Alpha particle energy deposition calculation method based on monte carlo particle transport simulation tool

Abstract

The application relates to the technical field of particle deposition and semiconductor radiation resistance analysis, and discloses an alpha particle energy deposition calculation method based on a Monte Carlo particle transport simulation tool, which sequentially comprises material and particle source setting, use of an energy deposition scorer, statistics of single particle events, calculation of a soft error rate, and output and data analysis. By using the application, the need for field experiments is greatly reduced, and the time and cost required by experiments are saved. Moreover, by using a simulation method, a designer can more efficiently calculate the energy deposition of alpha particles in a semiconductor device and obtain the soft error rate of the alpha particles, and can perform radiation resistance reinforcement design of the semiconductor device according to the data, so that the radiation resistance performance and reliability of the device are improved.

Description

Technical Field

[0001] This application relates to the fields of particle deposition and semiconductor radiation resistance analysis technology, specifically a method for calculating alpha particle energy deposition based on Monte Carlo particle transport simulation tools. Background Technology

[0002] In modern electronic devices and materials science, the study of radiation effects is becoming increasingly important, especially in the aerospace, nuclear, and semiconductor industries. Electronic systems operating in atmospheric environments (aviation, high altitudes, ground environments, etc.) are constantly bombarded by atmospheric neutrons, while the packaging materials of integrated circuits in these systems also continuously release alpha particles. When alpha particles enter semiconductor devices, their energy is deposited inside the device, potentially causing soft errors. Although these soft errors do not lead to physical damage to the device, they can have a significant impact on the reliability of the electronic system, potentially causing system-level failures. Furthermore, with the continuous development of semiconductor technology and the gradual shrinking of transistor sizes, devices are becoming more sensitive to single-event effects, making the effective assessment and reduction of the impact of single-event effects a critical issue.

[0003] Currently, traditional energy deposition measurements rely on physical experiments, which involve constructing specific environments in the laboratory, irradiating devices with high-energy particles, and recording the energy deposition. However, this method is costly and time-consuming. Therefore, numerical simulation calculations have become an efficient and economical solution to replace experiments.

[0004] In addition, some existing technologies employ intelligent models to analyze particle transport and energy deposition. For example, Chinese Patent Application No. CN202210087323.3 discloses a neural network-based method for predicting proton energy deposition in devices. This technology establishes a database reflecting the characteristics of the target system, builds and optimizes a BP neural network, and makes predictions using the determined optimal network model and the prediction dataset. Theoretically, through model construction and automatic matching and analysis, it is possible to evaluate the radiation resistance of semiconductor devices in a particle environment. However, limited by the sample data required during model construction, it cannot be guaranteed that the network model can completely simulate the trajectory of particles in the environment. This results in significant uncertainty in the evaluation of the radiation resistance of semiconductors in a radiation environment, and cannot provide reliable analytical results for the research and application of advanced technologies. Therefore, there is an urgent need for a reliable technology to evaluate the radiation resistance of devices under different radiation environments. Summary of the Invention

[0005] The purpose of this application is to provide a method for calculating alpha particle energy deposition based on Monte Carlo particle transport simulation tools, so as to achieve rapid and accurate evaluation of the performance of semiconductor devices under radiation environment, and to predict the soft error rate of devices without relying on physical experiments, thus providing important data support for semiconductor device design and radiation hardening.

[0006] To achieve the above objectives, this application discloses the following technical solution: a method for calculating alpha particle energy deposition based on a Monte Carlo particle transport simulation tool, comprising the following steps:

[0007] Materials and particle source settings include: defining the geometric and physical parameters of the semiconductor device in the materials section of the Monte Carlo particle transport simulation tool, and defining the alpha particle source in the source section;

[0008] The use of an energy deposition scorer includes: using a deposition plate in a Monte Carlo particle transport simulation tool to monitor energy deposition within a semiconductor device, wherein the deposition plate is set with a grid, and the scorer counts and collects the energy deposition of alpha particles at each position in the entire grid during the simulation to obtain energy deposition data;

[0009] The statistics of single-particle events include: setting a critical energy threshold for soft errors in the Monte Carlo particle transport simulation tool, and recording a single-particle event as when the energy deposited by an alpha particle in an event reaches or exceeds the critical energy threshold;

[0010] Calculate the soft error rate, including: obtaining the soft error rate during the simulation process based on single-event events;

[0011] Output and data analysis include: after the simulation is completed, outputting the energy deposition data of each particle event at different locations in the semiconductor device, and the soft error risk of the semiconductor device under different environments.

[0012] Preferably, the geometric parameters include at least thickness, and the physical parameters include at least density, composition, and number of atoms.

[0013] Preferably, defining the alpha particle source in the source portion specifically includes:

[0014] Set the energy distribution, emission direction, and emission location of the alpha particle source.

[0015] Preferably, the setting of the energy distribution of the alpha particle source includes:

[0016] The alpha particle energy of the radioactive element in the packaging material is set as either a discrete energy distribution or a continuous energy distribution.

[0017] Preferably, the definition of the alpha particle source in the source portion further includes:

[0018] The angular distribution of alpha particle incident particles can be controlled by setting the geometry of the source and the emission direction.

[0019] Preferably, the mesh includes a regular xyz mesh or a mesh of various shapes divided in three-dimensional space.

[0020] Preferably, the scoring system is configured as follows:

[0021] Select the unit format corresponding to the energy deposition data according to the requirements. The unit format shall include at least: energy absorbed by a unit mass of material, energy absorbed by a unit volume of material, and ionizing radiation energy absorbed by a unit mass of material.

[0022] Preferably, the soft error rate includes the probability of a soft error occurring, which is calculated using the following formula:

[0023]

[0024] Among them, AMT OCC AMT records the cumulative number of events that reach or exceed the critical energy threshold. SUM This represents the total number of events.

[0025] Preferably, the soft error rate also includes the actual soft error rate, which is calculated using the following formula:

[0026] SER ACT =SER SIM ×φ α

[0027] Where, φ α This represents the alpha particle flux per unit area.

[0028] Preferably, the output and data analysis further includes generating reports and data charts from the energy deposition data and the soft error risk.

[0029] Technical Benefits: The alpha particle energy deposition calculation method based on Monte Carlo particle transport simulation tools proposed in this application significantly reduces the need for in-situ experiments, saving time and costs. Furthermore, through simulation, designers can more efficiently calculate the energy deposition of alpha particles in semiconductor devices and obtain the soft error rate of alpha particles. This data can be used for radiation hardening design of semiconductor devices, thereby improving the radiation resistance and reliability of the devices. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating the alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool provided in this application embodiment. Detailed Implementation

[0032] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0033] In this document, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0034] The Monte Carlo particle transport simulation system provides accurate simulation capabilities for particle transport and energy deposition, handling various particle types including neutrons, protons, and alpha particles. It supports a wide range of physical processes and material models, and is widely used in radiation protection, accelerator technology, and space radiation. Using the Monte Carlo particle transport simulation system, the trajectory and energy deposition process of alpha particles in semiconductor devices can be simulated, thereby evaluating the radiation resistance of devices under different radiation environments.

[0035] The nouns involved are:

[0036] Single event effect (SEE): The single event effect is a phenomenon in which a single radiating particle enters the sensitive area of ​​a semiconductor device, and the large number of electron-hole pairs generated on the particle's track are collected by the node, thereby generating a pulse current and causing abnormal device state.

[0037] Soft error: refers to a flip or transient pulse in a semiconductor device caused by a single-event event, which does not cause permanent damage to the device;

[0038] Soft Error Rate: The frequency of soft errors caused by radiation under a specific radiation environment;

[0039] Energy Deposition: The energy deposited by the interaction of alpha particles in a semiconductor device, usually expressed as energy per unit volume.

[0040] Based on this, this embodiment discloses a method such as Figure 1 The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool, as shown, includes the following steps in sequence:

[0041] S1 - Material and Particle Source Setup, including: defining the geometric and physical parameters of the semiconductor device in the Monte Carlo particle transport simulation tool through the material section, and defining the alpha particle source in the source section. Geometric parameters must include at least thickness, and physical parameters must include at least density, composition, and number of atoms. The density, thickness, composition, and number of atoms of various materials are important factors affecting energy deposition. When defining the alpha particle source, its energy distribution, direction (setting theta = 0, 90 restricting the emission angle between 0° and 90°), and emission position (e.g., setting a point source at the origin when x = 0, y = 0, z = 0) are set. For the alpha particle energy of radioactive elements in the packaging material, a discrete or continuous energy distribution can be set (e-type=2 for continuous energy distribution, e-type=3 for discrete energy distribution, e-type=11 for Gaussian distribution, e-type=12 for Maxwell distribution; for example, emin=10.0 and emax=14.0 set the alpha particle energy range to 10MeV to 14MeV) to simulate alpha particles at different energy levels in the environment. Furthermore, the angular distribution of alpha particle incidence can be controlled by setting the source geometry (e.g., s-type=1 for a point source, s-type=3 for a rectangular surface source, s-type=4 for a circular surface source, etc.) and emission direction, making the simulation more realistic.

[0042] The use of the S2-energy deposition scorer includes: monitoring energy deposition within a semiconductor device using a deposition grid within a Monte Carlo particle transport simulation tool. This deposition grid contains a grid, and the scorer statistically analyzes and collects the energy deposition of alpha particles at each location within the grid during the simulation, obtaining energy deposition data. To obtain accurate deposition data, a suitable scoring grid can be selected, such as a regular xyz grid or various shaped mesh grids in three-dimensional space. The mesh grid can be of various shapes, such as rz or tet formats, suitable for complex geometries, ensuring that sensitive areas within the semiconductor device are recorded.

[0043] Furthermore, the scorer configuration allows for the acquisition of alpha particle energy deposition data, with the ability to select different unit formats as needed, such as energy absorbed per unit mass of material (MeV / g), energy absorbed per unit volume of material (MeV / cm3), and ionizing radiation energy absorbed per unit mass of material (Gy). For example, the energy deposition per unit volume can be selected, and the energy density can be calculated by dividing the total deposited energy by the volume. Simultaneously, output can be selected based on individual events (single-particle events) or cumulative output to meet different analytical requirements.

[0044] S3 single-event statistics include: setting a critical energy threshold for soft errors in the Monte Carlo particle transport simulation tool. When the energy deposited by an alpha particle in an event reaches or exceeds this critical energy threshold, it is recorded as a single-event event. The critical energy threshold is a threshold set in the analysis to determine the soft error rate. When the energy deposited by an alpha particle in an event reaches or exceeds this threshold, a single-event event (such as a single-event flip or soft error) is considered to have occurred. This threshold can be selected based on the specific device, typically the energy corresponding to the minimum charge required for the device to flip.

[0045] S4 – Calculates the soft error rate, including obtaining the soft error rate during the simulation process based on single-event events. Specifically, it uses the formula... The probability of soft error occurrence is calculated, where AMT OCC AMT records the cumulative number of events that reach or exceed the critical energy threshold. SUM The probability of a soft error can be calculated by dividing the cumulative number of events that reach the critical threshold by the total number of events.

[0046] To further derive the actual soft error rate in a specific radiation environment, the formula SER can be used. ACT =SER SIM ×φ α The actual soft error rate is calculated, where φ α The alpha particle flux per unit area is the actual soft error rate, which is obtained by multiplying the probability of soft errors by the particle flux.

[0047] S5 output and data analysis include: after the simulation is complete, outputting energy deposition data for each particle event at different locations in the semiconductor device; generating energy deposition distribution maps of alpha particles at different locations in the semiconductor device based on the output energy deposition data; analyzing energy deposition under different conditions based on the data in the output file; and outputting the soft error risk of the semiconductor device under different environments by calculating the soft error probability and actual soft error rate. Finally, detailed reports and data charts can be generated to provide a reference for the radiation-hardened design of semiconductor devices.

[0048] Finally, it should be noted that the above are merely preferred embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for alpha particle energy deposition calculation based on a Monte Carlo particle transport simulation tool, characterized in that, The method comprises the following steps in sequence: Material and particle source setting, comprising: defining the geometric parameters and physical parameters of a semiconductor device in a Monte Carlo particle transport simulation tool through a material section, and defining an alpha particle source in a source section; Energy deposition scorer usage, comprising: monitoring the energy deposition in the semiconductor device by using a deposition plate in the Monte Carlo particle transport simulation tool, wherein a grid is arranged in the deposition plate, and the energy deposition data is obtained by counting and collecting the energy deposition of the alpha particles at each position in the entire grid during the simulation process through a scorer; Single particle event statistics, comprising: setting a critical energy threshold of soft error in the Monte Carlo particle transport simulation tool, and recording a single particle event when the energy deposited by the alpha particles in an event reaches or exceeds the critical energy threshold; Soft error rate calculation, comprising: obtaining the soft error rate in the simulation process based on the single particle events; Output and data analysis, comprising: outputting the energy deposition data of each particle event at different positions in the semiconductor device and the corresponding soft error risk of the semiconductor device in different environments after the simulation process is completed; The soft error rate comprises a soft error occurrence probability, and the soft error occurrence probability is calculated by the following formula: wherein, the number of events for which the accumulation of records reaches or exceeds the critical energy threshold, is the total number of events; The soft error rate further comprises an actual soft error rate, and the actual soft error rate is calculated by the following formula: wherein, is the alpha particle flux per unit area.

2. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 1, wherein, The geometric parameters at least comprise thickness, and the physical parameters at least comprise density, composition and atomic number.

3. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 1, wherein, The alpha particle source is defined in the source section, and specifically comprises: Setting the energy distribution, emission direction and emission position of the alpha particle source.

4. The method of claim 3, wherein the Monte Carlo particle transport simulation tool is MCNP. The setting of the energy distribution of the alpha particle source comprises: For the alpha particle energy of the radioactive element in the packaging material, a discrete energy distribution or a continuous energy distribution is set.

5. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 3, wherein, The alpha particle source is defined in the source section, and specifically comprises: By setting the geometric shape and emission direction of the source, the angular distribution of the incident alpha particles is controlled.

6. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 1, wherein, The grid comprises a regular xyz grid or a mesh grid with various shapes divided in a three-dimensional space.

7. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 1, wherein, The configuration of the scorer comprises: According to the requirement, the unit format corresponding to the energy deposition data is selected, and the unit format at least comprises the energy absorbed by unit mass of matter, the energy absorbed by unit volume of matter and the ionizing radiation energy absorbed by unit mass of matter.

8. The Monte Carlo particle transport simulation tool based alpha particle energy deposition calculation method of claim 1, wherein, The output and data analysis further comprises: generating a report and a data chart of the energy deposition data and the soft error risk.

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