CN107300629A - Scan probe scaling method - Google Patents
Scan probe scaling method Download PDFInfo
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- CN107300629A CN107300629A CN201710641861.1A CN201710641861A CN107300629A CN 107300629 A CN107300629 A CN 107300629A CN 201710641861 A CN201710641861 A CN 201710641861A CN 107300629 A CN107300629 A CN 107300629A
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- G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
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Abstract
Probe scaling method is scanned the present invention relates to one kind, is demarcated for the scanning probe to PSTM.Methods described includes：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 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 demarcation is qualified.The above method, quick, high-precision can detect that whether scanning probe is qualified, realizes that probe is demarcated.The probe scaling method, judges whether scanning probe is qualified using data categorization module analysis, reduces the consuming time length of scanning probe calibration process, demarcates the problem of difficulty is big.
Description
Technical field
The present invention relates to detection device technical field, more particularly to a kind of scanning probe scaling method.
Background technology
During the improving and continue to develop of nanoscale science and technology, two class scientific instrument serve important promotion and made
With.One class scientific instrument are the electron microscope using electron beam as probe, such as transmission electron microscope and scanning electron microscopy
Mirror.Another kind of scientific instrument are the scanning probe microscopy using solid needle point as probe, such as PSTM and atom
Force microscope.
PSTM is to detect testing sample using quantum-mechanical tunneling effect.Specifically, in vacuum
The scanning probe of middle close proximity and making alive on testing sample, can pass through between them and distance has exponent relation and scanned
The tunnelling current that the needle point of probe and the density of electronic states of testing sample are directly proportional.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.PSTM master
Will be dependent on scanning probe tip in the high-precision scanning of sample surfaces progress, so being demarcated to scanning probe tip performance
There is vital meaning to obtaining accurate experimental result with processing.
At present, for PSTM scanning probe tip demarcation and detection is completed using artificial operation mostly.
Artificial demarcation needs data volume to be processed big, 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, huge burden and time, the waste of energy can be caused to experimenter.
The content of the invention
Based on this, it is necessary to need processing mass data, consumption when being demarcated for traditional scheme for scanning probe tip
The problem of taking a large amount of manpowers, material resources, there is provided the efficient scanning probe scaling method of one kind.
One kind scanning probe scaling method, is demarcated for the scanning probe to PSTM, including following
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 demarcation 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 demarcation is qualified.
In one of the embodiments, the scanning probe scaling method also includes：
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 the scanning probe is modified；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.
In one of the embodiments, the data categorization module passes through SVMs machine learning method or nerve
The machine learning method of network is obtained.
In one of the embodiments, the side of the data categorization module is obtained by the machine learning method of neutral net
Method, is specifically included：
Select multigroup image information to be learned；
After the progress classification storage of described image information, multitiered network is set up for the characteristic information of described image information
Structure；
To the multitiered network structure setting function corresponding relation, the function pair in each layer network structure
Middle arrange parameter should be related to；
The parameter in each layer network structure is constantly adjusted to form the data categorization module.
In one of the embodiments, the machine learning method by SVMs obtains the data classification mould
The method of block, is specifically included：
Select multigroup image information to be learned；
The characteristic information extraction from described image information, different classifying rules, institute are set up for different characteristic informations
The process of setting up for stating classifying rules is to carry out coordinate transform to described image information；
By setting different weight ratios between the different classifying rules, to form the data categorization module.
In one of the embodiments, it is described by it is described scanning probe obtain calibration sample shape appearance figure the step of it is specific
Including：
The scanning probe is controlled to be moved horizontally with fixed step-length by the PSTM；
The scanning probe obtains the density of electronic states value on the calibration sample every bit position in moving process,
To constitute the shape appearance figure.
In one of the embodiments, the scanning probe scaling method also includes：The shape appearance figure is normalized
Processing；
It is described the shape appearance figure to be normalized the shape appearance figure is carried out going background process, the shape is filtered out
The noise of looks figure, the characteristic information to the shape appearance figure carry out intensive treatment.
In one of the embodiments, after the shape appearance figure by the scanning probe acquisition calibration sample, institute
Stating scanning probe scaling method also includes：
Binary conversion treatment is carried out to the shape appearance figure, the impurity point edge in the shape appearance figure is found；
Judge the major 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.
In one of the embodiments, it is described to it is described scanning probe be modified the step of specifically include：
The control system carried by SEM 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 insert the scanning probe into the calibration sample to be measured so that the needle point of the scanning probe reams one
Individual or multiple atoms suck one or more atoms.
In one of the embodiments, if the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe
Scaling method also includes：
Judge whether the scanning probe is stablized by the data categorization module；
If the scanning probe is unstable, the scanning probe is modified.
The scanning probe scaling method that the present invention is provided, the shape appearance figure and power spectrum of calibration sample are obtained by scanning probe
Figure, by data categorization module integrate it is described judge whether shape appearance figure and the energy spectrum diagram qualified, being capable of quick, high-precision inspection
Survey whether the scanning probe is qualified, realize the demarcation of the scanning probe.The scanning probe scaling method, using the number
Judge whether the scanning probe is qualified according to sort module analysis, reduce the consuming time of the scanning probe calibration process
The problem of length, big demarcation difficulty.
Brief description of the drawings
The flow chart for the scanning probe scaling method that Fig. 1 provides for one embodiment of the invention；
The flow chart for the scanning probe scaling method that Fig. 2 provides for another embodiment of the present invention；
Fig. 3 is the qualified shape appearance figure in gold (111) surface that the scanning probe test that one embodiment of the invention is provided is arrived；
The underproof shape appearance figure in gold (111) surface that Fig. 4 arrives for the scanning probe test that one embodiment of the invention is provided；
The shape appearance figure that test in the scanning probe scaling method that Fig. 5 a provide for one embodiment of the invention is obtained；
The energy spectrum diagram that the test for the scanning probe scaling method that Fig. 5 b provide for one embodiment of the invention is obtained；
Test synthesis analysis chart in the scanning probe scaling method that Fig. 5 c provide for one embodiment of the invention；
The picture qualified through qualified scanning probe scanning that Fig. 6 provides for one embodiment of the invention；
Fig. 7 scans the underproof picture of probe scanning for what one embodiment of the invention was provided through underproof；
The flow chart for the scanning probe scaling method that Fig. 8 provides for further embodiment of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with drawings and Examples pair
The scanning probe scaling method of the present invention is further described.It should be appreciated that specific embodiment described herein is only used to
The present invention is explained, is not intended to limit the present invention.
It please participate in Fig. 1 and scan probe scaling method there is provided one kind, for the scanning probe progress to PSTM
Demarcation, comprises the following steps：
S100, the shape appearance figure of calibration sample is obtained 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
The probe that pin is used when can be PSTM test sample.The calibration sample can be the to be scanned of known properties
Film, bulk or other.The shape appearance figure can scan probe from a panel region by PSTM control
Start on one angle.Every time when it is described scanning probe implement scanning when, control it is described scanning probe pinpoint movement fix 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, judgement is standardized by data categorization module to the shape appearance figure.
In step S200, the data categorization module can be the grader set up by the method for various machine learning.
A certain specific judgement can be trained and learnt to the data categorization module or the grader according to the specific shape appearance figure
Standard.Specifically, the shape appearance figure obtained in step S100 is input into the data categorization module or the grader.Institute
A certain specific criterion that data categorization module or the grader learn before is stated to the calibration sample
The shape appearance figure is standardized judgement.It is appreciated that can show whether the shape appearance figure is qualified after standardization judges.
The whether qualified result of the shape appearance figure that is drawn after normalized judgement is probably a probable value, and for the shape appearance figure
Described a certain specific criterion can the method for machine learning obtain.
S300, if the data categorization module judges that the shape appearance figure is qualified, obtains described by the scanning probe
The energy spectrum diagram of calibration sample.
After the data categorization module judges that the shape appearance figure is qualified, then it can be entered by the PSTM
The one step control scanning probe obtains the energy spectrum diagram of the calibration sample.The acquisition of the energy spectrum diagram of the calibration sample is needed
Qualified rear progress is determined in the shape appearance figure of the calibration sample.Obtaining the energy spectrum diagram of the calibration sample
When, it is necessary to first the reconnaissance in the shape appearance figure of the calibration sample, then tested again.
Power spectrum refers to that impulse amplitude just can obtain distribution curve of the counting rate with particle energy 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 during tunnelling
Degree is proportional.And by measuring tunnelling current under different voltages, using conductance G=I/V, voltage can be obtained for transverse 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
Concentrate in some specific scopes, some particular ranges that the energy of electronics is concentrated are referred to 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 is represented, accommodates electronics under the energy
Number.So differential conductance curve is just directly proportional with density of electronic states, can just measure solid by measuring differential conductance curve
The power spectrum of body.Such as, it is used as the calibration sample using golden (111) in test process.The power spectrum of the calibration sample golden (111)
It is known, whether the needle point that the scanning probe can be demarcated with the power spectrum of known golden (111) is qualified.
S400, judgement is standardized by the data categorization module to the energy spectrum diagram.
In step S400, the data categorization module equally can be the classification set up by the method for various machine learning
Device.The data categorization module or the grader can train according to the specific energy spectrum diagram and learn a certain specifically to sentence
Disconnected standard.Specifically, the energy spectrum diagram obtained in step S300 is input into the data categorization module or the grader.
The power spectrum of the criterion that the data categorization module or the grader learn before to the calibration sample
Figure is standardized judgement.It is appreciated that the data categorization module or the grader herein is to the shape appearance figure and institute
The analysis for stating energy spectrum diagram judges that different criterions can be used.The data categorization module or the grader can be directed to
Different criterions is arrived in the shape appearance figure and energy spectrum diagram study.That is, the data categorization module or described point
Class device, which can be trained, identifies whether the shape appearance figure and the energy spectrum diagram are qualified, so as to further judge that the scanning probe is
It is no qualified.The shape appearance figure and the whether qualified result of the energy spectrum diagram drawn after normalized judgement is probably a probability
Value.
S500, if the data categorization module judges that the energy spectrum diagram is qualified, the scanning probe demarcation 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 test is arrived are qualified, now can determine that the scanning probe demarcation is qualified.Afterwards
In continuous experiment, test experiments can be carried out to new test sample by the scanning probe.
PSTM needs to be operated and measure in ultrahigh vacuum, it is to avoid molecular density fluctuation in air,
The tunnelling signal fluctuation that thermal fluctuation is brought.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, have a strong impact on experimental performance.Therefore the scanning probe can not be from when measuring sample
Open ultrahigh vacuum.That is, " in site measurement " needs to carry out in ultrahigh vacuum, the otherwise fluctuation in air can direct interference
Measurement data.Probe scaling method is scanned described in the present embodiment to carry 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 of which atom.In addition, in the whole scanning probe calibration process, the scanning probe and the calibration sample
In all the time have certain slight distance.The scanning probe measurement is the tunnelling current for passing through vacuum.Due to the scanning
The needle point of probe is very small, for realizing atomic level resolution ratio, may only one of which on the needle point of the scanning probe
Atom.The needle point yardstick 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 the high-precision collection of composite calibration needs and analysis
Mass data, these repeated labors are analyzed and drawn corresponding conclusion by the data categorization module.Therefore, it is of the invention
The scanning probe scaling method provided, quick, high-precision can detect whether scanning probe is qualified, realizes probe mark
It is fixed.The scanning probe scaling method, reduces the consuming time length of scanning probe calibration process, demarcates the problem of difficulty is big.
Referring to Fig. 2, in one embodiment, the scanning probe scaling method also includes：
S600, if the data categorization module judges that the shape appearance figure is unqualified or the data categorization module judges institute
State energy spectrum diagram unqualified, then the scanning probe is modified；And
S700, after being modified to the scanning probe, further returns to described obtained by the scanning probe and demarcates
The step of shape appearance figure of sample.
Amendment to the scanning probe can include carrying out bulk processing and to the scanning probe 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.
Advance very short distance to the calibration sample because the correcting mode of the scanning probe is the control scanning probe, then move back
Go back to original position.The scanning probe normal work distance 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 strengthen rapidly, so as to pull institute
The needle point pattern of the scanning probe adjustment scanning probe is stated, makes the needle point of the scanning probe keep needle point to only have one as possible
The state of atom.For example, making the needle point advance 0.4nm of the scanning probe retract again, holding -4V bias 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.Thin processing is carried out to the scanning probe thick relative to being carried out to the scanning probe
Processing, simply the needle point advance amount and bias of the scanning probe 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 and use 0.4nm, -4V.Needle point advance amount for the scanning probe can be visited for the scanning
The step value that pin is moved every time.Foregoing bias is the voltage of the needle point relative to the calibration sample for scanning probe.Can
To set the needle point of the scanning probe to be connected respectively by the two poles of the earth of same controllable voltage source with the calibration sample, so may be used
With the convenient size for changing 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 set to start to be scanned new test sample in data categorization module
Experiment.
When not completing the scanning probe demarcation, i.e., described scanning probe, can be with initialization system not by demarcation
The tips quality standard of the scanning probe.For example：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
Qualified measurement figure can be obtained, then it is assumed that the tips quality of the scanning probe is problematic, it is necessary to be carried out to the scanning needle point
Bulk processing.If not terminating current demarcation by the test of the shape appearance figure, in the pin of current region processing scanning probe
Point, then removes the region by the needle point for scanning probe (region may be destroyed).After being modified to scanning probe
Come back to pin under starting point, scanning probe and restart demarcation.If not by the test of the energy spectrum diagram, in completion pair
The step of judgement is standardized to the shape appearance figure by data categorization module described in rebound 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 added so that the scanning probe can be
During in-situ test without departing from existing equipment, do not change test environment in the case of, complete to it is described scanning probe amendment.
This step also causes the scanning probe scaling method more quick and intelligence.
In one embodiment, in the scanning probe calibration process, the calibration sample of use is of high quality, institute
State it is that the property of calibration sample is also appreciated that it is clear that therefore PSTM collect the quality of data approximately can be straight
The reversed tips quality for reflecting the scanning probe., can be with using coarse adjustment piezoelectric ceramics in the case where the calibration sample is motionless
The needle point of the scanning probe is set to be moved in the range of 300nm × 300nm.And when carrying out the shape appearance figure collection every time, need
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 300nm × 300nm regions described in per secondary control,
A figure is scanned every 40nm.For example can be in the way of "the" shape be scanned in entire area traveling, every time first along X side
To motion, made a move to border along Y-direction, then move to X opposite directions.
In one embodiment, need to select on the shape appearance figure before the calibration sample is carried out energy spectrum diagram test
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 be gone out by lock-in amplifier direct measurement, reflect the mark
The spectral information of random sample product.Need in the difference of the differential conductance curve in two regions, the peak value near searching -460mV biass.
, will be using computer program fitting to two after the scanning probe scanning goes out the differential conductance curve corresponding to two regions
Individual spike is analyzed, so as 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 experiment, can pass through program setting
Experimenter is notified to start experiment.
In one embodiment, the S100, the step of obtaining the shape appearance figure of calibration sample by the scanning probe has
Body includes：
The scanning probe is controlled to be moved horizontally with fixed step-length by the PSTM；
The scanning probe obtains the density of electronic states value on the calibration sample every bit position in moving process,
To constitute the shape appearance figure.
It is described scanning probe scaling method idiographic flow be：PSTM is controlled to scan the needle point of probe, by
Gradually close to the calibration sample.The needle point of the scanning probe is reached apart from the position of the calibration sample number angstrom, it is possible to achieve
Sensitive tunnelling.After the needle point of the scanning probe reaches the position that can be tested, by controlling two pieces of X/Y directions pressure
Voltage on electroceramics, control piezoelectric ceramics is flexible so that the needle point of the scanning probe carries out level shifting with fixed step-length
It is dynamic.In moving process, the bias of the needle point of the fixed scanning probe measures the size of tunnelling current, the electric current in every bit
Value can reflect the density of electronic states value of this lower point of bias, so that correspondence obtains on sample the point to the needle point for scanning probe
The information such as distance and the space of points charge density.The scanning probe is in 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 be analyzed from these data
Go out the information such as surface topography and property, the density of electronic states of sample.
In one embodiment, the scanning probe scaling method also includes the shape appearance figure is normalized.
The concrete mode of the normalized is that the shape appearance figure is carried out going background process, the noise, right of the shape appearance figure is filtered out
The characteristic information of the shape appearance figure carries out intensive treatment.Background process is gone to can be understood as filtering out because the calibration sample is being surveyed
The shape appearance figure has out-of-flatness, irregular situation caused by out-of-flatness or other reasonses are put during examination.By removing the back of the body
Scape processing is filtered out due to the test error to the calibration sample that test process is brought.Noise processed is filtered out to can be understood as pair
Some uncertain, insignificant noise characteristics of the shape appearance figure are filtered out, to increase to the shape appearance figure result of determination
Accuracy.Characteristic information intensive treatment, which can be understood as doing the characteristic information of the shape appearance figure, strengthens or protrudes processing with side
Just the classification to the shape appearance figure is integrated.The shape appearance figure is carried out going background process, noise processed is filtered out and characteristic information
Intensive treatment can be carried 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
Body, more obvious, the more convenient data categorization module progress classification discrimination.
In the scanning probe scaling method, the calibration sample can be used as using golden (111) surface.Gold is with structure cell
One summit is that origin makees rectangular coordinate system in space, makes x, y, three sides of z-axis positive direction and structure cell are overlapping, and set the structure cell length of side as
1, golden (111) face is x=1, y=1, the plane of 3 points of determinations of z=1.The property of golden (111) is well known.Golden conduct
Extraordinary metal, electric charge is very free, and its tunnelling property is easy to measurement.Golden (111) surface has distinct electronic structure special
Levy, be easy to application scanning tunnel microscope to measure.For example in calibration sample surface gold atom in order to reach energy most
Low state, can form the structure that two kinds of arrangement modes (being referred to as hcp and fcc) interlock.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 of hcp and fcc are separated by these bright borders.Hcp and two regions of fcc are all dark.Hcp regions are wider, fcc regions compared with
It is narrow.When using the scanning of PSTM 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 Fishbone that light and shade interlocks.In the shape appearance figure, the scanning probe is swept
The quality of the striped described is used as a whether qualified standard of the needle point for weighing the scanning probe.Fig. 3 and Fig. 4 are referred to,
Fig. 3 is the qualified shape appearance figure in golden (111) surface, and wide black is hcp, and narrow black is fcc, here it can also be seen that turning round existing
As.The size that also show 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 of hcp and fcc can not be shown in Fig. 4.
By it is described scanning probe obtain sample the energy spectrum diagram the step of be specially：Be judged as it is qualified described in
Arbitrarily rule in shape appearance figure.The line with different in width striped is chosen as p-wire, and (p-wire as shown in Figure 3, can be with
Understand that the p-wire can arbitrarily be chosen, two regions of hcp and fcc can be found).It can be distinguished simultaneously on the p-wire
Measure the width in two regions of hcp and fcc.The different adjacent hcp and fcc areas of multigroup width can be obtained on the p-wire
Domain.Measure the spacing of the central point in the adjacent hcp and fcc regions of each group of different in width.Hcp regions central point and
The central point in fcc regions is as test point, and in two above test point, the lower section scanning probe goes detection respectively.By changing
The scanning probe can measure differential conductance curve, i.e., described energy spectrum diagram in the bias added by above-mentioned test point.Will be at two
The curve that region is measured subtracts each other, and can measure a peaks characteristic near -460mV biass.Specifically, in hcp regions
First group of V-I value is measured at heart point, the central spot in narrow fcc regions 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 the 3rd group of V-I value.If the 3rd group of V-I value can obtain spike,
The needle point demarcation of the scanning probe is qualified.If the 3rd group of V-I is worth less than spike, to the pin of the scanning probe
Point is modified.
Refer to the shape appearance figure for showing to measure in one embodiment in Fig. 5 a, Fig. 5 b, Fig. 5 c, Fig. 5 a.In Fig. 5 a
It also show the test point in hcp regions and the test point in fcc regions.Fig. 5 b show that the test point in hcp regions is (in Fig. 5 b deep
The power spectrum of black) and fcc regions two power spectrums gathering respectively of test point (power spectrum of light/dark balance in Fig. 5 b) place.Fig. 5 c are figures
The difference of two power spectrums in 5b, it can clearly be seen that there is a peak value near -480mV.Comprehensive analysis Fig. 5 b and Fig. 5 c can be obtained
Go out this time scanning probe demarcation qualified.Reconstruct (turning phenomenon) typically occurs in the scanning process of the shape appearance figure,
This reconstruct is general to occur turn per 280nm, and some small impurity particles are easily absorbing in corner；When scanning probe
Needle point it is in good condition when, picture of these particles in the shape appearance figure be less circular bright spot.If these impurity
Particle deviates circular 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
Also include：
Binary conversion treatment is carried out to the shape appearance figure, the impurity point edge in the shape appearance figure is found；
Judge the major 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 can also be judged it is appreciated that whether the shape appearance figure is qualified by impure point present in the shape appearance figure.
Refer to Fig. 6 and Fig. 7, Fig. 6 is through the qualified picture of the qualified scanning probe scanning, it can be seen that impure point is in Fig. 6
Circular.In Fig. 6, binary conversion treatment is carried out to the shape appearance figure, the impurity point edge in the shape appearance figure is found.Impure point side
The major 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 is arrived.Impure point deviates circular in Fig. 7, illustrates the needle point of the scanning probe and has partially
Difference, the major axis of impurity point edge and the ratio of short axle differ greatly with 1.Test pictures shown in Fig. 7 are unqualified.
In one embodiment, obtained by the machine learning method of SVMs or the machine learning method of neutral net
Obtain the data categorization module.
The quality of the needle point of the scanning probe is typically used and good data comparison is judged in history, present invention profit
Handled with machine learning.Specifically, the shape appearance figure scanned can be sent to Matlab programs and be carried out automatically
Change data analysis.The program is designed using machine learning techniques (convolution artificial neural network, SVMs etc.), using huge
PSTM technology be trained, can with the quality of higher degree of accuracy identification good data in history so that
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 programs to experiment
Flow is automatically controlled, so that significant increase conventional efficient.In the Matlab programs that the calculating of program is generally noted above,
LabVIEW programs realize automation, unattended control.It is appreciated that the method for machine learning has a variety of, and not only limit
The SVMs or the machine learning method of neutral net 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 current main flow
Speech is LabVIEW, because its company National Instruments provide corresponding program for various scientific instrument and connect
Mouthful.Authorized in theory, can access these instruments with other language, various program languages such as Python, C++, C# is
Automatic control system can be write, wherein database preparation is realized using machine learning, so as 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, such as, and the test data in golden (111) face, using ANN
Network, SVMs scheduling algorithm carry out machine learning to historical data, learn a classifier functions.If the scanning is visited
The needle point of pin 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
Hcp and fcc regional centers position carries out the measurement of energy spectrum diagram.The machine learning program of Land use models identification can be from the pattern
Both regions are recognized in figure and the coordinate of their centers is provided.By marking p-wire in the shape appearance figure, find
Test point, the energy spectrum diagram is obtained by the scanning probe.The data categorization module is completed to institute by the energy spectrum diagram
State the evaluation of the tips quality of scanning probe.It is " qualified " or " unqualified " to evaluate the scanning probe.By training and learning
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
State after the judged result that data categorization module provides " qualified " or " unqualified ", different steps pair is used further according to judged result
The scanning probe is operated accordingly.
In one embodiment, the data categorization module, specific bag are obtained by the machine learning method of neutral net
Include：
Select multigroup image information to be learned；
After the progress classification storage of described image information, multitiered network is set up for the characteristic information of described image information
Structure；
To the multitiered network structure setting function corresponding relation, the function pair in each layer network structure
Middle arrange parameter should be related to；
The parameter in each layer network structure is constantly adjusted to form the data categorization module.
Multigroup image information to be learned is selected as training data.The training data is inputted into the data classification mould
Block is stored.The training data of input is normalized the data categorization module.For example, shape appearance figure is inputted
One matrix, its brightness is normalized.Input data is regarded as a point of higher dimensional space, a vector (tensor), can
So that 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 add
Upper offset vector b, that is, being y=wTX+b computings, obtain mapping value.
Multigroup image information to be learned is stored and normalized after, for described to be learned
The characteristic information of image information sets up 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 last hidden neuron layer.Parameter is mapped as 0 using decision function
A number between~1, for classifying, such as 1 represent it is qualified, 0 represent it is unqualified, at this moment map out come number be exactly qualified
Probability.
The training data can be the corresponding true classification of each data point clearly known, then will truly classify
It is compared with classification out calculated above, obtains the accuracy p under the situation.Then, whole network is iterated, and is utilized
The methods such as least square, maximum likelihood, gradient decline, constantly change the weight matrix of each layer of neuron, until making accuracy p
Obtain maximum.This just completes the training to the whole data categorization module.
After the completion of the training of the data categorization module, a series of weight matrix w can be obtained1, w2... wn.Power
Weight matrix w1, w2... wnEach layer of neutral net is corresponded to respectively.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 SVMs, specifically
Including：
Select multigroup image information to be learned；
The characteristic information extraction from described image information, different classifying rules, institute are set up for different characteristic informations
The process of setting up for stating classifying rules carries out coordinate transform to described image information；
Different weight ratios are set between the different classifying rules, to form the data categorization module.This reality
Apply in example, the data categorization module is set up using the machine learning method of SVMs.By from multigroup image information
Middle characteristic information extraction, then sets up different classifying rules for different characteristic informations.According to test needs (survey herein
Examination needs can be that the quality requirement of image information to be judged is different), different power are set between different classifying rules
Compare again, to form the data categorization module.It is appreciated that the data categorization module can train a variety of classification gauges of generation
Then.For example：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 can also be generated when the scanning probe steady judges corresponding
Classifying rules, judges the stability data for scanning probe, so that whether stablizing for the scanning probe drawn.
In one embodiment, the S600, the step of being modified to the scanning probe specifically includes：
The control system carried by SEM applies specific voltage waveform to the scanning probe so that
The needle point of the scanning probe reams one or more atoms or sucks one or more atoms；Or by it is described scanning probe to
Inserted in testing sample so that the needle point of the scanning probe reams one or more atoms or sucks one or more originals
Son.It is appreciated that the step of being modified to the scanning probe can be with it is not limited to both the above.If can be former
The method of the position amendment scanning probe tip can also be attached in 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
Retouching probe scaling method also includes：
Whether S800, the needle point for judging the scanning probe by the data categorization module is stablized, if the scanning is visited
Pin is unstable, then the scanning probe is modified.
The flow chart being estimated to 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 PSTM measurement process scan probe needle point away from
It is very near from the calibration sample, it is easy to be acted on by power such as calibration sample surface Van der Waals and occur form
Change.The needle point of the scanning probe changed can provide different data for identical measurement point, so as to cause this
Preceding measurement result all fails.Accordingly, it would be desirable to by the needle point of the mobile scanning probe, close to the calibration sample, and
Judge it is this during the pattern of needle point of the scanning probe whether can change, to judge the needle point of the scanning probe
Stability.To repeat processing for the needle point of the unstable scanning probe, 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 can include：By PSTM to described
Scan probe and apply 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 the scanning needle point stability is judged, before the needle point completion once that equally also control the scanning probe
Enter/backward movement, now require that the tunnelling current curve scanned does not change, that is, prove that the needle point of the scanning probe exists
The close calibration sample will not change when measurement, the needle point performance of the scanning probe is stable.If in advancing
Electric current also no longer changes when exceeding the withdraw of the needle after range, remains the state more than range, then shows the pin of the scanning probe
Point has been adsorbed onto some atoms on the calibration sample.At this time, it may be necessary to be handled some to remove to the scanning probe
Atom, then demarcate the scanning probe.If some immediate current reduces suddenly, have on the needle point for showing the scanning probe
A part of atom comes off.Can with the needle point for ensureing the scanning probe at this time, it may be necessary to be handled the scanning probe
Test request is met, and demarcates the scanning probe again.
If the needle point of the scanning probe has passed through the demarcation of the shape appearance figure but not by the demarcation of the energy spectrum diagram,
Then represent that the needle point of the scanning probe is problematic but not serious.At this point it is possible to from more small value to the scanning probe
Needle point be modified.Amendment to the needle point of the scanning probe first occurs at the needle point of the scanning probe not by surveying
During examination.Determination of stability can just be entered by the needle point of all scanning probes of test.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply 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, the scope of this specification record is all considered to be.
Embodiment described above only expresses the several embodiments of the present invention, and it describes more specific and detailed, but simultaneously
Can not therefore it be construed as limiting the scope of the patent.It should be pointed out that coming for one of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
1. one kind scanning probe scaling method, is demarcated for the scanning probe to PSTM, it is characterised in that
Comprise the following steps：
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 demarcation is qualified.
2. probe scaling method is scanned as claimed in claim 1, it is characterised in that the scanning probe scaling method is also wrapped
Include：
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.
3. as claimed in claim 1 scanning probe scaling method, it is characterised in that the data categorization module by support to
The machine learning method of amount machine or the machine learning method of neutral net are obtained.
4. probe scaling method is scanned as claimed in claim 3, it is characterised in that pass through the machine learning method of neutral net
The method for obtaining the data categorization module, is specifically included：
Select multigroup image information to be learned；
After the progress classification storage of described image information, multitiered network knot is set up for the characteristic information of described image information
Structure；
To the multitiered network structure setting function corresponding relation, the function pair in each layer network structure should be closed
Arrange parameter in system；
The parameter in each layer network structure is constantly adjusted to form the data categorization module.
5. probe scaling method is scanned as claimed in claim 3, it is characterised in that the engineering by SVMs
The method that learning method obtains the data categorization module, is specifically included：
Select multigroup image information to be learned；
The characteristic information extraction from described image information, different classifying rules, described point are set up for different characteristic informations
The process of setting up of rule-like is to carry out coordinate transform to described image information；
By setting different weight ratios between the different classifying rules, to form the data categorization module.
6. probe scaling method is scanned as claimed in claim 1, it is characterised in that described to pass through the scanning probe acquisition mark
The step of shape appearance figure of random sample product, specifically includes：
The scanning probe is controlled to be moved horizontally with fixed step-length by the PSTM；
The scanning probe obtains the density of electronic states value on the calibration sample every bit position, with structure in moving process
Into the shape appearance figure.
7. probe scaling method is scanned as claimed in claim 6, it is characterised in that the scanning probe scaling method is also wrapped
Include：The shape appearance figure is normalized；
It is described the shape appearance figure to be normalized the shape appearance figure is carried out going background process, the shape appearance figure is filtered out
Noise, intensive treatment is carried out to the characteristic information of the shape appearance figure.
8. probe scaling method is scanned as claimed in claim 1, it is characterised in that pass through the scanning probe acquisition described
After the shape appearance figure of calibration sample, the scanning probe scaling method also includes：
Binary conversion treatment is carried out to the shape appearance figure, the impurity point edge in the shape appearance figure is found；
Judge the major 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.
9. probe scaling method is scanned as claimed in claim 1, it is characterised in that if the data categorization module judges described
Energy spectrum diagram is qualified, and the scanning probe scaling method also includes：
Judge whether the scanning probe is stablized by the data categorization module；
If the scanning probe is unstable, the scanning probe is modified.
10. the scanning probe scaling method as any one of claim 2,8 or 9, it is characterised in that described to be swept to described
The step of probe is modified is retouched to specifically include：
The control system carried by SEM is to the scanning probe making alive so that the pin of the scanning probe
Taper falls one or more atoms or sucks one or more atoms；
Or by it is described scanning probe insert into the calibration sample to be measured so that it is described scan probe needle point ream one or
Multiple atoms suck one or more atoms.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109142358A (en) * | 2018-08-22 | 2019-01-04 | 王天骄 | Metal material mechanics performance test methods neural network based |
| CN110770624A (en) * | 2017-06-29 | 2020-02-07 | 科磊股份有限公司 | Objective calibration for scanning electron microscopes |
| CN112730895A (en) * | 2020-12-22 | 2021-04-30 | 中国科学院物理研究所 | Atom/molecule carrying method and system |
| EP3848860A4 (en) * | 2018-09-05 | 2021-11-17 | Alibaba Group Holding Limited | Qubit detection system and detection method |
| CN114046959A (en) * | 2021-12-01 | 2022-02-15 | 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 |
| CN114581343A (en) * | 2022-05-05 | 2022-06-03 | 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 |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1950516A (en) * | 2003-12-31 | 2007-04-18 | 英特尔公司 | Methods and compositions for detecting nucleic acids using scanning probe microscopy and nanocodes |
| CN201974444U (en) * | 2009-11-12 | 2011-09-14 | 国家纳米技术与工程研究院 | Instrument for non-destructive evaluation of nano-scale glass microprobe performance |
| CN104730293A (en) * | 2015-03-27 | 2015-06-24 | 华中科技大学 | Calibration device and calibration method of white light interference atomic-power scanning probe |
| CN104931732A (en) * | 2015-06-17 | 2015-09-23 | 扬州大学 | Micronano metal fiber surface topography measuring device, use method thereof and movement distance measuring method of drive in device |
| US20170131322A1 (en) * | 2014-04-23 | 2017-05-11 | Oxford Instruments Asylum Research, Inc. | AM/FM Measurements Using Multiple Frequency of Atomic Force Microscopy |
| CN106940389A (en) * | 2017-02-06 | 2017-07-11 | 华中科技大学 | White light interference atomic force probe caliberating device and scaling method that a kind of super-resolution can trace to the source |
-
2017
- 2017-07-31 CN CN201710641861.1A patent/CN107300629B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1950516A (en) * | 2003-12-31 | 2007-04-18 | 英特尔公司 | Methods and compositions for detecting nucleic acids using scanning probe microscopy and nanocodes |
| CN201974444U (en) * | 2009-11-12 | 2011-09-14 | 国家纳米技术与工程研究院 | Instrument for non-destructive evaluation of nano-scale glass microprobe performance |
| US20170131322A1 (en) * | 2014-04-23 | 2017-05-11 | Oxford Instruments Asylum Research, Inc. | AM/FM Measurements Using Multiple Frequency of Atomic Force Microscopy |
| CN104730293A (en) * | 2015-03-27 | 2015-06-24 | 华中科技大学 | Calibration device and calibration method of white light interference atomic-power scanning probe |
| CN104931732A (en) * | 2015-06-17 | 2015-09-23 | 扬州大学 | Micronano metal fiber surface topography measuring device, use method thereof and movement distance measuring method of drive in device |
| CN106940389A (en) * | 2017-02-06 | 2017-07-11 | 华中科技大学 | White light interference atomic force probe caliberating device and scaling method that a kind of super-resolution can trace to the source |
Non-Patent Citations (2)
| Title |
|---|
| 周兵 等: "基于人工神经网络的SPM自动标定", 《光学技术》 * |
| 孙鑫 等: "基于神经网络的扫描探针显微镜标定技术研究", 《节能环保 和谐发展——2007中国科协年会论文集（一）》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110770624A (en) * | 2017-06-29 | 2020-02-07 | 科磊股份有限公司 | Objective calibration for scanning electron microscopes |
| CN110770624B (en) * | 2017-06-29 | 2021-10-08 | 科磊股份有限公司 | Objective calibration for scanning electron microscopes |
| CN109142358A (en) * | 2018-08-22 | 2019-01-04 | 王天骄 | Metal material mechanics performance test methods neural network based |
| EP3848860A4 (en) * | 2018-09-05 | 2021-11-17 | Alibaba Group Holding Limited | Qubit detection system and detection method |
| TWI803659B (en) * | 2018-09-05 | 2023-06-01 | 香港商阿里巴巴集團服務有限公司 | Quantum bit detection system and detection method |
| CN112730895A (en) * | 2020-12-22 | 2021-04-30 | 中国科学院物理研究所 | Atom/molecule carrying method and system |
| CN114046959A (en) * | 2021-12-01 | 2022-02-15 | 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 |
| 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 |
| CN114581343A (en) * | 2022-05-05 | 2022-06-03 | 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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