance figure, equally may be used
To generate a kind of classifying rules for energy spectrum diagram.Even, it also can be generated when the scanning probe steady judges corresponding
Classifying rules judges the stability data of scanning probe, the whether stable of the scanning probe obtained.
In one embodiment, the S600, the step of being modified to the scanning probe, specifically include:
Specific voltage waveform is applied to the scanning probe by the control system that scanning electron microscope carries, so that
The needle point of the scanning probe reams one or more atoms or sucks one or more atoms；Or by the scanning probe to
Insertion in sample to be tested, so that the needle point of the scanning probe reams one or more atoms or sucking is one or more former
Son.It is appreciated that the step of being modified to the scanning probe can be it is not limited to both the above.If there is can be former
The method that the scanning probe tip is corrected in position also can be incorporated into the present invention.
Referring to Fig. 8, in one embodiment, it is described to sweep if the data categorization module judges that the energy spectrum diagram is qualified
Retouch probe scaling method further include:
S800 judges whether the needle point of the scanning probe is stable by the data categorization module, if the scanning is visited
Needle is unstable, then is modified to the scanning probe.
The flow chart assessed the stability of the scanning probe is shown in Fig. 8.The needle point of the scanning probe
Stability is required by measurement reproducibility.Because described in scanning tunneling microscope measurement process scan probe needle point away from
It is very close from the calibration sample, it is easy to be acted on by power such as calibration sample surface Van der Waals and form occurs
Change.The needle point of the scanning probe to change can provide identical measurement point different data, so as to cause this
Preceding measurement result all fails.Therefore, it is necessary to needle point, the close calibration samples by the mobile scanning probe, and
Judge whether the pattern of the needle point of this scanning probe described in the process can change, to judge the needle point of the scanning probe
Stability.The needle point of the unstable scanning probe will be repeated to handle, it is qualified and steady until obtaining performance
The needle point of the fixed scanning probe.
Specifically, the step of judging the scanning needle point stability may include: by scanning tunneling microscope to described
It scans probe and applies different voltages；The electric current of the scanning probe is obtained in real time；If in the voltage-current relation repeatedly measured
There is no burr presence, then the scanning probe steady, otherwise, the scanning probe is modified.
After judging the scanning needle point stability, before the needle point completion once that equally also control the scanning probe
It into/backward movement, requires the tunnelling current curve scanned not change at this time, that is, proves that the needle point of the scanning probe exists
It will not change when close to calibration sample measurement, the needle point performance of the scanning probe is stablized.If in advancing
Electric current remains the state more than range more than also no longer changing when the withdraw of the needle after range, then shows the needle of the scanning probe
Point has been adsorbed onto certain atoms on the calibration sample.At this time, it may be necessary to be handled the scanning probe certain to remove
Atom, then demarcate the scanning probe.If some immediate current reduces suddenly, show have on the needle point of the scanning probe
A part of atom falls off.It can with the needle point for guaranteeing the scanning probe at this time, it may be necessary to be handled the scanning probe
Meet test request, and demarcates the scanning probe again.
If the needle point of the scanning probe has passed through the calibration of the shape appearance figure but not by the calibration of the energy spectrum diagram,
Then indicate that the needle point of the scanning probe is problematic but not serious.At this point it is possible to select more small value to the scanning probe
Needle point be modified.The needle point for first occurring at the scanning probe to the amendment of the needle point of the scanning probe does not pass through survey
When examination.Determination of stability can just be entered by the needle point for the scanning probe all tested.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of scanning probe scaling method, is demarcated for the scanning probe to scanning tunneling microscope, which is characterized in that
The following steps are included:
The shape appearance figure of calibration sample is obtained by the scanning probe；
Judgement is standardized to the shape appearance figure by data categorization module；
If the data categorization module judges that the shape appearance figure is qualified, the calibration sample is obtained by the scanning probe
Energy spectrum diagram；
Judgement is standardized to the energy spectrum diagram by the data categorization module；And
If the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe calibration is qualified；
Wherein, the data categorization module is obtained by the machine learning method of support vector machines；
The method for obtaining the data categorization module by the machine learning method of support vector machines, specifically includes:
The image information for selecting multiple groups to be learned；
Characteristic information is extracted from described image information, establishes different classifying rules for different characteristic informations, described point
The establishment process of rule-like is to be coordinately transformed to described image information；
By the way that different weight ratios is arranged between the different classifying rules, to form the data categorization module.
2. scanning probe scaling method as described in claim 1, which is characterized in that wherein, the data categorization module may be used also
To be obtained by the machine learning method of neural network；
The method for obtaining the data categorization module by the machine learning method of neural network, specifically includes:
The image information for selecting multiple groups to be learned；
After described image information is carried out classification storage, multitiered network knot is established for the characteristic information of described image information
Structure；
The corresponding pass of the function to the multitiered network structure setting function corresponding relationship, in each layer network structure
Parameter is set in system；
Adjust the parameter in each layer network structure constantly to form the data categorization module.
3. scanning probe scaling method as claimed in claim 2, which is characterized in that described to obtain institute by the scanning probe
The energy spectrum diagram of calibration sample is stated, before further include:
Measurement point is selected on the shape appearance figure；
In the measurement point, fixation measuring position changes the needle point bias of the scanning probe, measures the micro- of the calibration sample
Divide conductance plots.
4. scanning probe scaling method as claimed in claim 2, which is characterized in that the scanning probe scaling method also wraps
It includes:
If the data categorization module judges that the shape appearance figure is unqualified or the data categorization module judges the energy spectrum diagram
It is unqualified, then the scanning probe is modified；And
After being modified to the scanning probe, the pattern that calibration sample is obtained by the scanning probe is further returned
The step of figure.
5. scanning probe scaling method as claimed in claim 2, which is characterized in that described obtained by the scanning probe is marked
The step of shape appearance figure of random sample product, specifically includes:
The scanning probe is controlled by the scanning tunneling microscope to be horizontally moved with fixed step-length；
The scanning probe obtains the density of electronic states value on calibration sample every bit position, in moving process with structure
At the shape appearance figure.
6. scanning probe scaling method as claimed in claim 5, which is characterized in that the scanning probe scaling method also wraps
It includes: the shape appearance figure is normalized；
It is described the shape appearance figure to be normalized to carry out background process to the shape appearance figure, filtering out the shape appearance figure
Noise, intensive treatment is carried out to the characteristic information of the shape appearance figure.
7. scanning probe scaling method as claimed in claim 2, which is characterized in that obtained described by the scanning probe
After the shape appearance figure of calibration sample, the scanning probe scaling method further include:
Binary conversion treatment is carried out to the shape appearance figure, finds the impurity point edge in the shape appearance figure；
Judge the long axis of the impurity point edge and the ratio of short axle；
If the ratio is not equal to 1, the scanning probe is modified.
8. scanning probe scaling method as claimed in claim 2, which is characterized in that if described in data categorization module judgement
Energy spectrum diagram is qualified, the scanning probe scaling method further include:
Judge whether the scanning probe is stable by the data categorization module；
If the scanning probe is unstable, the scanning probe is modified.
9. scanning probe scaling method as claimed in claim 8, which is characterized in that steady in the needle point for judging the scanning probe
After fixed, further includes:
The needle point for controlling the scanning probe completes primary forward/backward movement；
If the needle point of the scanning probe, during advance, electric current is moved back more than after range in the needle point of the scanning probe
Also no longer change when needle, remain the state more than range, then shows that the needle point of the scanning probe has been adsorbed onto the mark
Certain atoms on random sample product；At this time, it may be necessary to be handled the scanning probe to remove certain atoms, then demarcate described sweep
Retouch probe；
If the needle point of the scanning probe is in completing primary forward/backward action process, some immediate current reduces suddenly,
Then show that some atom falls off on the needle point of the scanning probe；At this time, it may be necessary to handle the scanning probe
To guarantee that the needle point of the scanning probe can satisfy test request, and the scanning probe is demarcated again.
10. the scanning probe scaling method as described in any one of claim 4,7 or 8, which is characterized in that described to be swept to described
The step of probe is modified is retouched to specifically include:
The control system carried by scanning electron microscope is to the scanning probe making alive, so that the needle of the scanning probe
Taper falls one or more atoms or sucks one or more atoms；
Or the scanning probe is inserted into the calibration sample to be measured so that it is described scanning probe needle point ream one or
Multiple atoms suck one or more atoms.
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| CN109142358B (en) * | 2018-08-22 | 2020-09-29 | 王天骄 | Metal material mechanical property testing method based on neural network |
| CN114264372A (en) * | 2018-09-05 | 2022-04-01 | 阿里巴巴集团控股有限公司 | Quantum bit detection system and detection method |
| CN112730895B (en) * | 2020-12-22 | 2021-11-19 | 中国科学院物理研究所 | Atom/molecule carrying method and system |
| CN114046959B (en) * | 2021-12-01 | 2024-05-10 | 中国科学院工程热物理研究所 | Five-hole pneumatic probe calibration method based on two-stage artificial neural network |
| CN114581343B (en) * | 2022-05-05 | 2022-07-29 | 南京大学 | Image restoration method and device, electronic equipment and storage medium |
| WO2024131051A1 (en) * | 2022-12-23 | 2024-06-27 | 前微科技(上海)有限公司 | Scanning probe system measurement method for improving signal-to-noise ratio, and scanning probe system |
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