CN114295908B - 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 method for rapidly detecting an internal microstructure of a nano electronic device based on an F-SRU network, which comprises the following steps: step 1: aiming at various nano-electronic devices, under given electron beam working conditions and physical parameter conditions, adopting numerical calculation to obtain secondary electron currents and electron beam induced currents corresponding to different microstructures, and forming 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 microstructure in the device and related current by using the calculated data set of the secondary electron current and the electron beam induced current; step 3: constructing a measuring platform, and rapidly measuring secondary electron current and electron beam induced current of a detection object; 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 microscopic scale structure information such as the groove structure, the interface structure and the like in the device is a key factor affecting the performance of the device, and is also the main content of device process detection.
However, conventional inspection methods such as X-ray technology, ultrasonic technology, optical microscopy, etc. can only achieve structural inspection of micro-scale and larger-sized devices, and cannot be applied to inspection of nano-electronic devices having a thickness of nano-scale. In addition, the transmission electron microscope irradiates the device through high-energy electron beams, and the penetrated electrons carry the structural information in the device, so that the detection and observation of the internal microstructure can be realized. But has the disadvantages that: 1) The transmitted electron beam may damage the device; 2) The charging phenomenon generated under the irradiation of the electron beam may reduce the reliability of detection; 3) In the detection process, the surface of the device is required to be coated with gold, and the measurement process is complex; the method has strict requirements on the measuring environment, can not realize process detection rapidly, and has no engineering practicability. In summary, there is no effective practical detection method and detection tool capable of rapidly implementing the internal microstructure of the nano electronic device.
With the development of artificial intelligence technology in recent years, the application of deep learning technology in various industries has been rapidly developed. The deep learning is based on a large amount of existing measurement data, and an implicit relation between the data is searched, so that information which is difficult to measure in practice can be quickly and accurately obtained. However, due to the complexity of model construction, there is currently no research progress and report on the application of deep learning methods in the detection of micro-structures inside nanoelectronics.
Disclosure of Invention
Aiming at the technical problems, the invention provides a method for rapidly detecting the internal microstructure of a nano electronic device based on an F-SRU network, which only needs to detect secondary electron current and electron beam induced current of the device, establishes the relation between the internal microstructure and current and can rapidly detect the internal microstructure of the nano electronic device.
The invention adopts the technical scheme that:
a method for rapidly detecting internal microstructures of nano electronic devices based on an F-SRU network comprises the following steps:
step 1: aiming at various nano-electronic devices, under given electron beam working conditions and physical parameter conditions, adopting numerical calculation to obtain secondary electron currents and electron beam induced currents corresponding to different microstructures, and forming 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 microstructure in the device and related current by using the calculated data set of the secondary electron current and the electron beam induced current;
step 3: constructing a measuring platform, and rapidly measuring secondary electron current and electron beam induced current of a detection object;
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, calculating a scattering process of electrons in the device, then calculating an interface process and a charge transport process, and finally obtaining secondary electron current and electron beam induced current.
Preferably, the scattering process of electrons inside the device is calculated as follows:
the Mott elastic scattering cross section expression is as follows:
wherein σ represents a scattering cross section; θ represents the scattering angle, representing the angle between the two collisions in the direction; f (θ) and g (θ) represent an incident and a scattering branching function, respectively; p (P) l And P l 1 Representing a Legendre polynomial and its associated polynomial;is a fractional phase shift;
the inelastic scattering cross section can be obtained by using Penn dielectric function, and the inelastic mean free path is as follows:
wherein a is 0 Is of the bohr radius,for energy loss, +.>Is the momentum transfer of electrons with kinetic energy E through the solid; lambda (lambda) in-e A non-elastic mean free path; epsilon (q, omega) is the dielectric function of the solid that characterizes a particular electron excitation process, im (-1/epsilon (q, omega)) is the energy loss that determines the probability of occurrence of inelastic scattering eventsA loss function;
mean free path of inelastic scatteringThe method comprises the following steps:
in the method, in the process of the invention,E F is fermi energy, after obtaining the lost energy of the primary electrons, generating a secondary electron in each inelastic scattering event, the secondary electron obtaining energy Δe and exiting the collision point at an angle;
the whole process of electron scattering can be obtained by adopting a Monte Carlo model through the free range of elastic scattering and inelastic scattering and random numbers.
Preferably, the computational interface procedure is as follows:
the trench structure inside the device and the interfaces between different dielectrics will trap charge, the differential form of free electron concentration is as follows:
wherein T (T) represents the density of the trapped electrons, ε 0 And epsilon r Vacuum dielectric constant and sample relative dielectric constant, N T Representing the capture density, S PF Is the capture coefficient;
because of the existence of excessive positive ions at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, and the negative charges of the insulating layer are caused to move to the interface, a model based on fixed surface charges is built, and firstly, the positive charge density P with a certain size is assumed to exist at the interface FB The method comprises the steps of carrying out a first treatment on the surface of the At the initial calculation, the initial value of the positive charge density at the interface of different media inside the device becomes:
P(0)＝P FB (5)
it is accumulated into the positive charge density and the electric field distribution is calculated.
Preferably, the charge transport process is calculated as follows:
free charges in the device are transferred under the action of an electrostatic field and a density gradient, and traditional charge transport is described by adopting a current continuity equation:
wherein μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transportation under the high field condition in the device under the irradiation of the electron beam, the dynamic electron mobility correction is as follows:
wherein E is the electric field strength of the current position, ε 0 Is optical phonon energy; accordingly, the dynamic diffusion coefficient is corrected as:
thus, the dynamic transport process is modified as:
preferably, the secondary electron current and the electron beam induced current are calculated as follows:
constructing a three-dimensional discrete coordinate system, and calculating electric field components at the nodes (i, j, k) from the spatial 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 three-dimensional system (i, j, k) after gridding processing, the values of the electron beam induced currents are calculated as follows:
where J (i, J, k) represents the current density at node (i, J, k), Δx, Δy, and Δz are the grid dimensions for each direction.
Preferably, in step 2, the calculation process of the F-SRU network model is as follows:
wherein x is t Is an input door, f t In the state of forgetting to leave the door, r t To reset the door state c t Is an internal state; w, W f And W is equal to r Respectively, parameter matrix, v f 、v r And b r Respectively obtaining parameter matrixes through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of an optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) Initializing learning rate mu, W t Gradient of the flowg t First moment m of gradient t And second moment v t ；
2) Iteration g t 、m t V t ：
m t ＝β 1 m t-1 +(1-β 1 )g t (17)
Wherein beta is 1 And beta 2 Attenuation coefficients respectively representing a first moment and a second moment;
3) Calculating deviation:
m′ t ＝m t /(1-β 1 )-η 1 g t (19)
wherein eta is 1 And eta 2 The first-order and second-order random adjustment parameters are respectively as follows:
wherein R is 1 And R is 2 Respectively the intervals [0,1 ]]Random numbers distributed uniformly, in order to increase the iteration speed; 1/(1+e) -t ) The function of (2) is to increase the ability of the algorithm to jump out of local optimum at the later stage of the iteration.
Preferably, the F-SRU network constructs the relationship between the internal microstructure and the secondary electron current and beam induced current 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 related parameters;
4) Training of F-SRU network: training the F-SRU network by 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 measuring platform can be placed in a vacuum box or a normal-temperature non-vacuum environment, and comprises a sample stage, a field emission electron gun, a Faraday cup and a ammeter, wherein during actual measurement, the nano electronic device is glued on the sample stage through conductive glue, the field emission electron gun is positioned right above the sample stage, the Faraday cup is arranged above the sample stage for measuring secondary electron current and is positioned above the field emission electron gun, and the ammeter is arranged below the sample stage and is connected with 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. Finally, the built F-SRU model is based on the existing numerical calculation result, the optimizing capability of the model is improved by introducing a random adjustment strategy, the relationship between the internal microstructure of the nano electronic device and the secondary electron current and the electron beam induced current can be quickly and accurately built, and experimental verification is obtained on the result.
2. In order to detect the internal microstructure of the nano-electronic device, the traditional experimental measurement system needs to adopt a transmission electron microscope, the detection process is extremely complex, and the device needs to be subjected to surface treatment. And the requirements on the placement, the starting and the experimental conditions of the system are high, so that the whole detection process consumes a great deal of time, and the measurement efficiency is low. The special detection system 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 secondary electron current and electron beam induced current, and the two can be completed simultaneously, so that the method has the advantage of high detection speed.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for rapidly detecting internal microstructures of nanoelectronic devices based on an F-SRU network;
FIG. 2 is a graph showing the comparison of the current calculation result and the measurement result; (a) secondary electron current; (b) electron beam induced current;
FIG. 3 shows a flow of F-SRU network modeling;
FIG. 4 is a schematic cross-sectional view of a portion of a nanoelectronic device;
FIG. 5 is a schematic diagram of a measurement platform;
FIG. 6 is a diagram showing the cross-sectional structure of a sample compared with the detection result; (a) a schematic cross-sectional view of a nanoelectronic device; (b) the device structure reconstructed by the method.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the 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 invention, as 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 made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention particularly provides a method for rapidly detecting the internal microstructure of a nano electronic device based on an F-SRU network, which comprises the following steps (shown in figure 1):
step 1: based on the proposed novel charge transport model and interface model, a novel numerical calculation system is constructed by combining the existing scattering theory, and aiming at various nano-electronic devices, under given electron beam working conditions and physical parameter conditions, secondary electron currents and electron beam induced currents corresponding to different microstructures are obtained by adopting numerical calculation, so that a data set of the secondary electron currents and the electron beam induced currents is formed;
step 2: constructing an F-SRU network model, and establishing a corresponding relation between the microstructure in the device and related current by using the calculated data set of the secondary electron current and the electron beam induced current;
step 3: constructing a measuring platform, and rapidly measuring secondary electron current and electron beam induced current of a detection object;
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 system construction and current calculation
To obtain secondary electron current and electron beam induced current of a nanoelectronic device having an internal microstructure, a numerical calculation model of electron beam irradiation of the nanoelectronic device is established. According to the invention, firstly, an interface model of a semiconductor-insulating layer is considered, then a dynamic mobility charge transport model depending on an internal electric field is provided, and a numerical calculation system is constructed by combining an electron scattering theory. The whole numerical calculation process is as follows: firstly, calculating a scattering process of electrons in the device, then calculating an interface process and a charge transport process, and finally obtaining secondary electron current and electron beam induced current.
1.1 Electron scattering Process
Mott elastic scattering cross section is obtained by solvingThe equation is derived, the following is the differential Mott elastic scattering cross section:
wherein σ represents a scattering cross section; θ represents the scattering angle, representing the angle between the two collisions in the direction; f (θ) and g (θ) represent an incident and a scattering branching function, respectively; p (P) l And P l 1 Representing a Legendre polynomial and its associated polynomial;is a fractional phase shift.
The inelastic scattering cross section can be obtained by using Penn dielectric function, and the inelastic mean free path is as follows:
wherein a is 0 Is of the bohr radius,for energy loss, +.>Is the momentum transfer of electrons with kinetic energy E through the solid; lambda (lambda) in-e A non-elastic mean free path; the dielectric function of epsilon (q, omega) being a solid characterizes a particular electron excitation processIm (-1/epsilon (q, omega)) is an energy loss function that determines the probability of occurrence of inelastic scattering events;
mean free path of inelastic scatteringThe method comprises the following steps:
in the method, in the process of the invention,E F is fermi energy, after obtaining the lost energy of the primary electrons, generating a secondary electron in each inelastic scattering event, the secondary electron obtaining energy Δe and exiting the collision point at an angle;
the whole process of electron scattering can be obtained by adopting a Monte Carlo model through the free range of elastic scattering and inelastic scattering and random numbers.
1.2 interface procedure
The trench structure inside the device and the interfaces between different dielectrics will trap charge, the differential form of free electron concentration is as follows:
wherein T (T) represents the density of the trapped electrons, ε 0 And epsilon r Vacuum dielectric constant and sample relative dielectric constant, N T Representing the capture density, S PF Is the capture coefficient;
because of the existence of excessive positive ions at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, and the negative charges of the insulating layer are caused to move to the interface, a model based on fixed surface charges is built, and firstly, the positive charge density P with a certain size is assumed to exist at the interface FB . At the initial calculation, the initial value of the positive charge density at the interface of different media inside the device becomes:
P(0)＝P FB (5)
it is accumulated into the positive charge density and the electric field distribution is calculated.
1.3 Charge transport Process
Free charges in the device are transferred under the action of an electrostatic field and a density gradient, and traditional charge transport is described by adopting a current continuity equation:
wherein μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transportation under the high field condition in the device under the irradiation of the electron beam, the dynamic electron mobility correction is as follows:
wherein E is the electric field strength of the current position, ε 0 Is optical phonon energy; accordingly, the dynamic diffusion coefficient is corrected as:
thus, the dynamic transport process is modified as:
1.4 secondary electron current and electron beam induced current calculation
Constructing a three-dimensional discrete coordinate system, and calculating electric field components at the nodes (i, j, k) from the spatial 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 three-dimensional system (i, j, k) after gridding processing, the values of the electron beam induced currents are calculated as follows:
where J (i, J, k) represents the current density at node (i, J, k), Δx, Δy, and Δz are the grid dimensions for each direction.
Aiming at various nano-electronic devices, under given electron beam working conditions and physical parameter conditions, secondary electron currents and electron beam induced currents corresponding to different internal microstructures are calculated. Fig. 2 is a comparison of the calculated secondary electron current and electron beam induced current with the measurement results thereof, and verifies the reliability of the construction model of the present invention.
2.F-SRU model construction
The invention provides a novel simple circulation unit network, namely an F-SRU network, which establishes a corresponding relation between a microstructure in a nano electronic device and secondary electron current and electron beam induced current. For a given nanoelectronic device, the secondary electron current and electron beam induced current depend on the size of the microstructure within the device, the thickness of the insulating and metal layers, the trench location dimensions, and the like. However, only a limited amount of data can be obtained by numerical calculation, so that the invention constructs an 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 circulation unit for balancing the capacity and the scalability of a model, has high parallelization and sequence modeling capability, and comprises the following calculation processes:
wherein x is t Is an input door, f t In the state of forgetting to leave the door, r t To reset the door state c t Is an internal state; w, W f And W is equal to r Respectively, parameter matrix, v f 、v r And b r Respectively obtaining parameter matrixes through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of an optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) Initializing learning rate mu, W t Gradient g t First moment m of gradient t And second moment v t ；
2) Iteration g t 、m t V t ：
m t ＝β 1 m t-1 +(1-β 1 )g t (17)
Wherein beta is 1 And beta 2 Attenuation coefficients respectively representing a first moment and a second moment;
3) Calculating deviation:
m t ＝m t /(1-β 1 )-η 1 g t (19)
wherein eta is 1 And eta 2 The first-order and second-order random adjustment parameters are respectively as follows:
wherein R is 1 And R is 2 Respectively the intervals [0,1 ]]Random numbers distributed uniformly, in order to increase the iteration speed; 1/(1+e) -t ) The function of (2) is to increase the ability of the algorithm to jump out of local optimum at the later stage of the iteration.
The flow of the F-SRU network construction of the relationship between the internal microstructure and secondary electron current and beam induced current 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 related parameters;
4) Training of F-SRU network: training the F-SRU network by 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 schematic cross-sectional view of a nano-electronic device of a common deep trench structure, multilayer structure. For each nano-electronic device with a specific structure, the thicknesses of the organic layer, the insulating layer and the metal layer, the positions of the grooves and the like are respectively changed, and the secondary electron current and the electron beam induced current of the nano-electronic device under the irradiation of the electron beam are calculated. Finally, data sets with 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 fig. 5. The measurement platform includes: a proprietary sample stage, a field emission electron gun, a faraday cup, and a galvanometer. The whole measuring device can be placed in a vacuum box or in a normal-temperature non-vacuum environment. In actual measurement, the nano electronic device is adhered on a sample table through conductive adhesive; the field emission electron gun is positioned right above the sample stage; a Faraday cup is arranged above the sample stage and is used for measuring secondary electron current; and a galvanometer is connected below the sample stage and is used for measuring electron beam induced current.
The sample stage consists of a layer of latticed metal support and a layer of metal platform. In fact, since charges in the detection object accumulate to form charge transport and displacement current is formed, the displacement current is contained in electron beam induced current directly collected by the conventional sample stage through the metal bracket, so that the detection result is unreliable. The invention adopts a double-layer structure, wherein the upper metal bracket is in a grid shape, no displacement current is generated, and the induced displacement current is grounded through the metal platform below, so that the measurement value of the electron beam induced current is more accurate.
The specific measurement flow is as follows:
1) The beam energy size is determined. For the material thickness and material properties common to nanoelectronic devices, the beam energy is selected such that the maximum incident depth 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:
the thickness is typically less than a few hundred nanometers for nanoelectronics, and thus the beam energy is less than 5keV.
2) And determining the beam current size. The beam current of a field emission electron gun is typically much smaller than milliamp levels. In order to improve the detection reliability and reduce adverse effects of electron beams on device charging, the beam current is set to be in the nanoampere level.
3) The field emission electron gun irradiates the nano-electronic device vertically, and adopts a surface scanning mode to measure secondary electron current and electron beam induced current of each point in real time, and finally, a measurement result of relevant current at each point of the whole device is obtained.
4. Reconstruction of internal microstructures
And (3) inputting the secondary electron current and the electron beam induced measurement result of the detection object into a trained F-SRU network, and reconstructing the internal microstructure of the nano electronic device by 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, and verifies the feasibility and reliability of the detection result of the present invention.
The data acquisition point during the measurement is generally about ten seconds after the start of irradiation. At this time, 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 of 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 fast F-SRU deep learning model, which only needs to detect secondary electron current and electron beam induced current of a device, establishes the relation between an internal microstructure and current, and can fast detect the internal microstructure of a nano electronic device.
2) The invention combines an electron scattering model, provides an interface trapping model aiming at internal interface characteristics, 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 electron beam induced current and secondary electron current results.
3) The conventional electron beam induced current measurement results are unstable due to interference from 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, the grid-shaped metal bracket above only receives electron beam induced current, the double-layer structure can effectively eliminate adverse effects of the displacement current, and the accuracy of electron beam induced current measuring results is improved.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and any simple modification, variation and equivalent variation of the above embodiment according to the technical matter of the present invention still fall within the scope of the technical solution of the present invention.
Claims (10)
1. The method for rapidly detecting the internal microstructure of the nano electronic device based on the F-SRU network is characterized by comprising the following steps of:
step 1: aiming at various nano-electronic devices, under given electron beam working conditions and physical parameter conditions, adopting numerical calculation to obtain secondary electron currents and electron beam induced currents corresponding to different microstructures, and forming 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 microstructure in the device and related current by using the calculated data set of the secondary electron current and the electron beam induced current;
step 3: constructing a measuring platform, and rapidly measuring secondary electron current and electron beam induced current of a detection object;
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 a nanoelectronic device based on an F-SRU network according to claim 1, wherein in step 1, the numerical calculation process includes: firstly, calculating a scattering process of electrons in the device, then calculating an interface process and a charge transport process, and finally obtaining secondary electron current and electron beam induced current.
3. The method for rapidly detecting the internal microstructure of the nano-electronic device based on the F-SRU network according to 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:
wherein σ represents a scattering cross section; θ represents the scattering angle, representing the angle between the two collisions in the direction; f (θ) and g (θ) represent an incident and a scattering branching function, respectively; p (P) l And P l 1 Representing a Legendre polynomial and its associated polynomial;is a fractional phase shift;
the inelastic scattering cross section can be obtained by using Penn dielectric function, and the inelastic mean free path is as follows:
wherein a is 0 Is the Bohr radius, hω is the energy loss; lambda (lambda) in-e A non-elastic mean free path; epsilon (q, omega) is the dielectric function of the solid that characterizes a particular electron excitation process, im (-1/epsilon (q, omega)) is the energy loss function that determines the probability of occurrence of inelastic scattering events;
inelastic mean free pathThe method comprises the following steps:
in the method, in the process of the invention,E F is fermi energy, after obtaining the lost energy of the primary electrons, generating a secondary electron in each inelastic scattering event, the secondary electron obtaining energy Δe and exiting the collision point at an angle;
the whole process of electron scattering can be obtained by adopting a Monte Carlo model through the free range of elastic scattering and inelastic scattering and random numbers.
4. The method for rapidly detecting the internal microstructure of the nano electronic device based on the F-SRU network according to claim 2, wherein the interface calculation process is as follows:
the trench structure inside the device and the interfaces between different dielectrics will trap charge, the differential form of free electron concentration is as follows:
wherein T (T) represents the density of the trapped electrons, ε 0 And epsilon r Vacuum dielectric constant and sample relative dielectric constant, N T Representing the capture density, S PF Is the capture coefficient;
because of the existence of excessive positive ions at the interface of the semiconductor and the insulating layer, positive charges exist at the interface, and the negative charges of the insulating layer are caused to move to the interface, a model based on fixed surface charges is built, and firstly, the positive charge density P with a certain size is assumed to exist at the interface FB The method comprises the steps of carrying out a first treatment on the surface of the At the initial calculation, the initial value of the positive charge density at the interface of different media inside the device becomes:
P(0)＝P FB (5)
it is accumulated into the positive charge density and the electric field distribution is calculated.
5. The method for rapidly detecting the internal microstructure of the nano-electronic device based on the F-SRU network according to claim 2, wherein the charge transport process is calculated as follows:
free charges in the device are transferred under the action of an electrostatic field and a density gradient, and traditional charge transport is described by adopting a current continuity equation:
wherein μ and D are electron mobility and diffusion coefficient, respectively; in order to improve the accuracy of the model, for the transportation under the high field condition in the device under the irradiation of the electron beam, the dynamic electron mobility correction is as follows:
wherein E is the electric field strength of the current position, ε 0 Is optical phonon energy; accordingly, the dynamic diffusion coefficient is corrected as:
thus, the dynamic transport process is modified as:
。
6. the method for rapidly detecting the internal microstructure of the nano-electronic device based on the F-SRU network according to claim 2, wherein the secondary electron current and the electron beam induced current are calculated as follows:
constructing a three-dimensional discrete coordinate system, and calculating electric field components at the nodes (i, j, k) from the spatial 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 three-dimensional system (i, j, k) after gridding processing, the values of the electron beam induced currents are calculated as follows:
where J (i, J, k) represents the current density at node (i, J, k), Δx, Δy, and Δz are the grid dimensions for each direction.
7. The method for rapidly detecting the internal microstructure of a nanoelectronic device based on an F-SRU network according to claim 1, wherein in step 2, the calculation process of the F-SRU network model is as follows:
wherein x is t Is an input door, f t In the state of forgetting to leave the door, r t To reset the door state c t Is an internal state; w, W f And W is equal to r Respectively, parameter matrix, v f 、v r And b r Respectively obtaining parameter matrixes through training;
in the optimization solution of the F-SRU network model, in order to improve the convergence of an optimization algorithm, deviation adjustment based on random adjustment parameters is provided, and the flow is as follows:
1) Initializing learning rate mu, W t Gradient g t First moment m of gradient t And second moment v t ；
2) Iteration g t 、m t V t ：
m t ＝β 1 m t-1 +(1-β 1 )g t (17)
Wherein beta is 1 And beta 2 Attenuation coefficients respectively representing a first moment and a second moment;
3) Calculating deviation:
m′ t ＝m t /(1-β 1 )-η 1 g t (19)
wherein eta is 1 And eta 2 The first-order and second-order random adjustment parameters are respectively as follows:
wherein R is 1 And R is 2 Respectively the intervals [0,1 ]]Random numbers distributed uniformly, in order to increase the iteration speed; 1/(1+e) -t ) The function of (2) is to increase the ability of the algorithm to jump out of local optimum at the later stage of the iteration.
8. The method for rapidly detecting the internal microstructure of the nano-electronic device based on the F-SRU network according to claim 7, wherein the flow of the construction of the internal microstructure by the F-SRU network and the relation between the secondary electron current and the electron beam induced current 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 related parameters;
4) Training of F-SRU network: training the F-SRU network by 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 nano-electronic device based on the F-SRU network according to claim 1, wherein in the step 3, a 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 ammeter, the nano-electronic device is glued on the sample stage through conductive glue in actual measurement, the field emission electron gun is positioned right above the sample stage, the Faraday cup is arranged above the sample stage for measuring secondary electron current, and the ammeter is positioned above the field emission electron gun, and the ammeter is arranged below the sample stage and is connected with the sample stage for measuring electron beam induced current.
10. The method for rapidly detecting the internal microstructure of the nano electronic device based on the F-SRU network according to claim 9, wherein the sample stage is a double-layer structure formed by a metal platform and a latticed metal bracket.
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