Ion implantation simulation method, electronic device, and storage medium

Abstract

Example embodiments of the present disclosure relate to an ion implantation simulation method, an electronic device and a storage medium. The method comprises: determining a real-time energy of an incident ion before the ion collides with an atom in a semiconductor material matrix; in response to the real-time energy being greater than a first energy threshold, simulating a motion trajectory of the ion in the semiconductor material matrix using a BCA model; and in response to the real-time energy being less than or equal to the first energy threshold, selecting a corresponding model from the BCA model and an MD model to simulate an implantation process of the ion, wherein the implantation process of the ion in the semiconductor material matrix is simulated using the MD model. Embodiments of the present disclosure can achieve accurate simulation of the entire energy range (high energy to low energy) of ion implantation.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure mainly relate to the computer simulation technology of ion implantation process, and more particularly, to an ion implantation simulation method, an electronic device and a storage medium. BACKGROUND

[0002] Ion implantation is a key process of semiconductor manufacturing, which realizes the nanoscale controllable adjustment of three-dimensional spatial distribution (concentration gradient, junction depth, lateral diffusion) of doping elements by precisely implanting high-energy charged ion beams into a semiconductor material substrate (e.g., a semiconductor device, a semiconductor device, a semiconductor device, etc.), and is a decisive process link of the electrical performance of advanced semiconductor material substrates.

[0003] At present, the ion implantation simulation models mainly include a Binary Collision Approximation (BCA) model and a Molecular Dynamics (MD) model. The BCA model assumes that the collision between ions and target atoms is an independent binary collision event, and ignores the many-body interaction and atomic-level dynamic evolution. The BCA model has high calculation efficiency and is suitable for high-energy ion (e.g., ion with energy greater than 1 keV) implantation simulation, but it is difficult to accurately describe the low-energy ion (e.g., ion with energy less than or equal to 1 keV) implantation process or the cascade collision effect in the crystal material. The MD model can simulate atomic-level interaction and defect dynamic evolution, but has low calculation efficiency and is difficult to handle long-range transport of high-energy ions and large-size material systems.

[0004] It can be seen that the current ion implantation simulation method faces the challenge of balancing the calculation efficiency and accuracy. SUMMARY

[0005] According to example embodiments of the present disclosure, an ion implantation simulation scheme is provided to at least partially overcome the above or other potential deficiencies.

[0006] In a first aspect of the present disclosure, an ion implantation simulation method is provided. The method comprises: determining a real-time energy of an incident ion before the ion collides with an atom in a semiconductor material substrate; in response to the real-time energy being greater than a first energy threshold, using a BCA model to simulate a motion trajectory of the ion in the semiconductor material substrate; and in response to the real-time energy being less than or equal to the first energy threshold, selecting a corresponding model from the BCA model and a MD model to simulate an implantation process of the ion, including using the MD model to simulate the motion trajectory of the ion in the semiconductor material substrate. Embodiments of the present disclosure achieve accurate simulation of ion implantation in a full energy range from high energy to low energy by mixed use of the BCA model and the MD model in ion implantation simulation.

[0007] In a second aspect of the disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled with the processor, the memory having stored therein instructions that, when executed by the processor, cause the device to perform actions. The actions include determining a real-time energy of an incident ion before a collision with an atom in a semiconductor material matrix, in response to the real-time energy being greater than a first energy threshold, simulating a trajectory of the ion in the semiconductor material matrix using a BCA model, and in response to the real-time energy being less than or equal to the first energy threshold, selecting a respective model from among the BCA model and a MD model to simulate an implantation process of the ion, including simulating the trajectory of the ion in the semiconductor material matrix using the MD model.

[0008] In some embodiments, selecting the respective model from among the BCA model and the MD model in response to the real-time energy being less than or equal to the first energy threshold can include selecting the respective model from among the BCA model and the MD model based on a distance between the ion and an atom in the semiconductor material matrix that is to collide with the ion to simulate the implantation process of the ion.

[0009] In some embodiments, wherein selecting the respective model from among the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion can include, in response to the real-time energy being less than or equal to the first energy threshold and greater than a second energy threshold, selecting the respective model from among the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion to simulate the trajectory of the ion in the semiconductor material matrix.

[0010] In some embodiments, selecting the respective model from among the BCA model and the MD model in response to the real-time energy being less than or equal to the first energy threshold can further include, in response to the real-time energy being less than or equal to the second energy threshold, simulating the trajectory of the ion in the semiconductor material matrix using the MD model until the ion stops.

[0011] In some embodiments, selecting the respective model from among the BCA model and the MD model in response to the real-time energy being less than or equal to the first energy threshold can include, in response to the real-time energy being less than or equal to the first energy threshold and greater than a second energy threshold, selecting the respective model from among the BCA model and the MD model based on a distance between the ion and an atom in the semiconductor material matrix that is to collide with the ion to simulate the trajectory of the ion in the semiconductor material matrix, and in response to the real-time energy being less than or equal to the second energy threshold, simulating the trajectory of the ion in the semiconductor material matrix using the MD model until the ion stops.

[0012] In some embodiments, selecting the respective model from the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion can include: in response to the distance being less than or equal to a distance threshold, using the MD model to simulate the trajectory of the ion in the semiconductor material matrix; and in response to the distance being greater than the distance threshold, using the BCA model to simulate the trajectory of the ion in the semiconductor material matrix.

[0013] In some embodiments, the method can further include: in response to determining that the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion is greater than the distance threshold during the MD model simulating the implantation of the ion, performing a switch from the MD model to the BCA model to simulate the trajectory of the ion in the semiconductor material matrix using the BCA model.

[0014] In some embodiments, when the BCA model is about to switch to the MD model based on the real-time energy, the BCA model can provide, to the MD model, at least one of the potential energy, kinetic energy, acceleration, velocity, and position of the ion; and at least one of the potential energy, kinetic energy, acceleration, velocity, and position of the atom that collides with the ion.

[0015] In some embodiments, the BCA model can calculate the potential energy of the ion with all atoms within a spherical region formed with a predetermined distance as a radius of a sphere centered at a position where the ion is located.

[0016] In some embodiments, when the MD model is about to switch to the BCA model based on the distance, the MD model can provide, to the BCA model, at least one of the potential energy, kinetic energy, acceleration, velocity, and position of the ion; and at least one of the potential energy, kinetic energy, acceleration, velocity, and position of the atom that collides with the ion.

[0017] In some embodiments, the method can further include: in response to the real-time energy being less than or equal to a second energy threshold, using the MD model to simulate the trajectory of the ion in the semiconductor material matrix until the ion stops, wherein the second energy threshold is less than the first energy threshold.

[0018] In some embodiments, the first energy threshold can have a value in a range of 200 eV to 5 keV, the second energy threshold can have a value in a range of 100 eV to 900 eV, and the first energy threshold is greater than the second energy threshold.

[0019] In some embodiments, the distance threshold can have a value in a range of 5 angstroms to 15 angstroms.

[0020] In some embodiments, the method can further include determining a potential function based on the type of material in the semiconductor material matrix; and calculating the interaction potential and interaction force between the ion and the atoms in the semiconductor material matrix based on the potential function to update at least one of the potential energy, kinetic energy, acceleration, velocity, and position of the ion and the atoms in the semiconductor material matrix.

[0021] In some embodiments, the method can further include adjusting the velocity of each particle in the system at the time point of the switch using a velocity adjustment formula such that the total energy of the system of the new model after the switch is equal to the total energy of the system of the old model before the switch at the instant when the switch is completed.

[0022] In some embodiments, the velocity adjustment formula can be expressed as wherein is the adjusted velocity of particle i, is the velocity of particle i before the adjustment, is the total kinetic energy of the system before the switch, is the total potential energy of the system before the switch, is the difference between the total energy of the system calculated by the new model after the switch based on the initial state transferred from the old model before the switch and the total energy of the system calculated by the old model before the switch, is the Lagrange multiplier.

[0023] In some embodiments, the velocity adjustment formula can be expressed as wherein is the adjusted velocity of particle i, is the velocity of particle i before the adjustment, is the total kinetic energy of all particles in the system before the switch, is the difference between the total energy of the system calculated by the new model after the switch based on the initial state transferred from the old model before the switch and the total energy of the system calculated by the old model before the switch.

[0024] In some embodiments, before determining the real-time energy of the incident ion before the collision with the atoms in the semiconductor material matrix, the method can further include determining the material type of the semiconductor material matrix; and in response to determining that the material type of the semiconductor material matrix is amorphous material, using a BCA model to simulate the implantation process of the ion.

[0025] In a third aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the method according to the first aspect of the present disclosure.

[0026] The scheme of the embodiments of the present disclosure can realize accurate simulation of full scenarios from high energy to low energy and from crystal to amorphous material, and effectively solve the contradiction between precision and efficiency in the prior art.

[0027] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0028] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent by describing in detail the following embodiments with reference to the attached drawings. In the drawings, the same or similar reference numerals refer to the same or similar elements, in which:

[0029] Figure 1 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented is shown;

[0030] Figure 2 A schematic diagram showing selection of a potential function according to material properties according to some embodiments of the present disclosure is shown;

[0031] Figure 3 A flowchart showing an ion implantation simulation method according to some embodiments of the present disclosure is shown;

[0032] Figure 4 A schematic diagram showing BCA model / MD model switching according to some embodiments of the present disclosure is shown;

[0033] Figure 5 A schematic diagram showing a hybrid model in the Figure 4 of some embodiments of the present disclosure is shown;

[0034] Figure 6A A schematic diagram related to a method of calculating potential energy using a BCA model in the related art is shown;

[0035] Figure 6B A schematic diagram related to a method of calculating potential energy using a BCA model according to some embodiments of the present disclosure is shown; and

[0036] Figure 7 A block diagram of a computing device capable of implementing a plurality of embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0037] Embodiments of the present disclosure will be described below in greater detail with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein, but rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of protection of the present disclosure.

[0038] In the description of embodiments of the present disclosure, the term "comprising" and its conjugations are to be understood to be open-ended, i.e., not limiting to an embodiment having only the recited elements, but including other elements as well. The term "based on" is to be understood as "based, at least in part, on". The term "one embodiment" or "an embodiment" are to be understood not to refer to one and the same embodiment; rather, they refer to "at least one". The terms "first", "second" and the like are to be understood as "different" or "different from one another", unless explicitly stated otherwise.

[0039] As mentioned before, the BCA model assumes that the collision between ions and target atoms is an independent binary collision event, ignoring the many-body interaction and atomic-level dynamic evolution, and has high calculation efficiency, which is suitable for high-energy ion (e.g., ions with energy greater than 1 keV) implantation simulation, but it is difficult to accurately describe the low-energy ion (e.g., ions with energy less than or equal to 1 keV) implantation or the cascade collision effect in a crystal material; the MD model can simulate atomic-level interaction and defect dynamic evolution, but has low calculation efficiency, and it is difficult to handle long-range transport of high-energy ions and large-size material systems.

[0040] However, in the existing ion implantation simulation technology, for a single ion implantation process, i.e., from the beginning of ion implantation into a semiconductor material substrate to the entire process until the ion stops, one of the MD model and the BCA model is always used to simulate the entire motion trajectory of the ion in the semiconductor material substrate. Specifically, in the existing ion implantation simulation technology, for low-energy ion implantation, the MD model is used to simulate the motion trajectory of the ion in the semiconductor material substrate, and for high-energy ion implantation, the BCA model is used to simulate the motion trajectory of the ion in the semiconductor material substrate. Therefore, the existing ion implantation simulation method faces the challenge of balancing the calculation efficiency and accuracy.

[0041] In view of this, the present disclosure provides an improved solution.

[0042] According to some embodiments of the present disclosure, an ion implantation simulation method is provided. The ion implantation simulation method comprises: determining a real-time energy of an incident ion before the incident ion collides with an atom in a semiconductor material matrix; in response to the real-time energy being greater than a first energy threshold, simulating a motion trajectory of the ion in the semiconductor material matrix using a BCA model; and in response to the real-time energy being less than or equal to the first energy threshold, selecting a corresponding model from the BCA model and an MD model to simulate an implantation process of the ion, wherein the implantation process of the ion is simulated using the MD model to simulate the motion trajectory of the ion in the semiconductor material matrix.

[0043] Embodiments of the present disclosure can simulate the motion trajectory of the ion in the semiconductor material matrix by adaptively selecting a corresponding simulation model (e.g., selecting from both the BCA model and the MD model, such as using the BCA model and the MD model successively) according to the real-time energy of the ion throughout the entire process from when the incident ion is implanted into the semiconductor material matrix until the ion stops, for example, using the BCA model to handle high-energy collision events and using the MD model to accurately simulate low-energy and close-range atomic interactions, thereby achieving accurate simulation of the ion implantation full-energy range (from high-energy to low-energy), i.e., being able to take into account both efficiency and accuracy.

[0044] In some embodiments of the present disclosure, the semiconductor material matrix can refer to, but is not limited to, a semiconductor substrate or a semiconductor wafer.

[0045] In some embodiments of the present disclosure, the semiconductor material matrix is an object directly processed by an ion implantation process.

[0046] In some embodiments of the present disclosure, the semiconductor material matrix, such as a semiconductor substrate or a semiconductor wafer, after a series of processes such as ion implantation, is used to manufacture semiconductor devices, semiconductor devices, semiconductor devices, such as integrated circuits, transistors, etc.

[0047] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0048] Reference will first be made to Figure 1 which shows a schematic diagram of an example environment 100 in which a plurality of embodiments of the present disclosure can be implemented. As Figure 1 shown, the example environment 100 includes a computing device 110 and a client 120.

[0049] In some embodiments, the computing device 110 can interact with the client 120. For example, the computing device 110 can receive an input message from the client 120 and output a feedback message to the client 120. In some embodiments, the input message from the client 120 can be energy data of incident ions. The computing device 110 can perform corresponding mathematical operations on the energy data of incident ions and output corresponding results of the operations to the client 120.

[0050] In some embodiments, the computing device 110 can include, but is not limited to, a personal computer, a server computer, a handheld or laptop device, a mobile device (such as a mobile phone, a personal digital assistant PDA, a media player, etc.), a consumer electronic product, a small computer, a large computer, a cloud computing resource, etc.

[0051] It should be understood that the structure and function of the example environment 100 are described for illustrative purposes only and are not intended to limit the scope of the subject matter described herein. The subject matter described herein can be implemented in different structures and / or functions.

[0052] The technical solutions described above are only for examples, not to limit the disclosure. It should be understood that the example environment 100 can also have other various implementations. In order to more clearly explain the principles of the disclosed solutions, the following will be described in more detail with reference to Figure 3

[0053] The following will be described with reference to Figure 2 , Figure 2 A schematic diagram of selecting a potential function according to material properties is shown according to some embodiments of the disclosure.

[0054] Referring to Figure 2 , the simulation tool can first determine the material type of the semiconductor material matrix, and then automatically select the potential function according to the material type. In some embodiments, the simulation tool can include, but is not limited to, a simulation software, a simulation program, etc. In some embodiments, the simulation tool can first construct a material potential function library based on the material type, and automatically select a potential function or a user-specified potential function at runtime according to the material type of the material where the ion is located, such as a Lennard-Jones (LJ) potential, a Morse potential, a Ziegler-Biersack-Littmark (ZBL) potential, a Tersoff potential, an Embedded Atom Method (EAM) potential, etc., as shown in Figure 2

[0055] More specifically, referring to Figure 2 ​​When the material type is determined to be metallic, the simulation tool can automatically select the EAM potential; when the fundamental force constituting the material type is determined to be covalent bond, the simulation tool can automatically select the EAM potential; when it is determined to be a high-energy collision, the simulation tool can automatically select the ZBL potential; when the fundamental force constituting the material type is determined to be covalent bond, the simulation tool can automatically select the EAM potential; when the constituting material is determined to be diatomic or molecular, the simulation tool can automatically select the Morse potential; otherwise, the simulation tool automatically selects the LJ potential.

[0056] In some embodiments, the simulation tool can read the structure of the semiconductor substrate before performing ion implantation simulation (e.g., before the incident ions collide with atoms in the semiconductor substrate), where the structure of the semiconductor substrate includes the material type of each region. Therefore, before the ions are incident on the semiconductor substrate, the simulation tool already knows which materials in the semiconductor substrate the ions will interact with and can pre-construct the corresponding potential function types.

[0057] In some embodiments, the simulation tool can read the type of incident ions and the structure of the semiconductor material matrix when ions are incident on the matrix, and determine the potential function type of the ions and the material atoms in each region of the semiconductor material matrix.

[0058] In some embodiments, the incident ions are boron, and the semiconductor substrate comprises aluminum and silicon. Before the ions collide with atoms in the semiconductor substrate (e.g., before the simulation), the simulation tool can construct boron-aluminum and boron-silicon potential functions based on the material type. For example, during the simulation, the tool selects the boron-aluminum potential function when it determines that the ions are currently in an aluminum substrate, and the boron-silicon potential function when it determines that the ions are currently in a silicon substrate.

[0059] In some embodiments, the simulation tool can select an appropriate potential function based on a preset material type and a corresponding potential function library. By selecting a suitable potential function, the simulation speed can be significantly improved while maintaining reasonable accuracy, making it suitable for large-scale device simulation. For example, by selecting a potential function with clear physical meaning, the interaction between ions and matrix atoms can be described more accurately, thereby accurately calculating ion range, damage, and doping distribution.

[0060] The following is combined Figure 3 An ion implantation simulation method 300 according to an embodiment of the present disclosure is described.

[0061] like Figure 3 As shown, at 302, the real-time energy of the incident ion before it collides with atoms in the semiconductor material matrix is ​​determined.

[0062] In some embodiments, the real-time energy can be determined by a simulation tool. The real-time energy can be determined in various conventional ways.

[0063] At 304, in response to the real-time energy being greater than the first energy threshold, a BCA model is used to simulate a trajectory of the ion in the semiconductor material matrix.

[0064] As mentioned previously, the BCA model is suitable for high-energy ion (e.g., ion with energy greater than 1 keV) implantation simulation and is computationally efficient. Therefore, when the real-time energy is greater than the first energy threshold, the BCA model can be used for the simulation.

[0065] In some embodiments, the ion implantation simulation method 300 can be implemented using software, hardware, or any combination thereof.

[0066] Reference is made below to Figure 4 described, Figure 4 A schematic diagram of BCA model / MD model switching is shown according to some embodiments of the present disclosure.

[0067] In some embodiments, a simulation tool such as a simulation software can determine or judge the material type in the semiconductor material matrix before determining the real-time energy of the incident ion before collision with atoms in the semiconductor material matrix, and in response to determining that the material type of the semiconductor material matrix is amorphous material, use the BCA model to simulate the implantation process of the ion. In addition, in response to determining that the material type of the semiconductor material matrix is crystalline material, an energy range is determined.

[0068] As Figure 2 shown, in the case where the real-time energy of the incident ion is greater than the first energy threshold (e.g., greater than 1 keV), the BCA model is selected and used for ion implantation simulation. It should be noted that in the present disclosure, the real-time energy of the incident ion before being implanted into the semiconductor material matrix corresponds to the initial energy of the ion.

[0069] Reference is made back to Figure 3 described.

[0070] At 306, in response to the real-time energy being less than or equal to the first energy threshold, a corresponding model is selected from the BCA model and the MD model to simulate the implantation process of the ion, including using the MD model to simulate a trajectory of the ion in the semiconductor material matrix.

[0071] In some embodiments, in response to a real-time energy less than or equal to a first energy threshold, selecting a corresponding model from the BCA model and the MD model includes: in response to a real-time energy less than or equal to the first energy threshold and greater than a second energy threshold, selecting a corresponding model from the BCA model and the MD model based on the distance between the ion and an atom in the semiconductor material matrix that will collide with the ion to simulate the trajectory of the ion in the semiconductor material matrix; and in response to a real-time energy less than or equal to the second energy threshold, using the MD model to simulate the trajectory of the ion in the semiconductor material matrix until the ion stops.

[0072] In some embodiments, selecting a model from the BCA model and the MD model based on the distance between the ion and an atom in the semiconductor material matrix that will collide with the ion includes: using the MD model to simulate the trajectory of the ion in the semiconductor material matrix in response to the distance being less than or equal to a distance threshold; and using the BCA model to simulate the trajectory of the ion in the semiconductor material matrix in response to the distance being greater than the distance threshold.

[0073] In some embodiments, Figure 3 The publicly available ion implantation simulation methods can be executed by simulation tools (e.g., simulation software or simulation programs).

[0074] The following continues to refer to... Figure 4 Describe it. For example... Figure 4 As shown, in response to determining that the real-time energy of the incident ion is less than or equal to a first energy threshold and greater than (or greater than or equal to) a second energy threshold (e.g., 100 eV to 1 keV), a hybrid model (e.g., determining or selecting the appropriate model from the BCA model and the MD model) is used to simulate the ion's trajectory in the semiconductor material matrix. During the use of the hybrid model, real-time switching between the two models can be performed based on the real-time energy.

[0075] In some embodiments, the switching logic of the hybrid model can determine which model to adopt, or which model to select, based on the distance between the ion and the atom in the semiconductor material matrix that will collide with the ion.

[0076] In some embodiments, for long-range collisions, such as when the distance between the ion and the colliding atom is greater than or equal to a distance threshold, the BCA model can be used to perform ion implantation simulations. In some embodiments, for short-range collisions, such as when the distance between the ion and the colliding atom is less than a distance threshold, the MD model can be used to perform ion implantation simulations.

[0077] In some embodiments, the distance threshold can range from 5 to 15 angstroms (A), but the present disclosure is not limited thereto and can be adjusted according to actual applications.

[0078] In some embodiments, in response to determining or judging that the real-time energy of the ion is less than or equal to the first energy threshold (preferably, in response to determining or judging that the real-time energy of the ion is less than or equal to the first energy threshold and greater than or equal to the second energy threshold), a distance between the ion and an atom of the material to be collided with the ion (hereinafter, the distance can be referred to as a real-time distance) is determined or judged, and whether to perform switching from the BCA model to the MD model is determined based on the real-time distance.

[0079] In some embodiments, in response to determining or judging that the real-time energy of the ion is less than or equal to the first energy threshold, a corresponding model is selected from the BCA model and the MD model based on a distance between the ion and an atom of the semiconductor material matrix to be collided with the ion, to simulate a motion trajectory of the ion in the semiconductor material matrix.

[0080] In some embodiments, selecting a corresponding model from the BCA model and the MD model based on the distance between the ion and the atom of the semiconductor material matrix to be collided with the ion can include: in response to the distance being less than or equal to the distance threshold, using the MD model to simulate the motion trajectory of the ion in the semiconductor material matrix; and in response to the distance being greater than the distance threshold, using the BCA model to simulate the motion trajectory of the ion in the semiconductor material matrix.

[0081] In some embodiments, in response to determining or judging that the real-time energy of the ion is less than or equal to the first energy threshold, the simulation tool selects the MD model from the BCA model and the MD model based on the real-time distance being less than the distance threshold (for example, performs switching from the BCA model to the MD model), and uses the MD model to simulate the motion trajectory of the ion in the semiconductor material matrix; otherwise, the simulation tool uses the BCA model to simulate (for example, continues to simulate) the motion trajectory of the ion in the semiconductor material matrix based on the real-time distance being greater than or equal to the distance threshold.

[0082] In some embodiments, the model switching can not be performed before the real-time distance is less than the distance threshold.

[0083] It should be noted that one or more collisions can occur during the period from when the real-time distance is greater than or equal to the distance threshold to when the real-time distance is less than the distance threshold.

[0084] In some embodiments, even after a certain collision (e.g., the 100th collision), there can be a case where the distance (e.g., the real-time distance) between the ion and the atom of the material that is about to collide with the ion is less than the distance threshold.

[0085] In some embodiments, after the BCA model is switched to the MD model, in response to the real-time distance being greater than or equal to the distance threshold, the MD model can be switched back to the BCA model, and the BCA model can be used to simulate (e.g., continue to simulate) the ion’s trajectory in the semiconductor material matrix.

[0086] It should be noted that the distance threshold mentioned in the present disclosure can take any value in the range of 5 to 15 angstroms. In some embodiments, the distance threshold can preferably be 10 angstroms.

[0087] In some embodiments, for performing the switching from the BCA model to the MD model, the BCA model can be referred to as the old model before the switching, and the MD model can be referred to as the new model after the switching.

[0088] In some embodiments, for performing the switching from the MD model to the BCA model, the MD model can be referred to as the old model before the switching, and the BCA model can be referred to as the new model after the switching.

[0089] In some embodiments, at the switching point (the time point of performing the switching, or the switching time), the old model before the switching can pass all its data (e.g., including but not limited to: at least one of the potential energy, kinetic energy, acceleration, speed, deflection angle and position of the ion, and at least one of the potential energy, kinetic energy, acceleration, speed, deflection angle and position of the atom of the material that collides with the ion) to the new model after the switching.

[0090] In some embodiments, all the data of a certain model can refer to all the data that the model has obtained about the ion and the material atom that collides with it before the switching, for example, including but not limited to: the energy lost by the ion in each collision in the material, the speed, acceleration and energy of the ion deflected after each collision, the angle of the ion deflected after each collision, the position of the ion after each collision, and the speed, acceleration and energy of the atom in the material after each collision.

[0091] In some embodiments, at the switching point or before the switching, all the data (e.g., including but not limited to: at least one of the potential energy, kinetic energy, acceleration, speed, deflection angle and position of the ion, and at least one of the potential energy, kinetic energy, acceleration, speed, deflection angle and position of the atom of the material that collides with the ion) obtained by the MD model can be stored or saved in the MD model.

[0092] In some embodiments, the energy of the ion after each collision and the energy of the atoms in the material after each collision mentioned in the present disclosure can include at least one of kinetic energy and potential energy.

[0093] In some embodiments, in response to the real-time energy (e.g., original energy) of the ion being less than or equal to the second energy threshold, the MD model can be used to simulate the motion trajectory of the ion in the semiconductor material matrix, for example, until the ion stops.

[0094] In some embodiments, in the low-energy stage, a time-integration algorithm can be adopted to calculate the interatomic interaction force by solving Newton's motion equation, simulate the collision process between the ion and the atoms of the material, and obtain the position, velocity, acceleration, kinetic energy, and potential energy of the ion and the atoms.

[0095] In some embodiments, the first energy threshold has a value range of 200 eV to 5 keV, the second energy threshold has a value range of 100 eV to 900 eV, and the first energy threshold is greater than the second energy threshold. In some embodiments, the first energy threshold can preferably be 1 keV. In some embodiments, the second energy threshold can preferably be 100 eV. It should be understood that the above value ranges are illustrative, and the present disclosure is not limited thereto.

[0096] In some embodiments, simulating the motion trajectory of the ion in the semiconductor material matrix using the BCA model can be implemented or realized as in the related art, for example, as can be appreciated by those skilled in the art.

[0097] In some embodiments, simulating the motion trajectory of the ion in the semiconductor material matrix using the MD model can be implemented or realized as in the related art, for example, as can be appreciated by those skilled in the art.

[0098] In some embodiments, the simulation tool or simulation model (e.g., BCA model, MD model) can record each collision process in real time and update the current ion state data. In some embodiments, the current ion state data can include the current potential energy, acceleration, velocity, kinetic energy, and position of the ion. In some embodiments, the current ion state data can be stored in the simulation tool.

[0099] In some embodiments, the simulation tool can obtain the energy lost by the ion in each collision in the semiconductor material matrix, the velocity and acceleration of the ion after each collision, the direction and position of the ion after each collision, the velocity, acceleration and energy of the atoms in the material after each collision, and the position where the ion eventually stops, and finally can obtain the precise trajectory of the ion in the semiconductor material matrix, i.e. the motion trajectory of the ion in the semiconductor material matrix, including the direction and position of the ion after each collision.

[0100] In some embodiments, the precise result can include the precise trajectory of the ion in the semiconductor material matrix, i.e. the motion trajectory of the ion in the semiconductor material matrix, including the direction and position of the ion after each collision.

[0101] In some embodiments, the simulation tool can simulate one or more ion implantation processes, for example, simulate the collision of ions with atoms in various regions of the semiconductor material matrix and finally stop, record the final position, count all ion positions, and calculate the concentration distribution of ions in the semiconductor material matrix. It should be noted that when simulating multiple ion implantations, the simulation tool performs a complete implantation process of one ion until the ion stops, and then performs the implantation of the next ion, so the ion is determined each time.

[0102] In some embodiments, in the simulation of one ion implantation process, the ion can collide with material atoms in various regions of the semiconductor material matrix, and the energy loss for each collision, the position, direction, velocity, acceleration, kinetic energy and potential energy of the ion for each collision, and the position, direction, velocity, acceleration, kinetic energy and potential energy of the atoms for each collision can be obtained. In some embodiments, the simulation tool can read the initial energy of the ion when the ion is incident on the semiconductor material matrix. In some embodiments, the simulation tool or simulation model can obtain the energy and deflection direction of the ion at the moment when the first collision of the ion ends, so it can know which atom or atoms in the semiconductor material matrix the ion will collide with next, thereby determining the distance between the ion and the atom that will collide with the ion.

[0103] In some embodiments, when a high-speed moving ion is incident on a material (e.g. a semiconductor material matrix), it will interact with the atoms in the material. This interaction mainly includes the following two kinds: nuclear stopping and electronic stopping. Nuclear stopping can refer to the direct collision between the ion and the nucleus of the material atom, which will cause a change in the direction of motion of the ion and energy loss, while the material atom gains energy and can be dislocated. Electronic stopping can refer to the interaction between the ion and the electrons around the nucleus of the material atom, so that the ion loses part of the energy during the movement due to the electronic stopping effect.

[0104] In some embodiments, when performing simulation (e.g., calculation), both atomic nuclei and extranuclear electrons in a material (e.g., a semiconductor material matrix) can be attributed to an atom or collectively referred to as an atom. In some embodiments, the expression “ion collides with an atom” can mean calculating both nuclear collision of the ion with the atomic nucleus and electronic stopping of the ion with the extranuclear electron. In some embodiments, the expression “ion collides with an atomic nucleus” can be considered without considering the extranuclear electron stopping.

[0105] In some embodiments, an electronic stopping model can be expressed as:

[0106]

[0107] wherein, is the energy loss per unit distance of the ion due to electronic stopping, N is the atomic density, is the electronic stopping power.

[0108] In some embodiments, both the BCA model and the MD model can consider a nuclear stopping (nuclear collision) model and an electronic stopping model to determine the nuclear collision energy loss and the electronic stopping energy loss of the ion when a collision occurs.

[0109] In some embodiments, for amorphous materials, using the BCA model to calculate the interaction of the ion with the atoms of the material is sufficient to obtain accurate results due to the internal chaotic and disordered arrangement of atoms.

[0110] In some embodiments, for crystalline materials, the BCA model and the MD model can be dynamically switched according to the real-time energy of the ion (e.g., including but not limited to: when the energy of the ion is less than a second energy threshold, performing switching from the BCA model to the MD model).

[0111] In some embodiments, the collision process of the ion with the atoms in the semiconductor material matrix can be calculated using various theories, such as the Lindhand Scharff and Schiott (LSS) theory (dividing the specific collision process into nuclear stopping and electronic stopping), Rutherford scattering theory, etc.

[0112] In some embodiments, for each collision of the ion with the atoms in the semiconductor material matrix (hereinafter, this can be referred to as, for example, a collision event), at least one of the potential energy, kinetic energy, acceleration, speed, and position of the ion and the atom before and after the collision can be updated, for example, according to the LSS theory.

[0113] In some embodiments, in the high-energy (e.g., the energy of the ions exceeds the first energy threshold) ion implantation stage, the BCA model is used as the core calculation model, the interaction between the incident ions and the material is simplified as a two-body collision process between the ions and the atoms of the material, and the energy loss and angular deflection during the collision can be calculated using the classical scattering theory (such as the LSS theory or the Rutherford scattering theory) to obtain the position, velocity, and kinetic energy data of the ions and the atoms.

[0114] In some embodiments, the electron stopping model is related to the atomic density of the material (e.g., the semiconductor material matrix). In some embodiments, the BCA model can use the average atomic density of the material. In some embodiments, the MD model can use the local density, that is, a spherical region is formed with the position of the ion (i.e., the position of the ion at each collision, or the real-time position of the ion) as the center, the radius of the sphere is called the cutoff radius, and the number of all atoms falling inside the sphere is counted to obtain the atomic density of the region. Specifically, the atomic density of the region is equal to the number of all atoms inside the spherical region divided by the volume of the spherical region.

[0115] In some embodiments, the cutoff radius can be selected as the maximum distance between the ion and the atom. Specifically, if the distance between the ion and the atom is far, the interaction force is small, which means that the atom is difficult to affect the motion of the ion, and at this time, the atoms outside the cutoff radius can not be considered.

[0116] In the present disclosure, the cutoff radius is not explicitly limited. In some embodiments, the cutoff radius can generally be no greater than a few lattice constants, and 1-2 lattice constants are appropriate.

[0117] In some embodiments, the motion of a particle is described by Newton's equation of motion, which has the basic form where m is the mass of the particle, r is the position vector of the particle, t is time, and F is the total force acting on the particle.

[0118] In some embodiments, in the MD model simulation, Newton's equation of motion can be written as where m i is the mass of the i-th atom, r i is the position vector of the i-th atom, and F i is the total force acting on the i-th atom. The total force F i experienced by each atom can be calculated according to the potential function. From the potential function V(r), we have The particle state is updated using time integration algorithms (such as the Verlet algorithm, Runge-Kutta algorithm, etc.). In some embodiments, taking the Verlet algorithm as an example, by choosing an appropriate time step Δt, the position update can be expressed as... Speed ​​update can be expressed as Accelerated update can be represented as F i (t+Δt) is the new position at (t+Δt) according to It is obtained through calculation.

[0119] In some embodiments, for each collision event, a collision between at least two particles (including ions and atoms in a semiconductor material matrix) is involved, where the particle deflection angle Θ and the nuclear collision energy loss Δ are considered. E The following formulas can be used, but are not limited to:

[0120]

[0121]

[0122] in, E For collision energy, r The distance between the two particles. r min This represents the minimum distance between particles during the collision process. V ( r ) is the potential function. p For collision parameters, M 1 and M 2 represents the mass of the two particles.

[0123] In some embodiments, the interaction potential can be calculated based on the potential function, and the interaction force can be calculated based on the derivative of the potential function, to update one or more of the state information of the particle, including but not limited to kinetic energy, potential energy, acceleration, velocity, and position.

[0124] In some embodiments, the state information corresponding to the end of the previous collision may refer to the original state for the next collision.

[0125] In some embodiments, taking the Lennard-Jones potential as an example, the interaction potential and interaction force between two particles can be respectively:

[0126]

[0127]

[0128] Where ε and σ are empirical parameters, and r is the distance between particles.

[0129] According to Newtonian classical mechanics, F = ma, where m is the mass of the particle, the acceleration of the particle can be calculated, and the acceleration of the particle is updated, and the velocity and position information is updated within a time step t.

[0130] In some embodiments, the kinetic energy of the particle can be calculated by .

[0131] In some embodiments, the deflection angle of the ion can refer to the included angle between the velocity direction of the ion after the collision and the velocity direction of the ion before the collision.

[0132] Reference is made below to Figure 5 , Figure 5 schematic diagrams of the hybrid model in Figure 4 some embodiments of the present disclosure.

[0133] In some embodiments, in order to ensure energy conservation when switching between the BCA model and the MD model, an energy transfer method and a total energy correction method can be implemented.

[0134] In some embodiments, when switching from the BCA model to the MD model, the BCA model can transfer the kinetic energy and potential energy of the ion and the kinetic energy and potential energy of the colliding atom to the MD model. It should be noted that in the existing ion implantation simulation method, when the BCA model processes collisions, the complex interaction potential energy between atoms is usually ignored. The BCA model mainly focuses on the statistical results of collision trajectories and energy transfer. Therefore, at the moment when the BCA calculation ends and is about to switch to MD, the most direct information that the BCA model can provide is the motion speed (kinetic energy) of the ion and the atom colliding with it. In contrast, according to some embodiments of the present disclosure, when switching from the BCA model to the MD model, the BCA model not only transfers the kinetic energy of the ion and the kinetic energy of the colliding atom to the MD model, but also transfers the interaction potential energy between particles (for example, the potential energy of the ion and the potential energy of the colliding atom) to the MD model, so that the MD model can more accurately calculate the interaction force between the ion and the material atom and the subsequent motion.

[0135] In some embodiments, at the switch from MD model to BCA model, the MD model can pass the kinetic and potential energies of the ions and the kinetic and potential energies of the colliding atoms to the BCA model. In some embodiments, the MD model can accurately calculate the kinetic energy (determined by velocity) and potential energy (determined by relative positions and interaction forces between particles) of all particles in the system. In some embodiments, when the MD model determines or decides that the subsequent process is more suitable for simulation using the more efficient BCA model, the MD model can pass the complete energy state of the system at the current time to the BCA model. This can include the velocities of the particles (kinetic energy) and the potential energy between the particles due to the positional relationship. When the BCA model receives the complete energy state, it needs to know the total energy (kinetic energy + potential energy) state of the particles at this moment in order to correctly start its collision calculation.

[0136] It should be noted that the particles mentioned in the present disclosure can refer to ions and atoms colliding with the ions.

[0137] In some embodiments, methods such as the Lagrange multiplier method or the implicit integration method can be used to correct the total energy. Application of these methods can accurately capture the instantaneous dynamics of ion-atom collisions and avoid numerical divergence problems in energy-sensitive regions during model switching.

[0138] In some embodiments, the Lagrange multiplier method can strongly constrain the total energy of the system and correct the velocity to achieve instantaneous conservation.

[0139] In some embodiments, at the switching point, the system total energy E new calculated by the new model (receiver) according to the initial state passed by the old model (sender) is calculated. old The system total energy E new at the switching point calculated by the old model (sender) before switching is calculated. The energy deviation ΔE = E old (E new must be strictly equal to E old . In order to satisfy this rule instantaneously, a mathematical constraint force (i.e., Lagrange multiplier) is introduced to directly fine-tune the velocity of the particles so that the total energy of the system is accurately and forcibly corrected back to the value at the end of the old model at the moment of switching completion.

[0140] In some embodiments, the Lagrange multiplier can be obtained using the velocity scaling method (similar to the Berendsen heat bath).

[0141] In some embodiments, the total energy of the system can be corrected by adjusting the velocity v i of each particle.

[0142] In some embodiments, the velocity adjustment formula can be used to adjust the velocity of each particle in the system at the time point of the switch so that the total energy of the system of the new model after the switch equals the total energy of the system of the old model before the switch at the moment when the switch is completed.

[0143] In some embodiments, for example, the energy deviation at the switch point The velocity adjustment formula can be expressed as where is the adjusted velocity of particle i, is the velocity of particle i before adjustment, is the total kinetic energy of the system before the switch, is the total potential energy of the system before the switch, is the difference between the total energy of the system of the new model after the switch calculated according to the initial state transferred from the old model before the switch and the total energy of the system of the old model before the switch, is the Lagrange multiplier.

[0144] In some embodiments, the Lagrange multiplier λ is determined by an iterative method or an analytical method, so that the total energy of the adjusted system equals .

[0145] Taking the iterative method as an example, λ can be determined by the following steps:

[0146] Assume the initial value of λ, for example, λ = 0.1;

[0147] Adjust the particle velocity: where is the original velocity of particle i, is the total kinetic energy of the system, is the total potential energy of the system, is the Lagrange multiplier, is the difference between the total energy of the system, is the adjusted velocity;

[0148] Calculate the adjusted total kinetic energy : ;

[0149] Calculate the total energy of the adjusted system : ;

[0150] Calculate the new energy deviation : ;

[0151] If Approaching zero, stop iteration, determine lambda;

[0152] If Not approaching zero, adjust the value of lambda, for example, ;

[0153] Repeat the above steps until Approaching .

[0154] In some embodiments, the implicit integration method can handle energy drift at switching, distributing errors in proportion to kinetic energy.

[0155] In some embodiments, a small energy drift problem that can occur near the switching point, this drift can be due to the short-term disturbance introduced by the model switching operation itself, or the inherent characteristics of the numerical integration method when dealing with such discontinuous events. The energy error ΔE is not simply averaged to all particles, but is distributed according to the proportion of the current kinetic energy of each particle to the total kinetic energy of the system. So that the particles with large kinetic energy get more correction, and the particles with small kinetic energy get less correction. A smooth, small energy correction process is introduced, which distributes the small error ΔE accumulated in this period (for example, more than 10% energy loss, preferably more than 1% energy loss) according to the kinetic energy contribution to each particle, thereby suppressing energy drift.

[0156] In some embodiments, the correction process can include the following steps:

[0157] Calculate the energy deviation ;

[0158] Calculate the total kinetic energy of all particles in the system ;

[0159] Where is the mass of particle i, is the velocity of particle i.

[0160] In some embodiments, for each particle i, the kinetic energy is calculated as a proportion of the total kinetic energy of the system , according to the proportion , the energy error ( ) is distributed, and the velocity of each particle i is adjusted to obtain the adjusted velocity . .

[0161] In some embodiments, the proportion may be , and the adjusted velocity may be rewritten as .

[0162] In some embodiments, the ratio may be , at which time the adjusted speed may be rewritten as .

[0163] In some embodiments, the ratio may be cubic, quartic, etc.

[0164] In some embodiments, the above-mentioned particles can include incident ions and atoms that collide with the ions.

[0165] In some embodiments, the atoms within the cutoff radius around the ion can be selected as the collision atoms.

[0166] Reference is made below to Figure 6A and Figure 6B , Figure 6A shows a schematic diagram related to a method of calculating potential energy using a BCA model in the related art, Figure 6B shows a schematic diagram related to a method of calculating potential energy using a BCA model according to some embodiments of the present disclosure.

[0167] The method of calculating potential energy using a BCA model in the related art can be described with reference to Figure 6A . In the related art, the BCA model only calculates the potential energy of an ion and an atom (e.g., which can be referred to as a target atom) that can collide with the ion in the advancing direction of the ion, so the accurate potential energy of the ion and the target atom cannot be obtained. In Figure 6A , the arrow direction indicates the advancing direction of the ion, and for example, there are two atoms in the advancing direction of the ion, which are denoted as atom 1 and atom 2, and the distances between the ion and atom 1 and atom 2 can be denoted as r1 and r2, respectively. In the example shown in Figure 6A , it is known that the potential function can be represented as V(r), and the potential energy V total(离子) of the ion and all target atoms calculated according to the related art is V(r1) + V(r2).

[0168] By contrast, the method of calculating potential energy using a BCA model in embodiments of the present disclosure can be described with reference to Figure 6B .

[0169] In some embodiments, the BCA model can generate all atoms (e.g., 2x2x2 unit cells) of several unit cells around the ion and the target atom according to the coordinates of the ion and the target atom, and calculate the potential energy of the ion and all atoms within the cutoff radius. In this way, according to embodiments of the present disclosure, the BCA model can be used to more accurately calculate the potential energy.

[0170] In Figure 6B , the arrow direction represents the advancing direction of the ion, and the radius of the dashed circle is the cutoff radius, for example, there are a total of 4 target atoms within the cutoff radius, which are respectively denoted as atom 1, atom 2, atom 3 and atom 4, and the distances between the ion and atom 1, atom 2, atom 3 and atom 4 can be respectively represented as r1, r2, r3 and r4. In Figure 6B the example shown, the potential function can be represented as V(r), and the potential energy V total(离子) of the ion and all target atoms calculated according to the embodiments of the present disclosure is V(r1) + V(r2) + V(r3) + V(r4). Compared with the conventional potential energy calculation method, the potential energy calculation method according to the embodiments of the present disclosure can more accurately calculate the potential energy of the ion and the target atom using the BCA model.

[0171] In some embodiments, the BCA model can be implemented using tools such as SRIM (Stopping and Range of Ions in Matter) or TRIM (TRansport of Ions in Matter) TRIM.

[0172] In some embodiments, the MD model can be implemented using tools such as Large-scale Atomic / Molecular Massively Parallel Simulator (LAMMPS) or Meso-Bio-Nano Explorer (MBN Explorer).

[0173] Some embodiments of the present disclosure provide an adaptive BCA-MD ion implantation simulation method. It should be noted that the examples in the above embodiments are only for illustrating the schemes of the embodiments of the present disclosure, and are not used to limit the schemes of the present disclosure. The schemes of the embodiments of the present disclosure can realize accurate simulation of ion implantation in the full energy range (high energy to low energy) and multiple material systems (crystalline materials and amorphous materials) by simulating at different energy stages of the implanted ion using different models and automatically selecting potential functions based on the material type, and realize full-scale accurate calculation from atomic vibration at the angstrom level to ion distribution at the micron level.

[0174] Compared to existing solutions, the solution disclosed herein achieves accurate simulation of all scenarios from high energy to low energy and from crystalline to amorphous materials by dynamically switching between the BCA model and the MD model and combining them with a material adaptive algorithm. It is applicable to both crystalline and amorphous materials and covers commonly used materials in the field of integrated circuits, such as metals, insulators, and semiconductors.

[0175] The scheme disclosed herein seamlessly switches between modes and ensures the accuracy of model calculations through strict switching conditions and energy conservation algorithms during the simulation process.

[0176] The methods and electronic devices disclosed herein achieve a balance between efficiency and accuracy, alleviating the trade-off between these two aspects found in traditional technologies. These methods and electronic devices can be widely applied to ion implantation process optimization in semiconductor manufacturing, materials science, and nuclear engineering, providing an efficient and precise tool for the digital design of ion implantation processes. Furthermore, these methods and electronic devices can significantly reduce experimental trial-and-error costs, driving technological advancements in semiconductor and advanced materials manufacturing.

[0177] It should be understood that the embodiments shown in the accompanying drawings are merely illustrative of some embodiments of this disclosure and are not intended to limit this disclosure. Embodiments of this disclosure may also have various other forms.

[0178] An electronic device is also disclosed in embodiments of this disclosure. The electronic device includes: a processor; and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: determining the real-time energy of an incident ion before it collides with atoms in a semiconductor material matrix; and, in response to the real-time energy being greater than a first energy threshold, using a BCA model to simulate the trajectory of the ion in the semiconductor material matrix; and, in response to the real-time energy being less than or equal to the first energy threshold, selecting a suitable model from the BCA model and the MD model to simulate the ion implantation process, including using the MD model to simulate the trajectory of the ion in the semiconductor material matrix.

[0179] An embodiment of this disclosure also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an ion implantation simulation method according to an embodiment of this disclosure.

[0180] Figure 7A schematic block diagram of an example of a device 700 that can be used to implement embodiments of the present disclosure is shown. As shown, the device 700 includes a central processing unit (CPU) 701 that can perform various suitable actions and processes in accordance with computer program instructions stored in a read-only memory (ROM) 702 or computer program instructions loaded into a random access memory (RAM) 703 from a storage unit 708. Various programs and data used by the device 700, in addition to the computer program instructions, can also be stored in the RAM 703. The CPU 701, ROM 702, and RAM 703 are connected to each other by a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0181] A plurality of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, a magneto-optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0182] The processing unit 701 performs various methods and processes described above, such as the method 300. For example, in some embodiments, the method 300 can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the CPU 701, one or more steps of the method 300 described above can be performed. Alternatively, in other embodiments, the CPU 701 can be configured to perform the method 300 by any other suitable means, such as by means of firmware.

[0183] The functionality described herein above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0184] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0185] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores program code for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of a system, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0186] Moreover, while operations can be depicted in a particular, serial order, this should not be understood as requiring or implying that such operations be performed in the particular order shown, or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Likewise, while specific implementations are discussed herein, these should not be understood to limit the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in subcombination or as separate embodiments. Accordingly, the particular implementation described herein is illustrative only and not limiting.

[0187] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method of ion implantation simulation, comprising: determining a real-time energy of an incident ion prior to a collision of the ion with an atom in a semiconductor material matrix; in response to the real-time energy being greater than a first energy threshold, simulating a trajectory of the ion in the semiconductor material matrix using a binary collision approximation (BCA) model; and in response to the real-time energy being less than or equal to the first energy threshold, selecting a respective model from among the BCA model and a molecular dynamics (MD) model to simulate an implantation process of the ion, including using the MD model to simulate the trajectory of the ion in the semiconductor material matrix.

2. The method of claim 1, wherein selecting a respective model from among the BCA model and the MD model in response to the real-time energy being less than or equal to the first energy threshold comprises: selecting a respective model from among the BCA model and the MD model based on a distance between the ion and an atom in the semiconductor material matrix that is to collide with the ion to simulate the implantation process of the ion.

3. The method of claim 2, wherein selecting a respective model from among the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion comprises: in response to the real-time energy being less than or equal to the first energy threshold and greater than a second energy threshold, selecting a respective model from among the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion to simulate the trajectory of the ion in the semiconductor material matrix.

4. The method of claim 3, further comprising: in response to the real-time energy being less than or equal to the second energy threshold, using the MD model to simulate the trajectory of the ion in the semiconductor material matrix until the ion stops.

5. The method of claim 3, wherein selecting a respective model from among the BCA model and the MD model based on the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion further comprises: in response to the distance being less than or equal to a distance threshold, using the MD model to simulate the trajectory of the ion in the semiconductor material matrix; and in response to the distance being greater than the distance threshold, using the BCA model to simulate the trajectory of the ion in the semiconductor material matrix.

6. The method of any one of claims 1 to 5, further comprising: during simulation of the implantation process of the ion by the MD model, in response to determining that the distance between the ion and the atom in the semiconductor material matrix that is to collide with the ion is greater than a distance threshold, performing a switch from the MD model to the BCA model to simulate the trajectory of the ion in the semiconductor material matrix using the BCA model.

7. The method of claim 1, wherein when the BCA model is about to switch to the MD model based on the real-time energy, the BCA model provides to the MD model: at least one of potential energy, kinetic energy, acceleration, velocity, and position of the ion; and at least one of potential energy, kinetic energy, acceleration, velocity, and position of an atom that collides with the ion.

8. The method of claim 7, wherein the BCA model calculates potential energy of the ion with all atoms within a spherical region, the spherical region being a region formed with a center at a location where the ion is located and with a predetermined distance as a radius of a sphere.

9. The method of claim 5, wherein when the MD model is about to switch to the BCA model based on the distance, the MD model provides to the BCA model: at least one of potential energy, kinetic energy, acceleration, velocity, and position of the ion; and at least one of potential energy, kinetic energy, acceleration, velocity, and position of an atom that collides with the ion.

10. The method of claim 1, further comprising: in response to the real-time energy being less than or equal to a second energy threshold, simulating a trajectory of the ion in the semiconductor material matrix using the MD model until the ion stops, wherein the second energy threshold is less than the first energy threshold.

11. The method of claim 3 or 4, wherein the first energy threshold has a range of 200 eV to 5 keV, the second energy threshold has a range of 100 eV to 900 eV, and the first energy threshold is greater than the second energy threshold.

12. The method of claim 5, wherein the distance threshold has a range of 5 angstroms to 15 angstroms.

13. The method of claim 1, further comprising: determining a potential function based on a type of material in the semiconductor material matrix; and calculating, based on the potential function, an interaction potential and an interaction force between the ion and an atom in the semiconductor material matrix to update at least one of potential energy, kinetic energy, acceleration, velocity, and position of the ion and the atom in the semiconductor material matrix.

14. The method of claim 6, further comprising: At the switching point, the velocity of each particle in the system is adjusted using the velocity adjustment formula. Adjustments are made so that at the instant the switch is completed, the total system energy of the new model after the switch is equal to the total system energy of the old model before the switch.

15. The method of claim 14, wherein: the velocity adjustment formula is expressed as: wherein is the adjusted velocity of particle i, is the unadjusted velocity of particle i, is the total kinetic energy of the system before the switch, is the total potential energy of the system before the switch, is the difference between the total energy of the system calculated by the new model after the switch from the initial state passed from the old model before the switch and the total energy of the system calculated by the old model before the switch, is the Lagrange multiplier.

16. The method of claim 14, wherein: the velocity adjustment formula is expressed as: wherein is the adjusted velocity of particle i, is the unadjusted velocity of particle i, is the total kinetic energy of all particles in the system before the switch, is the difference between the total energy of the system calculated by the new model after the switch from the initial state passed from the old model before the switch and the total energy of the system calculated by the old model before the switch.

17. The method of claim 6, wherein before determining the real-time energy of the incident ion before colliding with an atom in the semiconductor material matrix, the method further comprises: determining a type of material of the semiconductor material matrix; and in response to determining that the type of material of the semiconductor material matrix is amorphous material, simulating an implantation process of the ion using the BCA model.

18. An electronic device, comprising: a processor; and a memory coupled with the processor, the memory having stored therein instructions which, when executed by the processor, cause the device to perform acts comprising: determining a real-time energy of the incident ion before colliding with an atom in a semiconductor material matrix; in response to the real-time energy being greater than a first energy threshold, simulating a motion trajectory of the ion in the semiconductor material matrix using a binary collision approximation (BCA) model; and in response to the real-time energy being less than or equal to the first energy threshold, selecting a corresponding model from the BCA model and a molecular dynamics (MD) model to simulate an implantation process of the ion, wherein the MD model is used to simulate the motion trajectory of the ion in the semiconductor material matrix. 19.A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the method according to any one of claims 1 to 17.

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