CN114295908A - Rapid detection method for internal microstructure of nano electronic device based on F-SRU network - Google Patents
Rapid detection method for internal microstructure of nano electronic device based on F-SRU network Download PDFInfo
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Abstract
The invention discloses a rapid detection method for an internal microstructure of a nanometer electronic device based on an F-SRU network, which comprises the following steps: step 1: aiming at various nano-electronic devices, under the given working condition and physical parameter condition of an electron beam, obtaining secondary electron current and electron beam induced current corresponding to different microstructures by adopting numerical calculation to form a data set of the secondary electron current and the electron beam induced current; step 2: constructing an F-SRU network model, and establishing a corresponding relation between the internal microstructure of the device and related current according to a data set of secondary electron current and electron beam induced current obtained by calculation; and step 3: a measuring platform is set up, and the secondary electron current and the electron beam induced current of a detection object are rapidly measured; and 4, step 4: and taking the measurement results of the secondary electron current and the electron beam induced current as the input of the F-SRU model, and reconstructing the internal microstructure from the output of the model.
Description
Technical Field
The invention belongs to the technical field of nano electronic device detection, and particularly relates to a method for rapidly detecting an internal microstructure of a nano electronic device based on an F-SRU network.
Background
The development of modern nanoscale electronics has placed extremely high demands on their manufacturing processes. In order to ensure the yield of the product, the semi-finished product and the finished product of the device need to be subjected to process detection. The information of micro-scale structures such as a groove structure and an interface structure in the device is a key factor influencing the performance of the device and is also the main content of the process detection of the device.
However, the conventional detection methods such as X-ray technology, ultrasonic technology, optical microscopy and the like can only realize the structural detection of devices with micron and larger sizes, and cannot detect nanoelectronic devices with the thickness of nanometer. In addition, the transmission electron microscope irradiates the device through the high-energy electron beam, and the penetrating electrons carry the structural information inside the device, so that the detection and observation of the internal microstructure can be realized. But the disadvantages are: 1) the transmitted electron beam can damage the device; 2) the charging phenomenon generated under the irradiation of the electron beam can reduce the reliability of detection; 3) the surface of the device needs to be coated with gold in the detection process, and the measurement process is complex; the requirement on the measurement environment is harsh, the process detection cannot be rapidly realized, and the engineering practicability is not realized. In short, no practical detection method and detection tool for rapidly realizing the internal microstructure of the nanoelectronic device exist at present.
In recent years, with the development of artificial intelligence technology, the application of deep learning technology in various industries is rapidly developed. Deep learning is based on a large amount of existing measurement data, implicit relations among the data are searched, and information which is difficult to measure in practice can be quickly and accurately obtained. However, due to the complexity of model construction, there is no research progress and report of the application of deep learning method in the internal microstructure detection of nanoelectronic devices.
Disclosure of Invention
Aiming at the technical problems, the invention provides a method for rapidly detecting the internal microstructure of the nano electronic device based on the F-SRU network, which only needs to detect the secondary electron current and the electron beam induced current of the device, establishes the relationship between the internal microstructure and the current and can rapidly detect the internal microstructure of the nano electronic device.
The technical scheme adopted by the invention is as follows:
a rapid detection method for an internal microstructure of a nanometer electronic device based on an F-SRU network comprises the following steps:
step 1: aiming at various nano-electronic devices, under the given working condition and physical parameter condition of an electron beam, obtaining secondary electron current and electron beam induced current corresponding to different microstructures by adopting numerical calculation to form a data set of the secondary electron current and the electron beam induced current;
step 2: constructing an F-SRU network model, and establishing a corresponding relation between the internal microstructure of the device and related current according to a data set of secondary electron current and electron beam induced current obtained by calculation;
and step 3: a measuring platform is set up, and the secondary electron current and the electron beam induced current of a detection object are rapidly measured;
and 4, step 4: and taking the measurement results of the secondary electron current and the electron beam induced current as the input of the F-SRU model, and reconstructing the internal microstructure from the output of the model.
Preferably, in step 1, the numerical calculation process includes: firstly, the scattering process of electrons in the device is calculated, then the interface process and the charge transport process are calculated, and finally the secondary electron current and the electron beam induced current are obtained.
Preferably, the scattering process of the computational electrons inside the device is as follows:
the Mott elastic scattering cross section expression is as follows:
in the formula, σ represents a scattering cross section; theta represents a scattering angle and represents a direction included angle between two collisions; f (theta) and g (theta) respectively represent an incident wave-splitting function and a scattering wave-splitting function; plAnd
representing Legendre polynomials and their accompanying polynomials;
is a partial wave phase shift;
inelastic scattering cross sections can be obtained by using the Penn dielectric function, and the inelastic mean free path is as follows:
in the formula, a0Which is the radius of the glass fiber,
in order to achieve the energy loss,
is the momentum transfer of electrons with kinetic energy E through a solid; lambda [ alpha ]in-eIs an inelastic mean free path; ε (q, ω) is the dielectric function of the solid which characterizes a particular electron excitation process, and Im (-1/ε (q, ω)) is the energy loss function which determines the probability of an inelastic scattering event occurring;
mean free path for inelastic scattering inλ-e is:
in the formula (I), the compound is shown in the specification,
EFis fermi energy, and after obtaining the lost energy of the primary electron, a secondary electron is generated in each inelastic scattering event, which obtains an energy Δ E and leaves the collision point at an angle;
the whole process of electron scattering can be obtained by the free path of elastic scattering and inelastic scattering and the random number by adopting a Monte Carlo model.
Preferably, the computational interface process is as follows:
the trench structure and the interfaces of different media inside the device can trap charges, and the differential form of the concentration of free electrons is as follows:
wherein T (t) represents a trapped electron density,. epsilon0And εrRespectively, the vacuum dielectric constant and the sample relative dielectric constant, NTRepresents the trapping density, SPFIs the capture coefficient;
because excessive positive ions exist at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, the negative charges of the insulating layer move to the interface, a model based on fixed surface charges is established, and first, a positive charge density P with a certain size is assumed to exist at the interfaceFB(ii) a In the initial calculation, the initial value of the positive charge density at the interface of different media in the device is changed into:
P(0)＝PFB (5)
it was accumulated into the positive charge density and the electric field distribution was calculated.
Preferably, the charge transport process is calculated as follows:
free charges in the device are transferred under an electrostatic field and diffused under the action of density gradient, and the traditional charge transport is described by adopting a current continuity equation:
where μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transport of the device under the irradiation of the electron beam under the condition of high field, the dynamic electron mobility is corrected as follows:
where E is the electric field strength of the current position,. epsilon0Is the optical phonon energy; accordingly, the dynamic diffusion coefficient is modified to:
thus, the dynamic transport process is modified to:
preferably, the secondary electron current and electron beam induced current are calculated as follows:
and (3) constructing a three-dimensional discrete coordinate system, and calculating the electric field components at the nodes (i, j, k) according to the space potential distribution as follows:
according to the surface electric field of the device, the number of secondary electrons collected by the Faraday cup collector can be calculated, and then secondary electron current is obtained;
for the gridded three-dimensional system (i, j, k), the values of the electron beam induced currents are calculated as follows:
in the formula, J (i, J, k) represents the current density at the node (i, J, k), and Δ x, Δ y, and Δ z are the mesh sizes in the respective directions.
Preferably, in step 2, the F-SRU network model is calculated as follows:
in the formula (I), the compound is shown in the specification, txto the input gate, ftIn order to forget the state of the door, trto reset the door state, ctIs in an internal state; w, W, fWAnd rWrespectively, are a matrix of parameters, respectively, fv、 rvand rbrespectively are parameter matrixes obtained through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of the optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) initial learning rate mu, WtGradient of tgFirst moment of gradient tmAnd second momentv t；
2) Iteration tg、 tmAnd tv：
mt＝β1mt-1+(1-β1)gt (17)
in the formula, beta1And beta2Respectively representing the attenuation coefficients of the first moment and the second moment;
3) calculating deviation:
m′t＝mt/(1-β1)-η1gt (19)
in the formula eta1And η2First and second order random tuning parameters, respectively, have the values:
in the formula, R1And R2Are respectively the interval [0, 1]Uniformly distributed random numbers in order to increase the iteration speed; 1/(1+ e)-t) The effect of (1) is to increase the ability of the algorithm to jump out of local optima later in the iteration.
Preferably, the process for constructing the relationship between the internal microstructure and the secondary electron current and the electron beam induced current by the F-SRU network is as follows:
1) determining input data of the F-SRU network: beam energy, beam current, secondary electron current, electron beam induced current;
2) determining output data of the F-SRU network: the size and characteristics of the internal microstructure;
3) initialization of the F-SRU network: determining the layer number of the F-SRU network, and initializing relevant parameters;
4) training of the F-SRU network: and training the F-SRU network by using the training data to finally obtain the corresponding relation between the internal microstructure and the secondary electron current and the electron beam induced current.
Preferably, in step 3, the measurement platform can be placed in a vacuum box or a normal-temperature non-vacuum environment, and includes a sample stage, a field emission electron gun, a faraday cup, and an ammeter, during actual measurement, the nano-electronic device is adhered to the sample stage through a conductive adhesive, the field emission electron gun is located right above the sample stage, the faraday cup is located above the sample stage for measuring secondary electron current and above the field emission electron gun, and the ammeter is located below the sample stage and connected to the sample stage for measuring electron beam induced current.
Preferably, the sample stage is a double-layer structure formed by a metal platform and a latticed metal bracket.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention establishes an accurate interface model and a dynamic charge transport model, can accurately obtain secondary electron current and electron beam induced current under different microstructures, and the result is verified by experiments. And finally, based on the existing numerical calculation result, the constructed F-SRU model improves the optimizing capability of the model by introducing a random adjustment strategy, can quickly and accurately construct the relationship between the internal microstructure of the nano-electronic device and secondary electron current and electron beam induced current, and obtains experimental verification of the result.
2. In order to detect the internal microstructure of the nano-electronic device, a transmission electron microscope is required in the traditional experimental measurement system, the detection process is very complex, and the surface treatment needs to be carried out on the device. And the requirements on the placement, the starting and the experimental conditions of the system are higher, so that the whole detection process consumes a large amount of time, and the measurement efficiency is low. The special detection system of the invention does not need to carry out surface treatment on the device, and the whole system does not need a complex imaging system and a vacuum environment. The whole process only needs to detect the secondary electron current and the electron beam induced current, the secondary electron current and the electron beam induced current can be simultaneously detected, and the method has the advantage of high detection speed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for rapidly detecting an internal microstructure of a nanoelectronic device based on an F-SRU network;
FIG. 2 is a graph comparing current calculation results with measurement results; (a) a secondary electron current; (b) electron beam induced current;
FIG. 3 shows a F-SRU network modeling flow;
FIG. 4 is a schematic cross-sectional view of a portion of a nanoelectronic device;
FIG. 5 is a schematic view of a measurement platform;
FIG. 6 is a graph showing the comparison between the cross-sectional structure of the sample and the results of the measurement; (a) a schematic cross-sectional view of a nanoelectronic device; (b) the method reconstructs the device structure.
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. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the 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.
The invention specifically provides a rapid detection method for an internal microstructure of a nano electronic device based on an F-SRU network, which comprises the following steps (as shown in figure 1):
step 1: based on the proposed novel charge transport model and interface model, in combination with the existing scattering theory, a novel numerical calculation system is constructed, and for various nano-electronic devices, under the given working condition of an electron beam and the physical parameter condition, the numerical calculation is adopted to obtain secondary electron currents and electron beam induced currents corresponding to different microstructures, so as to form a data set of the secondary electron currents and the electron beam induced currents;
step 2: constructing an F-SRU network model, and establishing a corresponding relation between the internal microstructure of the device and related current according to a data set of secondary electron current and electron beam induced current obtained by calculation;
and step 3: a measuring platform is set up, and the secondary electron current and the electron beam induced current of a detection object are rapidly measured;
and 4, step 4: and taking the measurement results of the secondary electron current and the electron beam induced current as the input of the F-SRU model, and reconstructing the internal microstructure from the output of the model.
1. Numerical calculation architecture construction and current calculation
In order to obtain the secondary electron current and the electron beam induced current of the nano-electronic device with the internal microstructure, a numerical calculation model of the electron beam irradiation nano-electronic device is established. The invention firstly provides an interface model considering a semiconductor-insulating layer, then provides a dynamic mobility charge transport model depending on an internal electric field, and combines an electron scattering theory to construct a numerical calculation system. The whole numerical calculation process is as follows: firstly, the scattering process of electrons in the device is calculated, then the interface process and the charge transport process are calculated, and finally the secondary electron current and the electron beam induced current are obtained.
1.1 Electron Scattering Process
Mott elastic scattering cross section solution
The equation is given as the differential Mott elastic scattering cross section:
in the formula, σ represents a scattering cross section; theta represents a scattering angle and represents a direction included angle between two collisions; f (theta) and g (theta) respectively represent an incident wave-splitting function and a scattering wave-splitting function; plAnd
representing Legendre polynomials and their accompanying polynomials;
is a partial wave phase shift.
Inelastic scattering cross sections can be obtained by using the Penn dielectric function, and the inelastic mean free path is as follows:
in the formula, a0Which is the radius of the glass fiber,
in order to achieve the energy loss,
is the momentum transfer of electrons with kinetic energy E through a solid; lambda [ alpha ]in-eIs an inelastic mean free path; ε (q, ω) is the dielectric function of the solid which characterizes a particular electron excitation process, and Im (-1/ε (q, ω)) is the energy loss function which determines the probability of an inelastic scattering event occurring;
mean free path for inelastic scatteringλin-e is:
in the formula (I), the compound is shown in the specification,
EFis fermi energy, and after obtaining the lost energy of the primary electron, a secondary electron is generated in each inelastic scattering event, which obtains an energy Δ E and leaves the collision point at an angle;
the whole process of electron scattering can be obtained by the free path of elastic scattering and inelastic scattering and the random number by adopting a Monte Carlo model.
1.2 interfacial Process
The trench structure and the interfaces of different media inside the device can trap charges, and the differential form of the concentration of free electrons is as follows:
wherein T (t) represents a trapped electron density,. epsilon0And εrRespectively, the vacuum dielectric constant and the sample relative dielectric constant, NTRepresents the trapping density, SPFIs the capture coefficient;
because excessive positive ions exist at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, the negative charges of the insulating layer move to the interface, a model based on fixed surface charges is established, and first, a positive charge density P with a certain size is assumed to exist at the interfaceFB. In the initial calculation, the initial value of the positive charge density at the interface of different media in the device is changed into:
P(0)＝PFB (5)
it was accumulated into the positive charge density and the electric field distribution was calculated.
1.3 Charge transport Process
Free charges in the device are transferred under an electrostatic field and diffused under the action of density gradient, and the traditional charge transport is described by adopting a current continuity equation:
where μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transport of the device under the irradiation of the electron beam under the condition of high field, the dynamic electron mobility is corrected as follows:
where E is the electric field strength of the current position,. epsilon0Is the optical phonon energy; accordingly, the dynamic diffusion coefficient is modified to:
thus, the dynamic transport process is modified to:
1.4 Secondary Electron Current and Electron Beam induced Current calculation
And (3) constructing a three-dimensional discrete coordinate system, and calculating the electric field components at the nodes (i, j, k) according to the space potential distribution as follows:
according to the surface electric field of the device, the number of secondary electrons collected by the Faraday cup collector can be calculated, and then secondary electron current is obtained;
for the gridded three-dimensional system (i, j, k), the values of the electron beam induced currents are calculated as follows:
in the formula, J (i, J, k) represents the current density at the node (i, J, k), and Δ x, Δ y, and Δ z are the mesh sizes in the respective directions.
And calculating secondary electron currents and electron beam induced currents corresponding to different internal microstructures under the given electron beam working condition and physical parameter conditions aiming at various nano-electronic devices. FIG. 2 is a comparison of the secondary electron current and electron beam induced current calculated by the present invention with their measurements, which verifies the reliability of the model constructed by the present invention.
F-SRU model construction
The invention provides a novel simple circulating unit network, namely an F-SRU network, which is used for establishing a corresponding relation between an internal microstructure of a nano electronic device and secondary electron current and electron beam induced current. For a given nanoelectronic device, the secondary electron current and the electron beam induced current depend on the size of the internal microstructure of the device, the thickness of the insulating and metal layers, the size of the trench locations, and the like. However, only a limited number of data can be obtained by numerical calculation, so that the invention constructs the F-SRU network and establishes the relationship between the internal microstructure characteristics and the secondary electron current and the electron beam induced current.
The F-SRU network is a light cycle unit which balances the capacity and the scalability of a model, has high parallelization and sequence modeling capacity, and has the following calculation process:
in the formula (I), the compound is shown in the specification, txto the input gate, ftIn order to forget the state of the door, trto reset the door state, ctIs in an internal state; w, W, fWAnd rWrespectively, are a matrix of parameters, respectively, fv、 rvand brRespectively are parameter matrixes obtained through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of the optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) initial learning rate mu, WtGradient of tgFirst moment of gradient tmAnd second moment tv；
2) Iteration tg、 tmAnd tv：
mt＝β1mt-1+(1-β1)gt (17)
in the formula, beta1And beta2Respectively representing the attenuation coefficients of the first moment and the second moment;
3) calculating deviation:
m′t＝mt/(1-β1)-η1gt (19)
in the formula eta1And η2First and second order random tuning parameters, respectively, have the values:
in the formula, R1And R2Are respectively the interval [0, 1]Uniformly distributed random numbers in order to increase the iteration speed; 1/(1+ e)-t) The effect of (1) is to increase the ability of the algorithm to jump out of local optima later in the iteration.
The process for constructing the relationship between the internal microstructure and the secondary electron current and electron beam induced current by the F-SRU network is as follows (see FIG. 3):
1) determining input data of the F-SRU network: beam energy, beam current, secondary electron current, electron beam induced current;
2) determining output data of the F-SRU network: the size and characteristics of the internal microstructure;
3) initialization of the F-SRU network: determining the layer number of the F-SRU network, and initializing relevant parameters;
4) training of the F-SRU network: and training the F-SRU network by using the training data to finally obtain the corresponding relation between the internal microstructure and the secondary electron current and the electron beam induced current.
FIG. 4 is a cross-sectional view of a conventional deep trench structure, multi-layer nanoelectronic device. For each nano-electronic device with a specific structure, the thicknesses of an organic layer, an insulating layer and a metal layer, the position of a groove and the like are respectively changed, and the secondary electron current and the electron beam induced current under the irradiation of an electron beam are calculated. Finally, data sets of different microstructures corresponding to the corresponding currents one by one can be obtained.
3. Measuring electron beam induced current and secondary electron current using a measurement platform
The measuring platform is shown in figure 5. The measuring platform comprises: special sample stage, field emission electron gun, Faraday cup, and galvanometer. The whole measuring device can be placed in a vacuum box or in a normal-temperature non-vacuum environment. In actual measurement, the nanoelectronic device is adhered to a sample stage through conductive adhesive; the field emission electron gun is positioned right above the sample table; a Faraday cup is arranged above the sample table and used for measuring secondary electron current; and an ammeter is connected below the sample table and used for measuring the induced current of the electron beam.
The sample stage consists of a layer of latticed metal support and a layer of metal platform. In fact, because the charges inside the detection object are accumulated to form the charge transportation and form the displacement current, the displacement current is included in the electron beam induced current directly collected by the metal bracket of the traditional sample stage, and the detection result is unreliable. The invention adopts a double-layer structure, wherein the upper layer metal support is in a grid shape, displacement current can not be generated, and the induced displacement current is grounded through the lower metal platform, so that the measurement value of the electron beam induced current is more accurate.
The specific measurement process is as follows:
1) the beam energy size is determined. For the typical material thickness and material properties of nanoelectronic devices, the beam energy is chosen such that the maximum depth of incidence of the electron beam is approximately equal to the thickness of the device. The beam energy and the maximum incidence depth satisfy the following conditions:
for nanoelectronic devices the thickness is typically less than a few hundred nanometers and thus the beam energy is less than 5 keV.
2) And determining the beam current. The beam current of a field emission electron gun is generally much smaller than milliampere. In order to improve the detection reliability and reduce the adverse effect of electron beams on device charging, the beam size is set to be nano-ampere.
3) The field emission electron gun vertically irradiates the nano-electron device, and the secondary electron current and the electron beam induced current of each point are measured in real time in a surface scanning mode, so that the measurement result of the related current of each point of the whole device is finally obtained.
4. Reconstruction of internal microstructures
And inputting the secondary electron current of the detection object and the measurement result induced by the electron beam into the trained F-SRU network, and reconstructing the internal microstructure of the nano-electronic device through the output of the network. FIG. 6 is a comparison of the cross-sectional structure of the nanoelectronic device with the cross-sectional structure calculated by the present invention, which verifies the feasibility and reliability of the detection results of the present invention.
The data acquisition point in the measurement process is preferably about ten seconds after the start of irradiation. At the moment, the whole process of irradiating the measuring object by the electron beam reaches a stable state, the secondary electron current and the electron beam induced current tend to be stable, and the measuring result is more reliable.
The invention has the advantages that:
1) the traditional detection method for the internal microstructure of the nano-electronic device is based on a transmission electron microscope and has the defects of long detection time, complex detection process, high detection condition and the like. The invention provides a rapid F-SRU deep learning model, which can be used for rapidly detecting the internal microstructure of a nano-electronic device by only detecting the secondary electron current of the device and the electron beam induced current and establishing the relationship between the internal microstructure and the current.
2) The invention provides an interface capture model aiming at the characteristics of an internal interface by combining an electron scattering model and provides a dynamic charge transport model. The model can accurately calculate scattering, capturing and transporting effects in the device, and ensures the reliability of the results of the electron beam induced current and the secondary electron current.
3) The conventional electron beam induced current measurement results are unstable due to interference of other currents. The invention builds a special measuring system. The sample stage of the measuring system adopts a double-layer structure, the metal platform below receives displacement current, and the latticed metal support above only receives electron beam induced current, so that the double-layer structure can effectively eliminate the adverse effect of the displacement current and improve the accuracy of the measurement result of the electron beam induced current.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, alterations and equivalent changes made to the above embodiment according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (10)
1. A rapid detection method for an internal microstructure of a nanometer electronic device based on an F-SRU network is characterized by comprising the following steps:
step 1: aiming at various nano-electronic devices, under the given working condition and physical parameter condition of an electron beam, obtaining secondary electron current and electron beam induced current corresponding to different microstructures by adopting numerical calculation to form a data set of the secondary electron current and the electron beam induced current;
step 2: constructing an F-SRU network model, and establishing a corresponding relation between the internal microstructure of the device and related current according to a data set of secondary electron current and electron beam induced current obtained by calculation;
and step 3: a measuring platform is set up, and the secondary electron current and the electron beam induced current of a detection object are rapidly measured;
and 4, step 4: and taking the measurement results of the secondary electron current and the electron beam induced current as the input of the F-SRU model, and reconstructing the internal microstructure from the output of the model.
2. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 1, wherein in the step 1, the numerical calculation process comprises: firstly, the scattering process of electrons in the device is calculated, then the interface process and the charge transport process are calculated, and finally the secondary electron current and the electron beam induced current are obtained.
3. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 2, wherein the scattering process of electrons in the device is calculated as follows:
the Mott elastic scattering cross section expression is as follows:
in the formula, σ represents a scattering cross section; theta represents a scattering angle and represents a direction included angle between two collisions; f (theta) and g (theta) respectively represent an incident wave-splitting function and a scattering wave-splitting function; plAnd Pl 1Representing Legendre polynomials and their accompanying polynomials;
is a partial wave phase shift;
inelastic scattering cross sections can be obtained by using the Penn dielectric function, and the inelastic mean free path is as follows:
in the formula, a0Which is the radius of the glass fiber,
in order to achieve the energy loss,
is the momentum transfer of electrons with kinetic energy E through a solid; lambda [ alpha ]in-eIs an inelastic mean free path; ε (q, ω) is the dielectric function of the solid which characterizes a particular electron excitation process, and Im (-1/ε (q, ω)) is the energy loss function which determines the probability of an inelastic scattering event occurring;
mean free path for inelastic scattering inλ -eComprises the following steps:
in the formula (I), the compound is shown in the specification,
EFis fermi energy, and after obtaining the lost energy of the primary electron, a secondary electron is generated in each inelastic scattering event, which obtains an energy Δ E and leaves the collision point at an angle;
the whole process of electron scattering can be obtained by the free path of elastic scattering and inelastic scattering and the random number by adopting a Monte Carlo model.
4. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 2, wherein the interface calculation process is as follows:
the trench structure and the interfaces of different media inside the device can trap charges, and the differential form of the concentration of free electrons is as follows:
wherein T (t) represents a trapped electron density,. epsilon0And εrRespectively is the dielectric constant in vacuumAnd the relative permittivity, N, of the sampleTRepresents the trapping density, SPFIs the capture coefficient;
because excessive positive ions exist at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, the negative charges of the insulating layer move to the interface, a model based on fixed surface charges is established, and first, a positive charge density P with a certain size is assumed to exist at the interfaceFB(ii) a In the initial calculation, the initial value of the positive charge density at the interface of different media in the device is changed into:
P(0)＝PFB (5)
it was accumulated into the positive charge density and the electric field distribution was calculated.
5. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 2, wherein the charge transport process is calculated as follows:
free charges in the device are transferred under an electrostatic field and diffused under the action of density gradient, and the traditional charge transport is described by adopting a current continuity equation:
where μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transport of the device under the irradiation of the electron beam under the condition of high field, the dynamic electron mobility is corrected as follows:
where E is the electric field strength of the current position,. epsilon0Is the optical phonon energy; accordingly, the dynamic diffusion coefficient is modified to:
thus, the dynamic transport process is modified to:
6. the method for rapidly detecting the internal microstructure of the nano-electronic device based on the F-SRU network as claimed in claim 2, wherein the calculation processes of the secondary electron current and the electron beam induced current are as follows:
and (3) constructing a three-dimensional discrete coordinate system, and calculating the electric field components at the nodes (i, j, k) according to the space potential distribution as follows:
according to the surface electric field of the device, the number of secondary electrons collected by the Faraday cup collector can be calculated, and then secondary electron current is obtained;
for the gridded three-dimensional system (i, j, k), the values of the electron beam induced currents are calculated as follows:
in the formula, J (i, J, k) represents the current density at the node (i, J, k), and Δ x, Δ y, and Δ z are the mesh sizes in the respective directions.
7. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 1, wherein in the step 2, the calculation process of the F-SRU network model is as follows:
in the formula (I), the compound is shown in the specification, txto the input gate, ftIn order to forget the state of the door, trto reset the door state, ctIs in an internal state; w, W, fWAnd rWrespectively, are a matrix of parameters, respectively, fv、 rvand rbrespectively are parameter matrixes obtained through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of the optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) initial learning rate mu, WtGradient of tgFirst moment of gradient tmAnd second moment tv；
2) Iteration tg、 tmAnd tv：
mt＝β1mt-1+(1-β1)gt (17)
in the formula, beta1And beta2Damping system respectively representing first and second order momentsCounting;
3) calculating deviation:
m′t＝mt/(1-β1)-η1gt (19)
in the formula eta1And η2First and second order random tuning parameters, respectively, have the values:
in the formula, R1And R2Are respectively the interval [0, 1]Uniformly distributed random numbers in order to increase the iteration speed; 1/(1+ e)-t) The effect of (1) is to increase the ability of the algorithm to jump out of local optima later in the iteration.
8. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 7, wherein the process of constructing the relationship between the internal microstructure and the secondary electron current and the electron beam induced current by the F-SRU network is as follows:
1) determining input data of the F-SRU network: beam energy, beam current, secondary electron current, electron beam induced current;
2) determining output data of the F-SRU network: the size and characteristics of the internal microstructure;
3) initialization of the F-SRU network: determining the layer number of the F-SRU network, and initializing relevant parameters;
4) training of the F-SRU network: and training the F-SRU network by using the training data to finally obtain the corresponding relation between the internal microstructure and the secondary electron current and the electron beam induced current.
9. The method for rapidly detecting the internal microstructure of the nanoelectronic device based on the F-SRU network as claimed in claim 1, wherein in step 3, the measuring platform can be placed in a vacuum box or a normal temperature non-vacuum environment, the measuring platform comprises a sample stage, a field emission electron gun, a faraday cup, and a galvanometer, during actual measurement, the nanoelectronic device is adhered to the sample stage by a conductive adhesive, the field emission electron gun is located right above the sample stage, the faraday cup is located above the sample stage for measuring the secondary electron current and located above the field emission electron gun, and the galvanometer is located below the sample stage and connected to the sample stage for measuring the electron beam induced current.
10. The method for rapidly detecting the internal microstructure of the F-SRU network-based nanoelectronic device according to claim 9, wherein the sample stage is a double-layer structure consisting of a metal platform and a latticed metal support.
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