Alpha particle energy deposition calculation method based on Monte Carlo particle transport simulation tool

Abstract

The invention relates to the technical field of particle deposition and semiconductor anti-radiation analysis, and discloses an alpha particle energy deposition calculation method based on a Monte Carlo particle transport simulation tool, which sequentially comprises the steps of material and particle source setting, energy deposition score indicator use, single particle event statistics, soft error rate calculation and output and data analysis. By adopting the method and the device, the requirements of field experiments are greatly reduced, and the time and the cost required by the experiments are saved. Moreover, through the simulation method, designers can more efficiently calculate the energy deposition of the alpha particles in the semiconductor device and obtain the soft error rate of the alpha particles, and radiation hardening design of the semiconductor device can be carried out according to the data, so that the radiation resistance and reliability of the device are improved.

Description

Technical Field

[0001] The present application relates to the technical field of particle deposition and semiconductor radiation resistance analysis, and specifically to an alpha particle energy deposition calculation method based on a Monte Carlo particle transport simulation tool. Background Art

[0002] In modern electronic devices and materials science, the study of radiation effects is becoming increasingly important, especially in the aerospace, nuclear industry, and semiconductor industry. Electronic systems working in atmospheric environments (aviation, high altitude, ground environment, etc.) are constantly bombarded by atmospheric neutrons, and the packaging materials of integrated circuits in the system are also constantly releasing alpha particles. When alpha particles enter semiconductor devices, their energy is deposited inside the device, which may cause soft errors. Although these soft errors will not cause physical damage to the device, they may have a great impact on the reliability of the electronic system, and soft errors may cause system-level failures. In addition, with the continuous development of semiconductor technology, the size of transistors has gradually decreased, and devices have become more sensitive to single-particle effects, which makes how to effectively evaluate and reduce the impact of single-particle effects a key issue.

[0003] At present, traditional energy deposition measurement relies on physical experiments, which involves building a specific environment in the laboratory, irradiating high-energy particles onto the device and recording its energy deposition. However, this method is costly and time-consuming, so replacing experiments with numerical simulation calculations has become an efficient and economical solution.

[0004] In addition, there are some technologies in the prior art that use intelligent models to analyze particle transport and energy deposition, such as a neural network-based proton energy deposition prediction method in a device disclosed in a Chinese patent with application number CN202210087323.3. This technology, by establishing a database of the characteristics of the target system, builds a BP neural network and optimizes the training, and predicts by determining the optimal network model and the predicted data set. In theory, through the construction, automatic matching and analysis of the model, it is possible to evaluate the radiation resistance of semiconductor devices in a particle environment. However, due to the limitations of the sample data required in the model construction process, it is impossible to ensure whether the network model can completely simulate the trajectory of particles in the environment, which will cause a large uncertainty in the evaluation of the radiation resistance of semiconductors in radiation environments. For the research and development and application of high-precision technologies, reliable analysis results cannot be given. Therefore, there is an urgent need for a technology that can guarantee the reliability of evaluating the radiation resistance of devices in different radiation environments. Summary of the invention

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

[0006] To achieve the above purpose, the present 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 in sequence:

[0007] Material and particle source settings, including: defining geometric parameters and physical parameters of semiconductor devices through the material part in the Monte Carlo particle transport simulation tool, and defining alpha particle sources in the source part;

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

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

[0010] Calculating the soft error rate, including: obtaining the soft error rate in the simulation process based on the single particle event;

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

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

[0013] Preferably, the alpha particle source is defined in the source part, specifically including:

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

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

[0016] For the alpha particle energy of the radioactive elements in the packaging material, it is set to a discrete energy distribution or a continuous energy distribution.

[0017] Preferably, the alpha particle source is defined in the source part, further comprising:

[0018] Control the angular distribution of alpha particle incidence by setting the source geometry and emission direction.

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

[0020] Preferably, the configuration of the scorer includes:

[0021] The unit format corresponding to the energy deposition data is selected according to the requirements, and the unit format at least includes: energy absorbed by unit mass of matter, energy absorbed by unit volume of matter, and ionizing radiation energy absorbed by unit mass of matter.

[0022] Preferably, the soft error rate includes a soft error occurrence probability, and the soft error occurrence probability is calculated by the following formula:

[0023]

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

[0025] Preferably, the soft error rate also includes an actual soft error rate, and the actual soft error rate is calculated by the following formula:

[0026] SER ACT =SER SIM ×φ α

[0027] Among them, φ α is the alpha particle flux per unit area.

[0028] Preferably, the output and data analysis further comprises: generating reports and data charts based on the energy deposition data and the soft error risk.

[0029] Technical effect: The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool of the present application greatly reduces the need for field experiments and saves the time and cost required for experiments. In addition, through the simulation method, designers can more efficiently calculate the energy deposition of alpha particles in semiconductor devices and obtain the soft error rate of alpha particles. Based on this data, the radiation hardening design of semiconductor devices can be carried out, thereby improving the radiation resistance and reliability of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 A schematic flow chart of a method for calculating alpha particle energy deposition based on a Monte Carlo particle transport simulation tool provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0033] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0034] The Monte Carlo particle transport simulation system provides accurate particle transport and energy deposition simulation functions, can handle a variety of particle types, including neutrons, protons, alpha particles, etc., and supports a variety of physical processes and material models. It is widely used in radiation protection, accelerator technology, space radiation and other fields. Through the Monte Carlo particle transport simulation system, the trajectory and energy deposition process of alpha particles in semiconductor devices can be simulated, so as to evaluate the radiation resistance of devices in different radiation environments.

[0035] The nouns involved are:

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

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

[0038] Soft Error Rate: The frequency of soft errors caused by radiation in 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 as follows Figure 1 The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool shown includes the following steps in sequence:

[0041] S1-Material and particle source settings, including: defining the geometric parameters and physical parameters of semiconductor devices through the material part in the Monte Carlo particle transport simulation tool, and defining the alpha particle source in the source part. The geometric parameters include at least thickness, and the physical parameters include at least density, composition and atomic number. The density, thickness, composition, atomic number, etc. of various materials are important factors affecting energy deposition. When defining the alpha particle source, set its energy distribution, direction (set theta = 0, 90 to limit the emission angle between 0°-90°) and emission position (for example, x = 0, y = 0, z = 0 is to set a point source at the origin of the coordinate system). For the alpha particle energy of the radioactive elements in the packaging material, a discrete energy distribution or a continuous energy distribution can be set (e-type = 2 is a continuous energy distribution, e-type = 3 is a discrete energy distribution, e-type = 11 is a Gaussian distribution, and e-type = 12 is a Maxwell distribution; for example, emin = 10.0 and emax = 14.0 set the energy range of the alpha particles to 10MeV to 14MeV) to simulate alpha particles of different energy levels in the environment. In addition, the angle distribution of the alpha particle incident can be controlled by setting the source geometry (such as s-type = 1 sets a point source, s-type = 3 sets a rectangular surface source, s-type = 4 sets a circular surface source, etc.) and the emission direction, so that the simulation is more in line with the actual situation.

[0042] S2- The use of the energy deposition scorer includes: using the deposition plate in the Monte Carlo particle transport simulation tool to monitor the energy deposition in the semiconductor device, wherein the deposition plate is provided with a grid, and the energy deposition of alpha particles at each position in the entire grid during the simulation is counted and collected by the scorer to obtain energy deposition data. In order to obtain accurate deposition data, a suitable scoring grid can be selected, such as a regular xyz grid divided in three-dimensional space or a mesh grid of various shapes, wherein the mesh grid can be a grid of various shapes, such as rz, tet and other formats, suitable for complex geometric structures, to ensure that sensitive areas in the semiconductor device can be recorded.

[0043] Furthermore, in the counter configuration, the energy deposition data of alpha particles can be obtained, and different unit formats can be selected according to the needs, such as energy absorbed per unit mass of matter (MeV / g), energy absorbed per unit volume of matter (MeV / cm3), and ionizing radiation energy absorbed per unit mass of matter (Gy). For example, the energy deposition per unit volume can be selected, and the energy density can be calculated by dividing the total energy deposited by the volume. At the same time, the output can be selected by recording (single particle event) or cumulative output to meet different analysis needs.

[0044] S3-Single particle event statistics, including: setting the critical energy threshold of soft errors in the Monte Carlo particle transport simulation tool. When the energy deposited by an alpha particle in an event reaches or exceeds the critical energy threshold, it is recorded as a single particle event. For the critical energy threshold, it is for the purpose of analyzing the soft error rate. When the energy deposited by an alpha particle in an event reaches or exceeds this threshold, a single particle event (such as a single particle upset or soft error) is considered to have occurred. This threshold can be selected according to the specific device, usually the energy corresponding to the minimum charge required for the device to flip.

[0045] S4-Calculate the soft error rate, including: obtain the soft error rate in the simulation process based on the single particle event. Specifically, use the formula The probability of soft error is calculated, where AMT OCC AMT is the cumulative number of events that reach or exceed the critical energy threshold. SUM is the total number of events, that is, the probability of soft error occurrence can be calculated by accumulating the number of events that reach the critical threshold and dividing it by the total number of events.

[0046] In order to further derive the actual soft error rate in a specific radiation environment, the formula SER can be used ACT =SER SIM ×φ α Calculate the actual soft error rate, where φ α is the alpha particle flux per unit area, that is, the actual soft error rate can be obtained by multiplying the probability of soft error by the particle flux

[0047] S5-output and data analysis, including: after the simulation process is completed, the energy deposition data of each particle event at different positions in the semiconductor device is output, and the energy deposition distribution diagram of alpha particles at different positions in the semiconductor device is generated through the output energy deposition data. And according to the data in the output file, the energy deposition under different conditions is analyzed, and the corresponding soft error risk of semiconductor devices under different environments is output by calculating the probability of soft error occurrence and the actual soft error rate. Finally, detailed reports and data charts can be generated to provide a reference for the radiation-resistant design of semiconductor devices.

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

Claims

1. A method for calculating alpha particle energy deposition based on a Monte Carlo particle transport simulation tool, characterized in that: The following steps are included in sequence: Material and particle source settings, including: defining geometric parameters and physical parameters of semiconductor devices through the material part in the Monte Carlo particle transport simulation tool, and defining alpha particle sources in the source part; The use of the energy deposition scorer includes: using a deposition plate in a Monte Carlo particle transport simulation tool to monitor energy deposition in a semiconductor device, wherein a grid is provided in the deposition plate, and the energy deposition of alpha particles at each position in the entire grid during the simulation is counted and collected by the scorer to obtain energy deposition data; Statistics of single particle events include: setting a critical energy threshold of soft errors in a Monte Carlo particle transport simulation tool, and recording a single particle event when the energy deposited by an alpha particle in an event reaches or exceeds the critical energy threshold; Calculating the soft error rate, including: obtaining the soft error rate in the simulation process based on the single particle event; Output and data analysis include: after the simulation process is completed, output the energy deposition data of each particle event at different locations in the semiconductor device, and the corresponding soft error risks of the semiconductor device in different environments.

2. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1 is characterized in that: The geometric parameters include at least thickness, and the physical parameters include at least density, composition and atomic number.

3. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1, characterized in that: The alpha particle source is defined in the source part, specifically including: Set the energy distribution, emission direction and emission position of the alpha particle source.

4. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 3 is characterized in that: The setting of the energy distribution of the alpha particle source comprises: For the alpha particle energy of the radioactive elements in the packaging material, it is set to a discrete energy distribution or a continuous energy distribution.

5. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 3, characterized in that: The method of defining an alpha particle source in a source part further comprises: Control the angular distribution of alpha particle incidence by setting the source geometry and emission direction.

6. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1, characterized in that: The grid includes a regular xyz grid divided in three-dimensional space or mesh grids of various shapes.

7. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1, characterized in that: The configuration of the scorer includes: The unit format corresponding to the energy deposition data is selected according to the requirements, and the unit format at least includes: energy absorbed by unit mass of matter, energy absorbed by unit volume of matter, and ionizing radiation energy absorbed by unit mass of matter.

8. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1, characterized in that: The soft error rate includes the probability of soft error occurrence, which is calculated by the following formula: Among them, AMT OCC AMT is the cumulative number of events that reach or exceed the critical energy threshold. SUM is the total number of events.

9. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 8, characterized in that: The soft error rate also includes an actual soft error rate, and the actual soft error rate is calculated by the following formula: CHEESE ACT =CHEESE SIM ×φ α Among them, φ α is the alpha particle flux per unit area.

10. The alpha particle energy deposition calculation method based on the Monte Carlo particle transport simulation tool according to claim 1, characterized in that: The output and data analysis also includes: generating reports and data charts based on the energy deposition data and the soft error risk.

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