CN109580764B - SIMS (separation of materials and materials) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC - Google Patents
SIMS (separation of materials and materials) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC Download PDFInfo
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Abstract
The invention provides a SIMS (separation of principal component analysis) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC, which comprises the following steps: step S1, evaporating graphene on the surface of the sample; s2, placing the sample in a sample introduction chamber of a secondary ion depth analyzer, and vacuumizing; step S3, introducing oxygen into the sample introduction chamber; step S4, bombarding the sample by using argon cluster ions and oxygen ions together; step S5, adjusting the pulse width of the extracted voltage and the analysis frame number of each cycle period; step S6, collecting secondary ions through a mass analyzer; step S7, analyzing the secondary ions to obtain a depth profile and a secondary ion mass distribution image; and step S8, obtaining the detection result of the trace impurity elements in the sample according to the depth profile and the secondary ion mass distribution image. The technical scheme of the invention can optimize and detect the concentration and distribution of trace impurity elements in the semi-insulating GaAs and SiC.
Description
Technical Field
The invention relates to the technical field of material detection, in particular to an SIMS (simple independent modeling system) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC.
Background
The semiconductor substrate can be generally classified into a p-type low resistance, an n-type low resistance, a high resistance, and a semi-insulating type according to the magnitude of resistivity. Wherein the semi-insulating substrates have a resistivity of more than 107The omega-cm semiconductor substrate can effectively realize charge isolation, reduce parasitic capacitance effect and realize high-speed and high-frequency performance of devices, and is widely applied to the fields of microelectronics (HEMT, HBT, MISFET, MOSFET and the like) and photoelectronics (high-speed photodetectors). Common semi-insulating materials include GaAs, SiC, and the like.
The inventor finds that impurities in semi-insulating materials such as semi-insulating GaAs, SiC and the like have certain adverse effects on the performance of the semi-insulating materials, and particularly has great guiding significance for the research and development of precise electronic circuits and advanced military supplies and weaponry by accurately obtaining the parameters based on the functionalized devices of advanced semiconductor materials. However, no detection method or means can carry out SIMS optimization detection on trace impurity elements in semi-insulating GaAs or semi-insulating SiC at present.
Disclosure of Invention
The invention provides a SIMS (separation by mass spectrometry) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC, which can carry out SIMS optimization detection on trace impurity elements in the GaAs and SiC;
the invention provides a SIMS (simple in-situ chemical vapor deposition) optimization detection method for trace impurity concentration distribution in semi-insulating GaAs and SiC, which adopts the following technical scheme:
the SIMS optimization detection method comprises the following steps:
step S1, evaporating graphene on the surface of the sample;
s2, placing the sample with the surface coated with the graphene in a sample introduction chamber of a secondary ion depth analyzer, and vacuumizing;
step S3, introducing oxygen into the sample introduction chamber;
step S4, bombarding the sample by using argon cluster ion beams and oxygen ion beams together so as to sputter secondary ions from the sample;
step S5, adjusting the pulse width of the extracted voltage and the analysis frame number of each cycle period;
step S6, collecting the secondary ions through a mass analyzer in the secondary ion depth analyzer;
step S7, analyzing the secondary ions through the mass analyzer to obtain a depth profile and a secondary ion mass distribution image;
step S8, obtaining the detection result of the trace impurity elements in the sample according to the depth profile and the secondary ion mass distribution image;
optionally, in the step S1, the thickness of the graphene is 1 to 20 micrometers.
Optionally, in the step S2, the vacuum degree of the sample chamber after vacuum pumping is 1.0 × 10-8Pa～5.0×10-8Pa；
Optionally, in the step S3, 100ml of oxygen is introduced into the sample injection chamber;
optionally, in the step S4, the energy of the argon cluster ion beam is 10kV, and the beam intensity is2×10-6A/cm2The energy of the oxygen ion beam was 200V, and the beam intensity was 3.2X 10-4A/cm2The scanning area of the argon cluster ion beam is 100X 100 μm2The scanning area range of the oxygen ion beam is 100 multiplied by 100 mu m2The ion incidence angles are all 45 degrees;
alternatively, in the step S5, the pulse width of the extracted voltage is 30ns, and the number of analysis frames per cycle period is 10;
optionally, the step S8 includes:
obtaining the types of the trace impurity elements in the sample according to the depth profile;
obtaining the relation between the concentration and the depth of the trace impurity elements in the sample according to the secondary ion mass distribution image;
optionally, the obtaining the types of the trace impurity elements in the sample according to the depth profile includes:
determining the type of the impurity element corresponding to each peak according to the charge-to-mass ratio of each peak in the depth profile;
optionally, the obtaining the relationship between the concentration and the depth of the trace impurity element in the sample according to the secondary ion mass distribution image includes:
according to the curve relation between the secondary ion intensity and the sputtering time in the secondary ion mass distribution image, the depth and the concentration of the trace impurity elements are calculated by using the following formula:
depth is time x sputtering rate;
the concentration of trace impurity elements ═ (secondary ion signal intensity ÷ reference signal intensity) × (relative sensitivity factor);
simulating the relation between the concentration and the depth of the trace impurity elements in the sample according to the calculated depth and the concentration of the trace impurity elements in a three-dimensional simulation mode;
optionally, in the step S6, the collection area of the secondary ions has the following relationship with the sputtering pit formed when the sample is bombarded with argon gas cluster ions and oxygen ions together: d is more than or equal to L +4 phi between the side length D of the sputtering pit and the side length L of the collection area, wherein phi is the diameter of the scanning area of the argon cluster ions;
the collection area satisfies the following formula: a ═ R · X + d (R · Y + d), where R is the ion beam diameter of the argon cluster ions, X is the window proportion in the X direction, Y is the window proportion in the Y direction, and d is the diameter determined by the transfer lens and the field stop;
the invention provides a SIMS (separation of materials by mass spectrometry) optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC, which is used for detecting trace impurity elements in the GaAs and SiC as follows: firstly, graphene is evaporated on the surface of a sample, then the sample with the graphene evaporated on the surface is placed in a sample introduction chamber of a secondary ion depth analyzer, vacuumizing is performed, oxygen is introduced into the sample introduction chamber, argon cluster ions and oxygen ions are used for bombarding the sample together to sputter secondary ions from the sample, the pulse width of extraction voltage and the analysis frame number of each cycle period are adjusted, secondary ions are collected through a mass analyzer in the secondary ion depth analyzer, the secondary ions are analyzed through the mass analyzer to obtain a depth analysis diagram and a secondary ion mass distribution image, and finally, the detection result of trace impurity elements in the sample is obtained according to the depth diagram analysis and the secondary ion mass distribution image.
The invention has the following beneficial effects:
1) the optimized detection method can carry out SIMS optimized detection on the types and concentrations of trace impurity elements in GaAs and SiC, and the detectable trace impurity elements have a plurality of types;
2) the optimized detection method of the invention has the advantages that the detection limit of the body concentration can reach ppb level, the detection precision of impurity elements can reach below 10%, and the resolution of the distribution of the impurity elements is less than 10 nm.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a SIMS optimized detection method according to an embodiment of the present invention;
FIG. 2 is a depth profile of secondary ions of trace impurity element P in SiC provided by an embodiment of the present invention;
fig. 3 is a depth profile of secondary ions of a trace impurity element Ca in SiC provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the technical features in the embodiments of the present invention may be combined with each other without conflict.
The embodiment of the invention provides an SIMS (simple in-situ modeling) optimization detection method for trace impurity elements in semi-insulating GaAs and SiC (in the following, referred to as the SIMS optimization detection method), and specifically, as shown in FIG. 1, the SIMS optimization detection method comprises the following steps:
and step S1, depositing graphene on the surface of the sample.
The graphene evaporated on the surface of the sample contributes to the proportion of secondary ions in a sputtering product obtained by sputtering in a later step, the intensity of a vertical coordinate in a subsequently obtained depth analysis graph can be greatly improved (the intensity can be increased by about two orders of magnitude), and the precision and the accuracy of a detection result are improved.
Optionally, in step S1, the thickness of the graphene on the surface of the sample is 1 to 20 micrometers, so that the thickness of the graphene is appropriate, and the graphene cannot be used effectively due to an excessively small thickness, and the test cost cannot be increased due to an excessively large thickness.
Optionally, before step S1, the surface of the sample may also be cleaned with acetone and absolute ethanol.
Alternatively, the sample may be a 1cm x 1cm sheet, cut from a large sheet of about 2 inches in diameter.
And S2, placing the sample with the graphene evaporated on the surface in a sample introduction chamber of a secondary ion depth analyzer, and vacuumizing.
Based on the above, in step S2, the vacuum degree of the sample inlet chamber after vacuum pumping is 1.0 × 10, which is selected in the embodiment of the present invention, based on the fact that the vacuum degree of the sample inlet chamber after vacuum pumping is too low, so that air can collide with argon cluster ions and oxygen ions used for subsequent bombardment seriously, the energy of the argon cluster ions and the oxygen ions reaching the sample is reduced, and the bombardment effect is not good, and the vacuum degree of the sample inlet chamber after vacuum pumping is too high, which is difficult to realize and has a great requirement on a vacuum pump-8Pa ～5.0×10-8Pa, e.g. 2.0X 10-8Pa。
And step S3, introducing oxygen into the sample injection chamber.
The inventors found that O is utilized2The activity of the argon cluster ions and the oxygen ions which are adopted subsequently can be enhanced, the secondary ion yield is improved, the depth resolution of the SIMS optimization detection method is favorably optimized, and the trace impurity concentration detection limit is improved.
If the oxygen introduced into the sample introduction chamber is too much and the oxygen concentration is too high, the vacuum degree in the sample introduction chamber is reduced, the ion energy generated by sputtering is reduced, the sputtering process is hindered, and if the oxygen introduced into the sample introduction chamber is too little and the oxygen concentration is too low, the activity of generated ions is too low, and the mutual collision capacity among the ions is weakened. Based on this, in the embodiment of the present invention, 100ml of oxygen is introduced into the sample injection chamber in step S3.
And step S4, bombarding the sample by using the argon cluster ion beam and the oxygen ion beam together to sputter secondary ions from the sample.
The reason for adopting argon cluster ions is that a plurality of argon atoms form a cluster, the cluster ions bombard a sample and then disperse to form a plurality of small atoms, the energy of each atom is small, the damage to the sample is low, and more secondary ions are obtained. In addition, the argon cluster ions and the oxygen ions are used for bombarding the sample together, so that compared with the method of bombarding the sample only by using the oxygen ions, the damage to the sample caused by high energy of the oxygen ions can be reduced without reducing the yield of secondary ions, and the method is favorable for improving the resolution ratio of trace impurity atoms.
It should be noted that the energy, beam intensity, scanning area, ion incident angle of the argon cluster ion beam, and the energy, beam intensity, scanning area, ion incident angle of the oxygen ion beam all affect the sputtering effect, and after considering the above factors, the embodiment of the present invention selects that in step S4, the energy of the argon cluster ion beam is 10kV, and the beam intensity is 2 × 10kV-6A/cm2The scanning area range is 100 multiplied by 100 mu m2～1000×1000μm2For example, 100X 100 μm2The ion incidence angle was 45. (ii) a In step S4, the energy of the oxygen ion beam is 200V to 500V, for example 200V, and the beam intensity is 3.2X 10-4A/cm2。
Step S5, adjusting the pulse width of the extracted voltage and the analysis frame number of each cycle period;
wherein the pulse width of the extraction voltage has an effect on the number of secondary ions read per pulse width and the number of analysis frames has an effect on the intensity of the secondary ions read, in particular the greater the pulse width, the greater the number of secondary ions read per pulse width, the greater the number of analysis frames, the greater the intensity of the secondary ions since the data points plotted per cycle are the sum of all analysis frames obtained during that cycle. Based on this, in the embodiment of the present invention, it is selected that, in step S5, the pulse width of the extraction voltage is 30ns, and the number of analysis frames per cycle period is 10, so that the total time of one cycle is changed from 3.6S to 18.4S.
In step S5, the extraction voltage pulse may be adjusted to delay the on time of the extraction voltage (5 to 10 microseconds later than the start time of sputtering), thereby avoiding interference of impurity ions at the initial stage of sputtering and improving the yield of secondary ions.
Step S6, collecting secondary ions through a mass analyzer in the secondary ion depth analyzer;
alternatively, in step S6, the collection area of the secondary ions has the following relationship with the sputtering pit formed when the specimen is bombarded with the argon cluster ions and the oxygen ions together: d is larger than or equal to L +4 phi between the side length D of the sputtering pit and the side length L of the collecting area, wherein phi is the diameter of the scanning area of the argon cluster ions, so that sputtered secondary ions can be effectively collected, the collected secondary ions only come from the very flat bottom surface of the sputtering pit, and the ions at different depths of the side wall of the sputtering pit and the ion contribution of the surface of a nearby instrument do not exist, so that a more accurate analysis result can be obtained when the impurity atoms in the sample are subjected to depth analysis.
Wherein the above-mentioned collection area satisfies the following formula: where R is the ion beam diameter of the argon cluster ions, X is the window ratio in the X direction, Y is the window ratio in the Y direction, and d is the diameter determined by the transfer lens and the field stop.
And step S7, analyzing the secondary ions through a mass analyzer to obtain a depth profile and a secondary ion mass distribution image.
Taking the material of the sample as SiC as an example, fig. 2 and a depth profile shown in fig. 3 are obtained by analyzing the stripped secondary ions through a mass analyzer, fig. 2 is a depth profile of the secondary ions of the trace impurity element P in SiC provided by the embodiment of the present invention, fig. 3 is a depth profile of the secondary ions of the trace impurity element Ca in SiC provided by the embodiment of the present invention, and the abscissa represents the mass-to-charge ratio (m/z) and the ordinate represents the Intensity (Intensity, unit counts) in fig. 2 and fig. 3.
And step S8, obtaining the detection result of the trace impurity elements in the sample according to the depth profile and the secondary ion mass distribution image.
Using a focused primary ion beam (Ar) with a certain energy in the previous step+、 F-、O2 +、O-、Cs+Etc.) bombarding on the sample, ionizing the sputtered atoms partially to generate secondary ions, and for monoatomic ions, the relationship between the secondary ion intensity and the concentration of the atoms in the sample can be expressed as: i isA'α＝IP·Y·αA·CA·β±·f±Wherein, IA'αSecondary ion intensity (counts/sec) of a certain isotope which is an element to be measured; i isPPrimary ion strength (number of ions/second); y is the sputtering yield (total number of atoms per primary ion); alpha is alphaAIs the abundance of the isotope to be detected; cAIs the concentration n of the element AA/nB(nAIs the number of A atoms in the matrix, nBNumber of atoms of the substrate); beta is a±Ionization rate of positive or negative ions which are sputtered atoms; f. of±Efficiency (counts/ions) determined for secondary ions. From the above, the concentration of a certain atom in the sample can be calculated from the secondary ion intensity.
In the process of denudating the surface of the sample layer by layer through ion bombardment, the change of the secondary ion intensity of a certain element along with the bombardment time is monitored, so that the condition that the concentration of impurity atoms in the sample changes along with the depth from the surface to the inside can be analyzed, namely, the impurity atoms in the sample are subjected to depth analysis. An accurate depth analysis requires uniform bombardment of the analysis region to form a flat pit, and the detected secondary ions should come from the very flat pit bottom surface only, without contribution from ions at different depths of the pit sidewall sample and ions on the surface of nearby instruments.
Specifically, step S8 includes:
obtaining the types of the trace impurity elements in the sample according to the depth profile, for example, determining the types of the impurity elements corresponding to each peak according to the charge-to-mass ratio of each peak in the depth profile; and obtaining the relation between the concentration and the depth of the trace impurity element in the sample according to the secondary ion mass distribution image, for example, calculating the depth and the concentration of the trace impurity element according to the curve relation between the secondary ion intensity and the sputtering time in the secondary ion mass distribution image by using the following formula: depth is time x sputtering rate; the concentration of trace impurity elements ═ (secondary ion signal intensity ÷ reference signal intensity) × (relative sensitivity factor); and simulating the relation between the concentration and the depth of the trace impurity elements in the sample according to the calculated depth and the concentration of the trace impurity elements in a three-dimensional simulation mode.
The Relative Sensitivity Factor (RSF) was calculated from the results of the standard sample test.
The corresponding calculation formula is as follows:
in the formula ImIs the secondary ion current intensity of mass m in the standard sample, thetamThe concentration of the substance; i isnIs the secondary ion current intensity of mass n, theta, in the standard samplenIs the concentration occupied by the substance. Since the time-of-flight secondary ion mass spectrometer can obtain ion signals of all substances in one detection, Im、InAll can be obtained by detection; in the standard sample,. theta.m、θnThe relative sensitivity factor can be calculated from this equation for known quantities. Thus, after RSF is obtained, by measuring I of unknown samplemKnown as I of substance nnAnd obtaining the concentration of the m substance in the sample to be measured according to the known concentration of the n substance.
In a secondary ion mass spectrometer, a relative sensitivity factor method is mainly adopted for impurity quantitative analysis at present. By the method, the influence of the existence of other components on the yield of secondary ions can be eliminated to a great extent, namely the influence of matrix effect on the detection result is eliminated, so that a more accurate test result is obtained.
In the actual detection process, a plurality of (e.g., 3) test regions may be selected on one sample for testing, or steps S1 to S8 may be repeated to test a plurality of (e.g., 3) samples to ensure reliable test results.
The testing temperature in the working process of the SIMS optimization detection method provided by the embodiment of the invention is 20 +/-5 ℃.
The embodiment of the invention provides an SIMS (separation of materials by mass) optimization detection method for trace impurity elements in semi-insulating GaAs and SiC, which comprises the following steps of: firstly, graphene is evaporated on the surface of a sample, the sample with the graphene evaporated on the surface is placed in a sample introduction chamber of a secondary ion depth analyzer, the sample introduction chamber is vacuumized, oxygen is introduced into the sample introduction chamber, argon cluster ions and oxygen ions are used for bombarding the sample together to sputter secondary ions from the sample, the pulse width of extraction voltage and the analysis frame number of each cycle period are adjusted, the secondary ions are collected by a mass analyzer in the secondary ion depth analyzer, the secondary ions are analyzed by the mass analyzer to obtain a depth analysis diagram and a secondary ion mass distribution image, finally, the detection result of trace impurity elements in the sample is obtained according to the depth diagram and the secondary ion mass distribution image, the SIMS optimization detection can be carried out on the types and the concentrations of the trace impurity elements in GaAs and SiC, and the types of the detectable trace impurity elements are many, the detection limit of the bulk concentration can reach ppb level, the detection precision of the impurity elements can reach below 10 percent, and the distribution resolution of the impurity elements is less than 10 nm.
The detection limit of the body concentration of the main impurity elements Cr and Fe in the semi-insulating GaAs can reach ppb level by adopting the SIMS optimization detection method provided by the embodiment of the invention, wherein the detection limit aiming at the impurity element Cr can reach 5 multiplied by 1013atoms/cm3The detection limit of bulk concentration can reach 10ppb, and the detection limit aiming at the impurity element Fe can reach 3 multiplied by 1015atoms/cm3The detection limit of the bulk concentration can reach 200 ppb. In addition, when the SIMS optimization detection method provided by the embodiment of the invention is adopted to detect the main impurity elements in the GaAs, the impurity quantitative test precision<5% vertical distribution resolution of impurities<1nm。
The detection limit of the SIMS optimization detection method provided by the embodiment of the invention on the body concentration of main impurity elements Al and V in SiC can reach 80ppb level,the detection limit can reach 7.6 multiplied by 1015atoms/cm3Quantitative measurement accuracy of impurities<10% vertical distribution resolution of impurities<5nm。
The SIMS optimization detection method provided by the embodiment of the invention is suitable for semi-insulating GaAs prepared by a vertical gradient freezing method (VGF) and the like and semi-insulating SiC prepared by a physical vapor transport method (PVT) and the like.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A SIMS optimization detection method for concentration distribution of trace impurities in semi-insulating GaAs and SiC is characterized by comprising the following steps:
step S1, evaporating graphene on the surface of the sample;
s2, placing the sample with the surface coated with the graphene in a sample introduction chamber of a secondary ion depth analyzer, and vacuumizing;
step S3, introducing oxygen into the sample introduction chamber;
step S4, bombarding the sample by using argon cluster ion beams and oxygen ion beams together so as to sputter secondary ions from the sample;
step S5, adjusting the pulse width of the extracted voltage and the analysis frame number of each cycle period;
step S6, collecting the secondary ions through a mass analyzer in the secondary ion depth analyzer;
step S7, analyzing the secondary ions through the mass analyzer to obtain a depth profile and a secondary ion mass distribution image;
step S8, obtaining the detection result of the trace impurity elements in the sample according to the depth profile and the secondary ion mass distribution image;
in the step S4, the energy of the argon cluster ion beam is 10kV, and the beam intensity is 2 × 10-6A/cm2The energy of the oxygen ion beam is 200V, and the beam intensity is 3.2 x 10-4A/cm2The scanning area of argon cluster ion beam is 100 multiplied by 100 mu m2The scanning area range of the oxygen ion beam is 100 multiplied by 100 mu m2The ion incidence angles are all 45 degrees.
2. The optimized detection method according to claim 1, wherein in step S1, the thickness of the graphene is 1-20 μm.
3. The optimized detection method according to claim 1, wherein in step S2, the vacuum degree of the sample chamber after vacuum pumping is 1.0 x 10-8Pa～5.0×10-8Pa。
4. The optimized detection method according to claim 1, wherein in step S3, 100ml of oxygen is introduced into the sample introduction chamber.
5. The optimized detection method according to claim 1, wherein in the step S5, the pulse width of the extracted voltage is 30ns, and the number of analysis frames per cycle period is 10.
6. The optimized detection method according to claim 1, wherein the step S8 includes:
obtaining the types of the trace impurity elements in the sample according to the depth profile;
and obtaining the relation between the concentration and the depth of the trace impurity elements in the sample according to the secondary ion mass distribution image.
7. The optimized detection method according to claim 6, wherein the obtaining the types of the trace impurity elements in the sample according to the depth profile comprises:
and determining the type of the impurity element corresponding to each peak according to the charge-to-mass ratio of each peak in the depth profile.
8. The optimized detection method according to claim 7, wherein the obtaining of the relationship between the concentration and the depth of the trace impurity element in the sample from the secondary ion mass distribution image comprises:
according to the curve relation between the secondary ion intensity and the sputtering time in the secondary ion mass distribution image, the depth and the concentration of the trace impurity elements are calculated by using the following formula:
depth is time x sputtering rate;
the concentration of trace impurity elements ═ (secondary ion signal intensity ÷ reference signal intensity) × (relative sensitivity factor);
and simulating the relation between the concentration and the depth of the trace impurity elements in the sample according to the calculated depth and the concentration of the trace impurity elements in a three-dimensional simulation mode.
9. The SIMS-optimized detection method according to claim 1,
in the step S6, the collection area of the secondary ions and the sputtering pit and the collection area formed when the specimen is bombarded with the argon gas cluster ions and the oxygen ions together have the following relationship: d is more than or equal to L +4 phi between the side length D of the sputtering pit and the side length L of the collection area, wherein phi is the diameter of the scanning area of the argon cluster ions;
the collection area satisfies the following formula: a ═ R · X + d ═ R · Y + d, where R is the ion beam diameter of the argon cluster ions, X is the window proportion in the X direction, Y is the window proportion in the Y direction, and d is the diameter determined by the transfer lens and the field stop.
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| CN105004711A (en) * | 2015-07-14 | 2015-10-28 | 中国科学院上海硅酸盐研究所 | Quantitative analysis method of impurities in beta-SiC |
| EP3288061A1 (en) * | 2016-08-24 | 2018-02-28 | Instytut Technologii Materialów Elektronicznych | Graphene-enhanced secondary ion mass spectroscopy analysis |
| CN106990161A (en) * | 2017-03-02 | 2017-07-28 | 中国工程物理研究院电子工程研究所 | A kind of system and method for improving sandwich construction SIMS depth resolution |
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