A method for fabricating integrated circuit transistors with gas purification function

Abstract

This invention discloses an integrated circuit transistor fabrication method with gas purification function, belonging to the field of circuit transistor fabrication technology. It realizes accurate modeling, dynamic control and optimized execution of gas flow behavior and ion migration trend during the doping process. By constructing a three-dimensional gas turbulence simulation model in the spatial coordinate system of the processing cavity, combined with the set of turbulence optimization control factors generated by the structural parameters of the processing cavity, and a feedback adaptation mechanism based on the doping uniformity target standard, the closed-loop output and real-time adjustment of the optimal gas control strategy are realized, so that the doping injection process has a high degree of self-adaptation and turbulence guidance capability. Finally, by monitoring the changes in the concentration of pollutants and the stability index of the gas flow field, and with the acquisition and analysis of the final ion concentration distribution results, not only is the doping uniformity and spatial concentration stability improved, but also the uncontrollable diffusion of local pollutant atmosphere accumulation and gas turbulence path is significantly suppressed.

Description

Technical Field

[0001] This invention relates to the field of circuit transistor processing technology, specifically to a method for processing integrated circuit transistors with gas purification function. Background Technology

[0002] Currently, microelectronics manufacturing technology is in a stage of highly precise and nanoscale development, relying on semiconductor processing technology as one of its core technological areas. Within this technology system, integrated circuit manufacturing is a key branch, encompassing multiple complex process steps from wafer cleaning, photolithography, and ion implantation to metal interconnection. In the integrated circuit manufacturing process, transistors, as the basic building blocks, directly determine the efficiency and yield of the final chip through their performance stability and structural integrity. Specifically, in transistor manufacturing, doping is one of the core processes for controlling key parameters such as threshold voltage, conductivity type, and mobility.

[0003] Traditional doping process control schemes, despite employing high-precision mass flow controllers and rotating wafer support structures to attempt uniform implantation, still rely on static process window settings and the assumption of equal-flux implantation. They lack effective responses to dynamic disturbances, such as gas backflow, corner stagnation zones, and critical reaction accumulation factors. Furthermore, if the gas flow path deviates or becomes turbulent, without an effective purification mechanism, it can easily lead to both localized atmosphere contamination and doping deviation. For example, impurity gases or reaction byproducts often accumulate in edge regions and are difficult to expel with the mainstream gas, creating spatial "dead zone" contamination. This type of contamination will affect subsequent film adhesion and diffusion behavior through interfacial reactions, causing defect propagation and abnormal interfacial migration.

[0004] More importantly, most existing flow field optimization schemes only address temperature uniformity control within heat conduction models, lacking the ability to establish a coupled feedback model between gas disturbance behavior and dopant particle distribution. This "heat-gas separation control" approach prevents flow field simulation results from accurately mapping to the control requirements of ion implantation uniformity, thus offering very limited improvement in doping uniformity across the entire wafer. Currently, there is a lack of simulation-driven, dynamically adaptive flow field optimization strategies to simultaneously address the issues of contaminant gas migration and effective ion distribution control. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for fabricating integrated circuit transistors with gas purification functions, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for fabricating an integrated circuit transistor with gas purification function, comprising the following steps:

[0007] S1. By deploying micro-turbulence detection devices and gas flow velocity sensing devices, the gas velocity distribution, local turbulence characteristics, and initial ion concentration distribution data of the doping and implantation region within the circuit transistor processing area are collected to construct the original gas flow field characteristic information set.

[0008] S2. Based on the gas flow field feature information set, a three-dimensional gas turbulence simulation model is constructed in the circuit transistor processing cavity space. The gas flow path and ion concentration migration trend during the doping process are simulated, and the gas distribution and concentration coupling prediction results during the doping process are output.

[0009] S3. Based on the coupling prediction results, identify the turbulence generation region, high concentration accumulation region and insufficient doping region in the gas flow field, and combine them with the processing cavity structure parameters to adjust the gas injection angle and gas jet speed to form a set of turbulence optimization control factors.

[0010] S4. Input the set of turbulence optimization control factors into the feedback control module, compare it with the preset doping uniformity target standard, dynamically adjust the gas injection behavior, realize the real-time correction of the gas path during the doping process, and output the current optimal gas control strategy.

[0011] S5. Perform the doping injection circuit transistor processing step according to the current optimal gas control strategy, and at the same time monitor the changes in the concentration of pollutant gas in the circuit transistor processing area and record the stability index of the gas flow field.

[0012] S6. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, the concentration deviation evaluation value is output, and iterative optimization is performed based on the concentration deviation evaluation value.

[0013] Preferably, S1 includes S11 and S12;

[0014] S11. During the processing of circuit transistors, a micro-turbulence detection device and a gas flow velocity sensing device are deployed inside the processing cavity based on the spatial coordinate system of the processing cavity to collect two types of gas behavior data: gas velocity distribution information and local turbulence characteristic information, respectively.

[0015] The gas velocity distribution information represents the magnitude and direction of the gas flow velocity at each coordinate point in the processing cavity spatial coordinate system per unit time, including the mainstream velocity, local reverse velocity, and velocity fluctuation rate.

[0016] Local turbulence characteristic information is used to describe the turbulence, swirling and turbulent phenomena of gas at each coordinate point in the processing cavity spatial coordinate system caused by cavity structure, equipment vibration or external disturbance, including turbulence frequency, vortex formation rate, turbulence boundary position and duration, and is synchronously collected with the gas velocity distribution information and bound to the spatial coordinates in the processing cavity spatial coordinate system.

[0017] Preferably, in step S12, an ion concentration sensing device is installed in the target doping injection area within the processing cavity, and coordinate calibration is performed according to the spatial coordinate system of the processing cavity. The ion concentration in the initial stage of doping is detected in situ to form an initial doping concentration distribution map. The in situ distribution detection includes the detection of initial ion concentration values, concentration gradients, and diffusion trend information. By integrating the gas velocity distribution information, the local turbulence characteristic information, and the initial doping concentration distribution map, an original gas flow field characteristic information set is constructed.

[0018] The initial ion concentration value represents the initial ion concentration value of each doped region point, including the number of doped particles per unit volume, used to distinguish the concentration ratio of different implanted particle types, and to record the position of each doped region point in the cavity space coordinate system.

[0019] The detection of diffusion trend information is used to determine the ion concentration gradient distribution and its corresponding spatial diffusion direction between coordinate points in the processing cavity spatial coordinate system of the doped implantation region.

[0020] Preferably, S2 includes S21 and S22;

[0021] S21. Based on the gas flow field feature information set as the basic input for simulation modeling, a three-dimensional turbulence simulation model covering the entire circuit transistor processing cavity region is constructed according to the processing cavity spatial coordinate system. The three-dimensional turbulence simulation model takes the gas velocity distribution information and the local turbulence feature information in the gas flow field feature information set as the core input. Under the set cavity structure boundary conditions, combined with the gas viscosity and thermodynamic response characteristics and process-related parameters, the dynamic evolution model of the turbulence behavior at each coordinate point in the processing cavity spatial coordinate system is completed.

[0022] Preferably, in step S22, based on the three-dimensional turbulence simulation model, the initial doping concentration distribution map in the gas flow field feature information set is called and spatially aligned under the same reference in the processing cavity spatial coordinate system. By establishing a flow coupling function between the turbulence path and the ion diffusion direction, the evolution process of ion concentration at each coordinate point in the processing cavity spatial coordinate system with time and its corresponding spatial migration path trend are analyzed during the doping process. The analysis is complete through steps S221, S222 and S223.

[0023] S221. Analysis task begins: At each doping injection point in the spatial coordinate system of the processing cavity, establish a prediction function for the direction of ion movement driven by turbulence.

[0024] S222, Analysis Task Execution: Considering the linkage effect between ion particle characteristics and turbulence intensity, simulate the dynamic evolution of ion concentration distribution throughout the entire doping period.

[0025] S223. Analysis task complete: Output gas distribution and concentration coupling prediction results, including dynamic distribution map of ion concentration within the doping period and gas turbulence path and concentration coupling prediction results;

[0026] The dynamic distribution map of ion concentration within the doping period is used to describe the spatial isohyet distribution of ion concentration, the concentration trend along the coordinate axis, and the evolution of the concentration gradient over time within multiple time periods.

[0027] The gas turbulence path and concentration coupling prediction results are used to reflect the mutual influence between the turbulence structure and the ion migration path.

[0028] Preferably, S3 includes S31 and S32;

[0029] S31. Based on the dynamic distribution map of ion concentration within the doping period and the prediction results of gas turbulence path and concentration coupling, spatial mapping analysis is performed under the constraint of the processing cavity spatial coordinate system to identify key regions affecting uniformity and purification efficiency during the doping process. The key regions include turbulence generation regions, high concentration accumulation regions, and insufficient doping regions. The key regions constitute a key region positioning result set.

[0030] The turbulence generation region: Based on the spatial distribution of the rate of change of turbulence velocity and turbulence characteristic quantities in the gas turbulence path and concentration coupling prediction results, the region in the processing cavity spatial coordinate system where the airflow turbulence intensity exceeds the threshold is identified.

[0031] The high-concentration accumulation region: Based on the temporal concentration superposition trend in the dynamic distribution spectrum of ion concentration within the doping period, the region where ions are enriched and accumulated at certain spatial points is identified.

[0032] The insufficient doping region: By comparing the dynamic distribution spectrum of ion concentration within the doping period with the target concentration benchmark, the spatial range in which the ion concentration is consistently below the threshold is identified.

[0033] Preferably, in step S32, based on the key area positioning result set and combined with the physical structure parameters of the processing cavity and the gas injection interface layout, a turbulence control optimization task is performed to form an implementable turbulence control factor set, which is constructed and obtained through steps S321, S322 and S323.

[0034] S321. Generation of injection angle correction parameters: Based on the main gas turbulence direction and regional impact path indicated in the gas turbulence path and concentration coupling prediction results, the injection angle of the gas injection device is locally corrected. The deflection angle that the gas injection direction needs to be adjusted relative to the standard gas injection direction is obtained through the local correction and defined as the injection angle correction parameter.

[0035] S322. Generation of injection speed adjustment parameters: Combining the regional concentration change trend in the dynamic distribution spectrum of ion concentration within the doping period, matching the required gas injection flow rate compensation rate for the local area, and obtaining the injection speed adjustment parameters by adding the gas injection flow rate compensation rate to the base velocity of the injection flow rate.

[0036] S323, Output of turbulence control factor set: The air injection angle correction parameter and the injection speed adjustment parameter are respectively formatted and organized according to the spatial coordinate points of the processing cavity to form a structured turbulence control factor set for output.

[0037] Preferably, S4 includes S41;

[0038] S41. Based on the set of disturbance control factors as input, the data is imported into the feedback control module. In the spatial coordinate system of the processing cavity, the applicability of the set of disturbance control factors is evaluated and judged. The deviation evaluation and judgment process is based on the doping uniformity target standard preset in the system. The doping uniformity target standard includes the allowable spatial standard deviation range of ion concentration in the doping region, the allowable concentration deviation limit of each coordinate point, and the upper limit of the fitting error between the ion migration path and the theoretical path.

[0039] The deviation assessment and judgment process is performed through steps S411, S412 and S413.

[0040] S411. Comparison of simulation control parameter results with target standard: In the spatial coordinate system of the processing cavity, call the dynamic distribution map of ion concentration within the doping period and the gas turbulence path and concentration coupling prediction results, rerun the simulation process, take the gas injection angle correction parameter and the injection speed adjustment parameter as disturbance input, and calculate the ion concentration redistribution result after the effect of the turbulence optimization control factor.

[0041] S412. Target Deviation Calculation and Spatial Determination: The ion concentration redistribution result is compared point by point with the doping uniformity target standard. The concentration deviation value at each spatial coordinate point of the processing cavity is calculated, and the corresponding adaptation state is output according to the concentration deviation value. The state level is defined as follows:

[0042] When the concentration deviation value is within the preset operating deviation range, the normal adaptation status is output.

[0043] When the concentration deviation value exceeds the preset operating deviation range, an incompatible state is output.

[0044] S413. Evaluation Output: Integrate the degree deviation value, the adaptation state, and the set of turbulence control factors to obtain the turbulence control factor adaptation state result set as the optimal gas control strategy output for integrated circuit transistor processing.

[0045] Preferably, S5 includes S51;

[0046] S51. The injection angle correction parameters and injection speed adjustment parameters in the current optimal gas control strategy are applied to the doping injection device, and the doping injection operation is performed according to the spatial coordinate system of the processing chamber. During the doping process, the injection direction and injection speed are dynamically adjusted according to the control parameters corresponding to each spatial coordinate point to realize real-time control of the ion migration path and spatial guidance of the turbulence intensity. At the same time, the pollutant gas concentration detection module and the airflow stability sensing module deployed in the spatial coordinate system of the processing chamber synchronously collect: information on the change of pollutant gas concentration in the processing area, gas flow field fluctuation intensity, and turbulence cycle and change data. After the doping process is completed, the ion concentration measurement value of the doped region in the transistor structure is obtained through the ion concentration detection module, identified and reconstructed according to the coordinates of the spatial coordinate system of the processing chamber, and integrated to obtain the final ion concentration distribution result.

[0047] The final ion concentration distribution result includes a concentration value matrix, a concentration distribution continuity index set, and a concentration target deviation vector set;

[0048] The concentration value matrix represents the actual ion concentration value corresponding to each spatial coordinate point of the processing cavity.

[0049] The concentration distribution continuity index set uses the processing cavity spatial coordinate system as a reference system. Based on the difference between adjacent points in the concentration value matrix, it extracts the smoothness of the concentration change in the three directions of horizontal axis, vertical axis and vertical axis, which is used to quantitatively characterize the uniformity of doping concentration distribution in spatial dimension.

[0050] The concentration target deviation vector set calculates the deviation between the actual concentration value and the preset target concentration value for each spatial coordinate point of the processing cavity.

[0051] Preferably, S6 includes S61;

[0052] S61. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, and a concentration deviation evaluation value is output. Iterative optimization is performed based on the concentration deviation evaluation value. If the concentration deviation exceeds the set threshold, the three-dimensional turbulence simulation model is retrained. If the concentration deviation is within the threshold range, the optimal gas control strategy described in this round is saved as the process parameter template corresponding to the circuit transistor structure type, and the current optimization process is completed.

[0053] This invention provides a method for fabricating integrated circuit transistors with gas purification function, which has the following beneficial effects:

[0054] (1) Accurate modeling, dynamic control and optimized execution of gas flow behavior and ion migration trend during doping process are achieved. By constructing a three-dimensional gas turbulence simulation model in the spatial coordinate system of the processing cavity and outputting the gas distribution and concentration coupling prediction results during the doping process, the turbulence sensitive area, high concentration accumulation area and insufficient doping area can be predicted before processing. Then, combined with the set of turbulence optimization control factors generated by the processing cavity structure parameters and the feedback adaptation mechanism based on the doping uniformity target standard, the closed-loop output and real-time adjustment of the optimal gas control strategy are realized, so that the doping injection process has a high degree of self-adaptation and turbulence guidance capability. Finally, by monitoring the changes in the concentration of pollutants and the stability index of the gas flow field, and with the acquisition and analysis of the final ion concentration distribution results, not only is the doping uniformity and spatial concentration stability improved, but also the uncontrollable diffusion of local pollutant atmosphere accumulation and gas turbulence path is significantly suppressed. This solves the key problems of poor doping consistency, decreased yield and unstable transistor performance caused by the static gas injection scheme being unable to cope with turbulence fluctuations, doping offset and residual gas enrichment in the existing technology.

[0055] (2) By constructing a three-dimensional turbulence simulation model and establishing a flow coupling function between the turbulence path and the ion diffusion direction based on the spatial coordinate system of the processing cavity, the dynamic distribution spectrum of ion concentration within the doping period and the prediction results of the coupling between gas turbulence path and concentration are output. Compared with the existing technology that only relies on static parameters for process setting, the mathematical relationship between turbulence and ion behavior can be established in real time on the basis of complete gas flow field simulation, thereby improving the understanding and control of ion diffusion behavior under gas turbulence environment, effectively avoiding ion offset diffusion and local concentration imbalance caused by turbulence instability, and having strong spatial adaptability and dynamic scalability, it is particularly suitable for the high consistency doping processing requirements of complex structure transistors.

[0056] (3) By applying the injection angle correction parameter and injection speed adjustment parameter in the optimal gas control strategy to the processing equipment during the actual doping execution stage, and performing coordinate binding control in the spatial coordinate system of the processing cavity, it is possible to realize the dynamic adjustment of the turbulence path and the real-time guidance of the ion migration direction during the doping injection process. The final ion concentration distribution result containing the concentration value matrix, the concentration distribution continuity index set and the concentration target deviation vector set is constructed. Based on this, the difference analysis is performed with the target doping distribution result to form the concentration deviation evaluation value. The deviation evaluation result can trigger the retraining of the three-dimensional turbulence simulation model and can also be used to save the current optimal gas control strategy as the process parameter template corresponding to the circuit transistor structure type. Compared with the existing technology, which lacks a closed-loop verification and dynamic correction mechanism for doping results, a complete feedback closed loop from turbulence control to result evaluation and then to strategy iteration optimization is constructed. This realizes the continuous evolution of doping process quality, the automatic precipitation of process templates and the intelligent iteration of process flow, providing a highly adaptive operation guarantee for stable control in complex doping scenarios. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the steps in a method for fabricating an integrated circuit transistor with gas purification function according to the present invention.

[0058] Figure 2 A schematic diagram illustrating the dynamic evolution of perturbation behavior at each coordinate point in the spatial coordinate system of the machining cavity;

[0059] Figure 3 This is a schematic diagram for analyzing the doping process. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] Example 1

[0062] This invention provides a method for fabricating integrated circuit transistors with gas purification function. Please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

[0063] S1. By deploying micro-turbulence detection devices and gas flow velocity sensing devices, the gas velocity distribution, local turbulence characteristics, and initial ion concentration distribution data of the doping and implantation region within the circuit transistor processing area are collected to construct the original gas flow field characteristic information set.

[0064] S2. Based on the gas flow field feature information set, a three-dimensional gas turbulence simulation model is constructed in the circuit transistor processing cavity space. The gas flow path and ion concentration migration trend during the doping process are simulated, and the gas distribution and concentration coupling prediction results during the doping process are output.

[0065] S3. Based on the coupling prediction results, identify the turbulence generation region, high concentration accumulation region and insufficient doping region in the gas flow field, and combine them with the processing cavity structure parameters to adjust the gas injection angle and gas jet speed to form a set of turbulence optimization control factors.

[0066] S4. Input the set of turbulence optimization control factors into the feedback control module, compare it with the preset doping uniformity target standard, dynamically adjust the gas injection behavior, realize the real-time correction of the gas path during the doping process, and output the current optimal gas control strategy.

[0067] S5. Perform the doping injection circuit transistor processing step according to the current optimal gas control strategy, and at the same time monitor the changes in the concentration of pollutant gas in the circuit transistor processing area and record the stability index of the gas flow field.

[0068] S6. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, the concentration deviation evaluation value is output, and iterative optimization is performed based on the concentration deviation evaluation value.

[0069] In this embodiment, precise modeling, dynamic control, and optimized execution of gas flow behavior and ion migration trends during the doping process are achieved. By constructing a three-dimensional gas turbulence simulation model in the spatial coordinate system of the processing cavity and outputting the coupled prediction results of gas distribution and concentration during the doping process, the turbulence-sensitive area, high-concentration accumulation area, and insufficient doping area can be predicted before processing. Furthermore, by combining the set of turbulence optimization control factors generated by the processing cavity structural parameters and the feedback adaptation mechanism based on the doping uniformity target standard, the closed-loop output and real-time adjustment of the optimal gas control strategy are realized, enabling the doping injection process to have a high degree of self-adaptability and turbulence guidance capability. Finally, by monitoring the changes in contaminant gas concentration and gas flow field stability indicators, and by obtaining and analyzing the final ion concentration distribution results, not only is the doping uniformity and spatial concentration stability improved, but the uncontrollable diffusion of local contaminant atmosphere accumulation and gas turbulence paths is also significantly suppressed. This solves the key problems in the prior art, such as poor doping consistency, decreased yield, and unstable transistor performance caused by the inability of static gas injection schemes to cope with turbulence fluctuations, doping offset, and residual gas enrichment.

[0070] Example 2

[0071] Specifically: S1 includes S11 and S12;

[0072] S11. During the processing of circuit transistors, a micro-turbulence detection device and a gas flow velocity sensing device are deployed inside the processing cavity based on the spatial coordinate system of the processing cavity to collect two types of gas behavior data: gas velocity distribution information and local turbulence characteristic information, respectively.

[0073] The gas velocity distribution information represents the magnitude and direction of the gas flow velocity at each coordinate point in the processing cavity spatial coordinate system per unit time, including the mainstream velocity, local reverse velocity, and velocity fluctuation rate.

[0074] Local turbulence characteristic information is used to describe the turbulence, swirling and turbulence phenomena of gas at each coordinate point in the processing cavity spatial coordinate system caused by cavity structure, equipment vibration or external disturbance, including turbulence frequency, vortex formation rate, turbulence boundary position and duration, and is synchronously collected and bound to the spatial coordinates in the processing cavity spatial coordinate system with the gas velocity distribution information.

[0075] Among them, the mainstream velocity represents the gas velocity in the main injection direction; the local reverse velocity includes the backflow zone; and the velocity fluctuation rate is used to determine the intensity of the disturbance.

[0076] S12. In the target doping injection area within the processing cavity, an ion concentration sensing device is installed, and coordinate calibration is performed according to the spatial coordinate system of the processing cavity. The ion concentration in the initial stage of doping is detected in situ to form an initial doping concentration distribution map. The in situ distribution detection includes the detection of initial ion concentration values, concentration gradients, and diffusion trend information. By integrating the gas velocity distribution information, the local turbulence characteristic information, and the initial doping concentration distribution map, an original gas flow field characteristic information set is constructed.

[0077] The initial ion concentration value represents the initial ion concentration value of each doped region point, including the number of doped particles per unit volume, used to distinguish the concentration ratio of different implanted particle types, and to record the position of each doped region point in the cavity space coordinate system.

[0078] The detection of diffusion trend information is used to determine the ion concentration gradient distribution and its corresponding spatial diffusion direction between coordinate points in the processing cavity spatial coordinate system of the doped implantation region.

[0079] In this embodiment, by simultaneously acquiring gas velocity distribution information, local turbulence characteristic information, and initial doping concentration distribution map in the spatial coordinate system of the processing cavity, this method can comprehensively construct the original gas flow field characteristic information set before doping begins, achieving accurate characterization of gas turbulence state and ion concentration spatial distribution. This provides structured data support for subsequent turbulence simulation modeling and doping path optimization, helping to improve the response capability of the doping process to changes in spatial disturbance, enhance the prediction accuracy and control accuracy of ion distribution, thereby effectively improving problems such as uneven doping, abnormal migration, and contamination accumulation, and improving the consistency and yield of device processing.

[0080] Example 3

[0081] Please see Figure 1 , Figure 2 and Figure 3 Specifically: S2 includes S21 and S22;

[0082] S21. Based on the gas flow field feature information set as the basic input for simulation modeling, a three-dimensional turbulence simulation model covering the entire circuit transistor processing cavity region is constructed according to the processing cavity spatial coordinate system. The three-dimensional turbulence simulation model takes the gas velocity distribution information and the local turbulence feature information in the gas flow field feature information set as the core input. Under the set cavity structure boundary conditions, combined with the gas viscosity and thermodynamic response characteristics and process-related parameters, the dynamic evolution model of the turbulence behavior at each coordinate point in the processing cavity spatial coordinate system is completed.

[0083] The dynamic evolution modeling specifically includes the following modeling steps S211, S212 and S213;

[0084] S211. At each coordinate point in the spatial coordinate system of the processing cavity, reconstruct the corresponding gas velocity vector and turbulence frequency function;

[0085] S212. Simulate the flow path, disturbance boundary, formation frequency and intensity of each disturbance unit along the spatial direction within a standard process cycle;

[0086] S213. Output the turbulence spatial evolution field and its time series data that can be used for doping migration analysis.

[0087] S22. Based on the three-dimensional turbulence simulation model, the initial doping concentration distribution map in the gas flow field feature information set is called and spatially aligned under the same reference in the processing cavity spatial coordinate system. By establishing the flow coupling function between the turbulence path and the ion diffusion direction, the evolution process of ion concentration with time and its corresponding spatial migration path trend at each coordinate point in the processing cavity spatial coordinate system during the doping process are analyzed. The analysis is complete through the analysis steps S221, S222 and S223.

[0088] The flow coupling function refers to a functional expression in the spatial coordinate system of the processing cavity that describes the mutual influence between the gas turbulence path and the diffusion direction of doped ions. The flow coupling function mathematically correlates the local turbulence vector field with the ion migration vector field to simulate the actual distribution evolution trend of ions in space during the doping process.

[0089] S221. Analysis task begins: At each doping injection point in the spatial coordinate system of the processing cavity, establish a prediction function for the direction of ion movement driven by turbulence.

[0090] S222, Execution of analysis task: Considering the linkage effect between ion particle characteristics and turbulence intensity, simulate the dynamic evolution of ion concentration distribution throughout the entire doping period, where the ion particle characteristics include, for example, mass, charge, and mobility.

[0091] S223. Analysis task complete: Output gas distribution and concentration coupling prediction results, including dynamic distribution map of ion concentration within the doping period and gas turbulence path and concentration coupling prediction results;

[0092] The dynamic distribution map of ion concentration within the doping period is used to describe the spatial isohyet distribution of ion concentration, the concentration trend along the coordinate axis, and the evolution of the concentration gradient over time within multiple time periods.

[0093] The gas turbulence path and concentration coupling prediction results are used to reflect the mutual influence between the turbulence structure and the ion migration path.

[0094] In this embodiment, by constructing a three-dimensional turbulence simulation model and establishing a flow coupling function between the turbulence path and the ion diffusion direction based on the spatial coordinate system of the processing cavity, the evolution trend of ion concentration and migration path changes at different spatial points can be accurately predicted before doping. The model outputs a dynamic distribution map of ion concentration within the doping cycle and the prediction results of the gas turbulence path and concentration coupling. Compared to existing technologies that rely solely on static parameters for process settings, this method can establish a mathematical correlation between turbulence and ion behavior in real time based on a complete gas flow field simulation, thereby improving the understanding and control of ion diffusion behavior under gas disturbance conditions. This mechanism not only improves the physical accuracy and process adjustability of the model prediction but also significantly enhances the pre-response capability for doping uniformity adjustment, effectively avoiding ion offset diffusion and local concentration imbalance problems caused by turbulence instability. It has strong spatial adaptability and dynamic scalability, making it particularly suitable for the high-consistency doping processing requirements of complex structure transistors.

[0095] Example 4

[0096] Specifically: S3 includes S31 and S32;

[0097] S31. Based on the dynamic distribution map of ion concentration within the doping period and the prediction results of gas turbulence path and concentration coupling, spatial mapping analysis is performed under the constraint of the processing cavity spatial coordinate system to identify key regions affecting uniformity and purification efficiency during the doping process. The key regions include turbulence generation regions, high concentration accumulation regions, and insufficient doping regions. The key regions constitute a key region positioning result set.

[0098] The turbulence generation region: Based on the spatial distribution of the rate of change of turbulence velocity and turbulence characteristic quantities in the gas turbulence path and concentration coupling prediction results, the region in the processing cavity spatial coordinate system where the airflow turbulence intensity exceeds the threshold is identified.

[0099] The high-concentration accumulation region: Based on the temporal concentration superposition trend in the dynamic distribution spectrum of ion concentration within the doping period, the region where ions are enriched and accumulated at certain spatial points is identified.

[0100] The insufficient doping region: By comparing the dynamic distribution spectrum of ion concentration within the doping period with the target concentration benchmark, the spatial range in which the ion concentration is consistently below the threshold is identified.

[0101] S32. Based on the key area positioning result set and combined with the physical structure parameters of the processing cavity and the gas injection interface layout, perform the turbulence control optimization task to form an implementable turbulence control factor set. The turbulence control factor set is constructed and obtained through steps S321, S322 and S323.

[0102] S321. Generation of gas injection angle correction parameters: Based on the main gas turbulence direction and regional impact path indicated in the gas turbulence path and concentration coupling prediction results, the injection angle of the gas injection device is locally corrected. The deflection angle that the gas injection direction needs to be adjusted relative to the standard gas injection direction is obtained through the local correction and defined as the gas injection angle correction parameter, so that the gas flow direction avoids the turbulence sensitive area or guides the ion migration direction to tend to be balanced.

[0103] S322, Generation of injection speed adjustment parameters: Combining the regional concentration change trend in the dynamic distribution spectrum of ion concentration within the doping period, the required gas injection flow rate compensation rate for the local region is matched, and the injection speed adjustment parameters are obtained by adding the gas injection flow rate compensation rate to the base speed of the injection flow rate, which is used to control the stability in the ion transport path.

[0104] S323, Output of turbulence control factor set: The air injection angle correction parameter and the injection speed adjustment parameter are respectively formatted and organized according to the spatial coordinate points of the processing cavity to form a structured turbulence control factor set for output.

[0105] In this embodiment, spatial mapping analysis is performed based on the dynamic distribution spectrum of ion concentration within the doping cycle and the prediction results of gas turbulence path and concentration coupling. This step can accurately identify turbulence generation regions, high-concentration accumulation regions, and insufficiently doped regions in the spatial coordinate system of the processing cavity, and form a set of key region positioning results, providing clear intervention targets for the control strategy. On this basis, by analyzing the spatial positional relationship and disturbance requirements of each key region, injection angle correction parameters and injection speed adjustment parameters are further generated, and a set of turbulence control factors is output in a structured manner, enabling targeted correction of injection direction and speed at the spatial coordinate level. Compared with the existing process that relies solely on empirical window adjustment for doping unevenness, this method has the advantages of strong spatial adaptability, clear intervention boundaries, and high granularity of control parameters. It can achieve precise regional compensation and dynamic adjustment of turbulence path without relying on structural hardware changes, providing a refined control capability for achieving the dual objectives of doping uniformity and contamination purification in transistor processing.

[0106] Example 5

[0107] Specifically: S4 includes S41;

[0108] S41. Based on the set of disturbance control factors as input, the data is imported into the feedback control module. In the spatial coordinate system of the processing cavity, the applicability of the set of disturbance control factors is evaluated and judged. The deviation evaluation and judgment process is based on the doping uniformity target standard preset in the system. The doping uniformity target standard includes the allowable spatial standard deviation range of ion concentration in the doping region, the allowable concentration deviation limit of each coordinate point, and the upper limit of the fitting error between the ion migration path and the theoretical path.

[0109] The deviation assessment and judgment process is performed through steps S411, S412 and S413.

[0110] S411. Comparison of simulation control parameter results with target standard: In the spatial coordinate system of the processing cavity, call the dynamic distribution map of ion concentration within the doping period and the gas turbulence path and concentration coupling prediction results, rerun the simulation process, take the gas injection angle correction parameter and the injection speed adjustment parameter as disturbance input, and calculate the ion concentration redistribution result after the effect of the turbulence optimization control factor.

[0111] S412. Target Deviation Calculation and Spatial Determination: The ion concentration redistribution result is compared point by point with the doping uniformity target standard. The concentration deviation value at each spatial coordinate point of the processing cavity is calculated, and the corresponding adaptation state is output according to the concentration deviation value. The state level is defined as follows:

[0112] When the concentration deviation value is within the preset operating deviation range, the normal adaptation status is output.

[0113] When the concentration deviation value exceeds the preset operating deviation range, an incompatible state is output.

[0114] S413. Evaluation Output: Integrate the degree deviation value, the adaptation state, and the set of turbulence control factors to obtain the turbulence control factor adaptation state result set as the optimal gas control strategy output for integrated circuit transistor processing.

[0115] In this embodiment, by importing the set of turbulence control factors into the feedback control module, and performing simulation reconstruction and target deviation comparison based on the dynamic distribution map of ion concentration within the doping cycle and the prediction results of gas turbulence path and concentration coupling, the steps can accurately determine the applicability of the injection angle correction parameters and the injection speed adjustment parameters. Relying on the preset doping uniformity target standard, including the spatial standard deviation range of ion concentration, the concentration deviation limit, and the upper limit of the fitting error between the ion migration path and the theoretical path, not only is the point-by-point adaptation verification between the set of turbulence control factors and the target concentration control requirements achieved, but also a turbulence control factor adaptation state result set containing spatial coordinate index, adaptation state label, and control parameter binding relationship is output. Compared with the problems of the inability to quantify the adaptability of control strategies and the difficulty in forming standardized strategy output in the prior art, it has significant advantages such as high response accuracy, visible parameter adaptability, and reusable control strategy, ensuring that the optimal gas control strategy output has process execution value and standard matching capability, and constructing the core logic closed loop of doping path adaptive adjustment.

[0116] Example 5

[0117] Specifically: S5 includes S51;

[0118] S51. The injection angle correction parameters and injection speed adjustment parameters in the current optimal gas control strategy are applied to the doping injection device, and the doping injection operation is performed according to the spatial coordinate system of the processing chamber. During the doping process, the injection direction and injection speed are dynamically adjusted according to the control parameters corresponding to each spatial coordinate point to realize real-time control of the ion migration path and spatial guidance of the turbulence intensity. At the same time, the pollutant gas concentration detection module and the airflow stability sensing module deployed in the spatial coordinate system of the processing chamber synchronously collect: information on the change of pollutant gas concentration in the processing area, gas flow field fluctuation intensity, and turbulence cycle and change data. After the doping process is completed, the ion concentration measurement value of the doped region in the transistor structure is obtained through the ion concentration detection module, identified and reconstructed according to the coordinates of the spatial coordinate system of the processing chamber, and integrated to obtain the final ion concentration distribution result.

[0119] The final ion concentration distribution result includes a concentration value matrix, a concentration distribution continuity index set, and a concentration target deviation vector set;

[0120] The concentration value matrix represents the actual ion concentration value corresponding to each spatial coordinate point of the processing cavity, specifically representing the physical concentration of doped ions within a unit volume area.

[0121] The concentration distribution continuity index set uses the processing cavity spatial coordinate system as a reference system. Based on the difference between adjacent points in the concentration value matrix, it extracts the smoothness of the concentration change in the three directions of horizontal axis, vertical axis and vertical axis, which is used to quantitatively characterize the uniformity of doping concentration distribution in spatial dimension.

[0122] The concentration target deviation vector set calculates the deviation between the actual concentration value and the preset target concentration value for each spatial coordinate point of the processing cavity.

[0123] S6 includes S61;

[0124] S61. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, and a concentration deviation evaluation value is output. Iterative optimization is performed based on the concentration deviation evaluation value. If the concentration deviation exceeds the set threshold, the three-dimensional turbulence simulation model is retrained. If the concentration deviation is within the threshold range, the optimal gas control strategy described in this round is saved as the process parameter template corresponding to the circuit transistor structure type, and the current optimization process is completed.

[0125] In this embodiment, by applying the injection angle correction parameters and injection speed adjustment parameters from the optimal gas control strategy to the processing equipment during the actual doping execution stage, and performing coordinate binding control in the processing cavity spatial coordinate system, dynamic adjustment of the turbulence path and real-time guidance of ion migration direction during the doping injection process can be achieved. During the processing, information on changes in contaminant gas concentration and gas flow field disturbance is collected simultaneously. After doping, the ion concentration value obtained by the ion concentration detection module is used to construct the final ion concentration distribution result, which includes a concentration value matrix, a concentration distribution continuity index set, and a concentration target deviation vector set. Based on this, a difference analysis is performed with the target doping distribution result to form a concentration deviation evaluation value. The deviation evaluation result can trigger the retraining of the three-dimensional turbulence simulation model and can also be used to save the current optimal gas control strategy as a process parameter template corresponding to the circuit transistor structure type. Compared with the existing technology, which lacks a closed-loop verification and dynamic correction mechanism for doping results, this embodiment constructs a complete feedback closed loop from turbulence control to result evaluation and strategy iteration optimization, realizing continuous evolution of doping process quality, automatic precipitation of process templates, and intelligent iteration of the process flow, providing a highly adaptive operational guarantee for stable control in complex doping scenarios.

[0126] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for processing an integrated circuit transistor having a gas cleaning function, characterized by: Includes the following steps: S1. By deploying micro-turbulence detection devices and gas flow velocity sensing devices, the gas velocity distribution, local turbulence characteristics, and initial ion concentration distribution data of the doping and implantation region within the circuit transistor processing area are collected to construct the original gas flow field characteristic information set. S2. Based on the gas flow field feature information set, a three-dimensional gas turbulence simulation model is constructed in the circuit transistor processing cavity space. The gas flow path and ion concentration migration trend during the doping process are simulated, and the gas distribution and concentration coupling prediction results during the doping process are output. S3. Based on the coupling prediction results, identify the turbulence generation region, high concentration accumulation region and insufficient doping region in the gas flow field, and combine them with the processing cavity structure parameters to adjust the gas injection angle and gas jet speed to form a set of turbulence optimization control factors. S4. Input the set of turbulence optimization control factors into the feedback control module, compare it with the preset doping uniformity target standard, dynamically adjust the gas injection behavior, realize the real-time correction of the gas path during the doping process, and output the current optimal gas control strategy. S5. Perform the doping injection circuit transistor processing step according to the current optimal gas control strategy, and at the same time monitor the changes in the concentration of pollutant gas in the circuit transistor processing area and record the stability index of the gas flow field. S6. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, the concentration deviation evaluation value is output, and iterative optimization is performed based on the concentration deviation evaluation value.

2. The integrated circuit transistor processing method with gas cleaning function according to claim 1, characterized in that: S1 includes S11 and S12; S11. During the processing of circuit transistors, a micro-turbulence detection device and a gas flow velocity sensing device are deployed inside the processing cavity based on the spatial coordinate system of the processing cavity to collect two types of gas behavior data: gas velocity distribution information and local turbulence characteristic information, respectively. The gas velocity distribution information represents the magnitude and direction of the gas flow velocity at each coordinate point in the processing cavity spatial coordinate system per unit time, including the mainstream velocity, local reverse velocity, and velocity fluctuation rate. Local turbulence characteristic information is used to describe the turbulence, swirling and turbulent phenomena of gas at each coordinate point in the processing cavity spatial coordinate system caused by cavity structure, equipment vibration or external disturbance, including turbulence frequency, vortex formation rate, turbulence boundary position and duration, and is synchronously collected with the gas velocity distribution information and bound to the spatial coordinates in the processing cavity spatial coordinate system.

3. The integrated circuit transistor fabrication method with gas purification function according to claim 2, characterized in that: S12. In the target doping injection area within the processing cavity, an ion concentration sensing device is installed, and coordinate calibration is performed according to the spatial coordinate system of the processing cavity. The ion concentration in the initial stage of doping is detected in situ to form an initial doping concentration distribution map. The in situ distribution detection includes the detection of initial ion concentration values, concentration gradients, and diffusion trend information. By integrating the gas velocity distribution information, the local turbulence characteristic information, and the initial doping concentration distribution map, an original gas flow field characteristic information set is constructed. The initial ion concentration value represents the initial ion concentration value of each doped region point, including the number of doped particles per unit volume, used to distinguish the concentration ratio of different implanted particle types, and to record the position of each doped region point in the cavity space coordinate system. The detection of diffusion trend information is used to determine the ion concentration gradient distribution and its corresponding spatial diffusion direction between coordinate points in the processing cavity spatial coordinate system of the doped implantation region.

4. The integrated circuit transistor fabrication method with gas purification function according to claim 1, characterized in that: S2 includes S21 and S22; S21. Based on the gas flow field feature information set as the basic input for simulation modeling, a three-dimensional turbulence simulation model covering the entire circuit transistor processing cavity region is constructed according to the processing cavity spatial coordinate system. The three-dimensional turbulence simulation model takes the gas velocity distribution information and the local turbulence feature information in the gas flow field feature information set as the core input. Under the set cavity structure boundary conditions, combined with the gas viscosity and thermodynamic response characteristics and process-related parameters, the dynamic evolution model of the turbulence behavior at each coordinate point in the processing cavity spatial coordinate system is completed.

5. The integrated circuit transistor fabrication method with gas purification function according to claim 4, characterized in that: S22. Based on the three-dimensional turbulence simulation model, the initial doping concentration distribution map in the gas flow field feature information set is called and spatially aligned under the same reference in the processing cavity spatial coordinate system. By establishing the flow coupling function between the turbulence path and the ion diffusion direction, the evolution process of ion concentration with time and its corresponding spatial migration path trend at each coordinate point in the processing cavity spatial coordinate system during the doping process are analyzed. The analysis is complete through the analysis steps S221, S222 and S223. S221. Analysis task begins: At each doping injection point in the spatial coordinate system of the processing cavity, establish a prediction function for the direction of ion movement driven by turbulence. S222, Analysis Task Execution: Considering the linkage effect between ion particle characteristics and turbulence intensity, simulate the dynamic evolution of ion concentration distribution throughout the entire doping period. S223. Analysis task complete: Output gas distribution and concentration coupling prediction results, including dynamic distribution map of ion concentration within the doping period and gas turbulence path and concentration coupling prediction results. The dynamic distribution map of ion concentration within the doping period is used to describe the spatial isohyet distribution of ion concentration, the concentration trend along the coordinate axis, and the evolution of the concentration gradient over time within multiple time periods. The gas turbulence path and concentration coupling prediction results are used to reflect the mutual influence between the turbulence structure and the ion migration path.

6. The integrated circuit transistor fabrication method with gas purification function according to claim 5, characterized in that: S3 includes S31 and S32; S31. Based on the dynamic distribution map of ion concentration within the doping period and the prediction results of gas turbulence path and concentration coupling, spatial mapping analysis is performed under the constraint of the processing cavity spatial coordinate system to identify key regions affecting uniformity and purification efficiency during the doping process. The key regions include turbulence generation regions, high concentration accumulation regions, and insufficient doping regions. The key regions constitute a key region positioning result set. The turbulence generation region: Based on the spatial distribution of the rate of change of turbulence velocity and turbulence characteristic quantities in the gas turbulence path and concentration coupling prediction results, the region in the processing cavity spatial coordinate system where the airflow turbulence intensity exceeds the threshold is identified. The high-concentration accumulation region: Based on the temporal concentration superposition trend in the dynamic distribution spectrum of ion concentration within the doping period, the region where ions are enriched and accumulated at certain spatial points is identified. The insufficient doping region: By comparing the dynamic distribution spectrum of ion concentration within the doping period with the target concentration benchmark, the spatial range in which the ion concentration is consistently below the threshold is identified.

7. The integrated circuit transistor fabrication method with gas purification function according to claim 6, characterized in that: S32. Based on the key area positioning result set and combined with the physical structure parameters of the processing cavity and the gas injection interface layout, perform the turbulence control optimization task to form an implementable turbulence control factor set. The turbulence control factor set is constructed and obtained through steps S321, S322 and S323. S321. Generation of injection angle correction parameters: Based on the main gas turbulence direction and regional impact path indicated in the gas turbulence path and concentration coupling prediction results, the injection angle of the gas injection device is locally corrected. The deflection angle that the gas injection direction needs to be adjusted relative to the standard gas injection direction is obtained through the local correction and defined as the injection angle correction parameter. S322. Generation of injection speed adjustment parameters: Combining the regional concentration change trend in the dynamic distribution spectrum of ion concentration within the doping period, matching the required gas injection flow rate compensation rate for the local area, and obtaining the injection speed adjustment parameters by adding the gas injection flow rate compensation rate to the base velocity of the injection flow rate. S323, Output of turbulence control factor set: The air injection angle correction parameter and the injection speed adjustment parameter are respectively formatted and organized according to the spatial coordinate points of the processing cavity to form a structured turbulence control factor set for output.

8. The integrated circuit transistor fabrication method with gas purification function according to claim 7, characterized in that: S4 includes S41; S41. Based on the set of disturbance control factors as input, the data is imported into the feedback control module. In the spatial coordinate system of the processing cavity, the applicability of the set of disturbance control factors is evaluated and judged. The deviation evaluation and judgment process is based on the doping uniformity target standard preset in the system. The doping uniformity target standard includes the allowable spatial standard deviation range of ion concentration in the doping region, the allowable concentration deviation limit of each coordinate point, and the upper limit of the fitting error between the ion migration path and the theoretical path. The deviation assessment and judgment process is performed through steps S411, S412 and S413. S411. Comparison of simulation control parameter results with target standard: In the spatial coordinate system of the processing cavity, call the dynamic distribution map of ion concentration within the doping period and the gas turbulence path and concentration coupling prediction results, rerun the simulation process, take the gas injection angle correction parameter and the injection speed adjustment parameter as disturbance input, and calculate the ion concentration redistribution result after the effect of the turbulence optimization control factor. S412. Target Deviation Calculation and Spatial Determination: The ion concentration redistribution result is compared point by point with the doping uniformity target standard. The concentration deviation value at each spatial coordinate point of the processing cavity is calculated, and the corresponding adaptation state is output according to the concentration deviation value. The state level is defined as follows: When the concentration deviation value is within the preset operating deviation range, the normal adaptation status is output. When the concentration deviation value exceeds the preset operating deviation range, an incompatibility status is output; S413. Evaluation Output: Integrate the degree deviation value, the adaptation state, and the set of turbulence control factors to obtain the turbulence control factor adaptation state result set as the optimal gas control strategy output for integrated circuit transistor processing.

9. The integrated circuit transistor fabrication method with gas purification function according to claim 8, characterized in that: S5 includes S51; S51. Apply the gas injection angle correction parameter and the injection speed adjustment parameter in the current optimal gas control strategy to the doping injection device, and perform the doping injection operation according to the spatial coordinate system of the processing cavity. During the doping process, the injection direction and speed are dynamically adjusted according to the control parameters corresponding to each spatial coordinate point to achieve real-time control of the ion migration path and spatial guidance of the turbulence intensity. Simultaneously, the contaminant gas concentration detection module and the airflow stability sensing module deployed in the processing cavity spatial coordinate system synchronously collect: information on the change of contaminant gas concentration, gas flow field fluctuation intensity, and turbulence cycle and change data in the processing area. After the doping process is completed, the ion concentration measurement value of the doped region in the transistor structure is obtained through the ion concentration detection module, identified and reconstructed according to the coordinates of the processing cavity spatial coordinate system, and integrated to obtain the final ion concentration distribution result. The final ion concentration distribution result includes a concentration value matrix, a concentration distribution continuity index set, and a concentration target deviation vector set; The concentration value matrix represents the actual ion concentration value corresponding to each spatial coordinate point of the processing cavity. The concentration distribution continuity index set uses the processing cavity spatial coordinate system as a reference system. Based on the difference between adjacent points in the concentration value matrix, it extracts the smoothness of the concentration change in the three directions of horizontal axis, vertical axis and vertical axis, which is used to quantitatively characterize the uniformity of doping concentration distribution in spatial dimension. The concentration target deviation vector set calculates the deviation between the actual concentration value and the preset target concentration value for each spatial coordinate point of the processing cavity.

10. The integrated circuit transistor fabrication method with gas purification function according to claim 9, characterized in that: S6 includes S61; S61. After the doping process is completed, a comparative analysis is performed between the final ion concentration distribution result and the set target doping distribution result, and a concentration deviation evaluation value is output. Iterative optimization is performed based on the concentration deviation evaluation value. If the concentration deviation exceeds the set threshold, the three-dimensional turbulence simulation model is retrained. If the concentration deviation is within the threshold range, the optimal gas control strategy described in this round is saved as the process parameter template corresponding to the circuit transistor structure type, and the current optimization process is completed.

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