Industrial automatic control system and method based on artificial intelligence

Abstract

The invention discloses an industrial automatic control system and method based on artificial intelligence, and relates to the technical field of control, and the system comprises a data acquisition module, a doping planning module, a doping control module, an early warning terminal and a database. Parameter values of an ionic liquid and an ion implanter used during doping are obtained, when semiconductor doping is controlled according to the obtained data, the doping effect of the semiconductor is analyzed at all collection moments, if the doping effect is not good, the electrical property and crystal lattices of all areas of the semiconductor are analyzed, and if the electrical property of a certain area does not meet the requirement, the doping effect of the semiconductor is not good. And if a lattice damage area exists, adjusting each doping parameter value, and if the lattice damage area exists, analyzing the damage coefficient of the lattice, and performing annealing operation, thereby ensuring the effectiveness of the ionic liquid configuration and each parameter of the ion implanter, and also ensuring the doping efficiency of the semiconductor.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to an industrial automatic control system and method based on artificial intelligence. Background Art

[0002] Semiconductor doping is one of the key processes in the semiconductor manufacturing process. By introducing ions into semiconductor materials, the electrical properties of the semiconductor can be precisely controlled, thereby manufacturing a variety of semiconductor devices with excellent performance. In advanced processes, in order to achieve nanoscale device structures, extremely high requirements are placed on the accuracy and uniformity of doping. It is necessary to precisely control the concentration and distribution of doping ions to form devices such as transistors with excellent performance.

[0003] Traditional industrial automatic control systems and methods configure the ion liquid based on manual experience before doping and set the various parameters of the ion implanter. After the semiconductor is doped, the doping of the semiconductor is monitored. If the effect is not good, the doping process is adjusted. Obviously, this industrial automatic control system and method has the following shortcomings: 1. Traditional industrial automatic control systems and methods configure the ion liquid and adjust the various parameters of the ion implanter based on manual experience. There is no data support, and it is impossible to guarantee the effectiveness of the ion liquid configuration and the effectiveness of the various parameters of the ion implanter.

[0004] 2. Traditional industrial automatic control systems and methods detect the doping effect of semiconductors after the semiconductor doping is completed. They cannot detect the doping effect of semiconductors during the doping process, and thus cannot adjust the semiconductor doping process in real time, and cannot guarantee the doping efficiency of semiconductors. Summary of the Invention

[0005] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an industrial automatic control system and method based on artificial intelligence.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an industrial automatic control system based on artificial intelligence, comprising the following modules: a data acquisition module, a doping planning module, a doping control module, an early warning terminal and a database.

[0007] The data acquisition module is used to acquire the usage and lattice structure of semiconductors from a database.

[0008] The doping planning module includes an ion liquid planning unit and an ion implanter planning unit.

[0009] The ionic liquid planning unit is used to obtain the lattice structure, purpose and ionic liquid used by the semiconductor during each historical doping from the database, and configure the ionic liquid based on the purpose and lattice structure of the semiconductor at that time.

[0010] The ion implanter planning unit is used to obtain the uses of semiconductors and the structures of ions in the ionic liquid used during each historical doping from a database, and analyze the values ​​of various parameters of the ion implanter.

[0011] The doping control module is used to set each collection moment according to a preset time interval when doping the semiconductor according to the planned ion liquid and the parameter values ​​of the ion implanter, analyze the doping effect of the semiconductor at each collection moment, and issue an early warning if the effect is not good, and make adjustments and remedial measures.

[0012] The early warning terminal is used to issue an early warning when the doping effect is not good.

[0013] The database is used to store various information and standard lattice diffraction patterns at various historical doping times.

[0014] In a second aspect, the present invention provides an industrial automatic control method based on artificial intelligence, comprising the following steps: Step 1, obtaining data: obtaining the purpose and lattice structure of the semiconductor from a database.

[0015] Step 2: Plan the ionic liquid: Obtain the lattice structure, purpose, and ionic liquid used by the semiconductor during each historical doping from the database, and configure the ionic liquid based on the purpose and lattice structure of the semiconductor at that time.

[0016] Step 3: Plan the ion implanter: Obtain the usage of the semiconductor and the ion structure of each ion in the ionic liquid used during each historical doping from the database, and analyze the parameter values ​​of the ion implanter.

[0017] Step 4. Control doping: When doping the semiconductor according to the planned ion liquid and ion implanter parameter values, set each collection moment according to the preset time interval, analyze the doping effect of the semiconductor at each collection moment, and if the effect is not good, issue a warning, and make adjustments and remedial measures.

[0018] The beneficial effects of the present invention are: 1. The present invention provides an industrial automatic control system and method based on artificial intelligence. According to the use and lattice structure of the semiconductor, as well as various information of the semiconductor during each historical doping, the parameter values ​​of the ion liquid and ion implanter used during doping are obtained. When controlling the semiconductor doping according to the obtained data, the doping effect of the semiconductor is analyzed at each acquisition moment. If the doping effect is not good, the electrical properties and lattice of each region of the semiconductor are analyzed. If the electrical properties of a certain region do not meet the requirements, the values ​​of each doping parameter are adjusted. If there is an area with lattice damage, the damage coefficient of the lattice is analyzed and an annealing operation is performed, thereby ensuring the effectiveness of the ion liquid configuration and the various parameters of the ion implanter, and also ensuring the doping efficiency of the semiconductor.

[0019] 2. The present invention obtains the uses and lattice structures of semiconductors at each historical doping time from the database, and compares them to obtain each marked semiconductor, and obtains the energy band gap and state density of each marked semiconductor before doping, as well as the energy band gap and state density after doping, and calculates their difference. At the same time, the energy band gap difference and state density difference of each marked semiconductor are used to obtain the mixing ratio of each ion in the ionic liquid. Finally, the ionic liquid is configured according to the mixing ratio of each ion, the concentration of each ion stock solution and the required ionic liquid volume, thereby ensuring the effectiveness of the ionic liquid configuration.

[0020] 3. The present invention obtains the purpose of the semiconductor during each historical doping, the structure of each ion in the ionic liquid used, and the concentration of each ion from the database, and compares them with the purpose of the semiconductor during the current doping, the structure of each ion in the ionic liquid used, and the concentration of each ion, to obtain the acceleration voltage, electrode spacing, and magnetic field strength of the ion implanter, thereby ensuring the effectiveness of the various parameters of the ion implanter.

[0021] 4. When the semiconductor is doped, the present invention analyzes the qualitative indicators and lattice indicators of each region in the semiconductor at each acquisition moment. When at a certain acquisition moment, there is a region with an electrical index of 1 or a lattice index of 1, it means that at the acquisition moment, the doping effect of the semiconductor is not good. At this time, the electrical index of each region is obtained as the lattice index. If there is a region with an electrical index of 1, the values ​​of each doping parameter are adjusted. If there is a region with a lattice index of 1, each region with a lattice index of 1 is obtained, and its lattice damage coefficient is analyzed. At the same time, annealing operation is performed to ensure the doping efficiency of the semiconductor. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0024] Figure 2 The figure is a schematic flow chart of the steps for implementing the method of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1 As shown, the present invention provides an industrial automatic control system based on artificial intelligence, including the following modules: a data acquisition module, a doping planning module, a doping control module, an early warning terminal and a database.

[0027] The acquisition module is connected to the doping planning module, the doping planning module is connected to the doping control module, the doping control module is connected to the early warning terminal, and the database is connected to the data acquisition module, the doping planning module and the doping control module.

[0028] The data acquisition module is used to acquire the usage and lattice structure of semiconductors from a database.

[0029] It should be noted that the uses of semiconductors include integrated circuits, transistors, light-emitting diodes and laser diodes.

[0030] It should also be noted that the lattice structures of semiconductors include diamond structure, zinc blende structure and wurtzite structure, etc.

[0031] The doping planning module includes an ion liquid planning unit and an ion implanter planning unit.

[0032] The ionic liquid planning unit is used to obtain the lattice structure, purpose and ionic liquid used by the semiconductor during each historical doping from the database, and configure the ionic liquid based on the purpose and lattice structure of the semiconductor at that time.

[0033] In a specific embodiment, the ionic liquid is configured, and the specific process is as follows: S11, obtaining the purpose and lattice structure of the semiconductor during each historical doping from the database, and comparing it with the purpose and lattice structure of the semiconductor doped this time. If the purpose and structure of the semiconductor during a certain historical doping are the same as the purpose and lattice structure of the semiconductor doped this time, then the semiconductor doped at that historical time is called a marked semiconductor, and each marked semiconductor is obtained in this way.

[0034] It should be noted that the various ions include boron, phosphorus, and the like.

[0035] S12. Obtain the energy band gap and state density of each labeled semiconductor before doping, as well as the energy band gap and state density after doping from the database, calculate the energy band gap difference and state density difference of each labeled semiconductor, and compare them, select the labeled semiconductor with the smallest energy band gap difference and the smallest state density difference, obtain the mixing ratio of each ion in the ionic liquid used for the labeled semiconductor from the database, and use it as the mixing ratio of each ion in the ionic liquid used for this doping, and calculate the volume of each ion stock solution in the ionic liquid according to the required ionic liquid volume, the mixing ratio of each ion and the concentration of each ion stock solution, and configure it.

[0036] It should be noted that the volume of the required ionic liquid is set by the staff.

[0037] It should be noted that, assuming the required volume of ionic liquid is αL, the volumes of boron and phosphorus stock solutions in the ionic liquid are xL and yL respectively, the concentrations of boron and phosphorus stock solutions are 1 mol / L and 2 mol / L respectively, and the mixing ratio of boron and phosphorus is 1:2, then Thus we get

[0038] The ion implanter planning unit is used to obtain the uses of semiconductors and the structures of ions in the ionic liquid used during each historical doping from a database, and analyze the values ​​of various parameters of the ion implanter.

[0039] It should be noted that ions have different ionic structures. Taking boron as an example, its ionic structure includes B 3+ Ionic structure and B 3- Ionic structure, etc.

[0040] In a specific embodiment, the analysis of the parameter values ​​of the ion implanter is as follows: S21, the purpose of the semiconductor during each historical doping is compared with the purpose of the semiconductor doped this time. If the purpose of the semiconductor during a certain historical doping is the same as the purpose of the semiconductor doped this time, the semiconductor doped at that historical time is called a semiconductor sample. In this way, each semiconductor sample is obtained, and the availability coefficient of the ion implanter acceleration voltage of each semiconductor sample is analyzed. The ion implanter acceleration voltage of the semiconductor sample with the highest availability coefficient is obtained, and it is used as the acceleration voltage of the ion implanter during this doping.

[0041] S22. Obtain the structure and concentration of each ion in the ionic liquid used for each semiconductor sample, and compare them with the structure and concentration of each ion in the ionic liquid used during doping at this time. If the structure and concentration of each ion in the ionic liquid used for a certain semiconductor sample are the same as the structure and concentration of each ion in the ionic liquid used during doping at this time, then the semiconductor sample is called a control semiconductor. Use this method to obtain each control semiconductor, and obtain the ion concentration range on each control semiconductor from the database. Calculate the difference between the maximum and minimum values ​​of the ion concentration range of each control semiconductor, and call it the ion concentration range difference of each control semiconductor. Select the ion implanter electrode spacing and magnetic field strength of the control semiconductor with the smallest difference, and use it as the electrode spacing and magnetic field strength of the ion implanter this time.

[0042] In the above, the analysis of the availability coefficient of the accelerating voltage of the ion implanter for each semiconductor sample is carried out as follows: the doping depth, lattice damage area during doping, and doping area of ​​each semiconductor sample are obtained from the database and normalized. Then, according to the analysis formula: Get the availability coefficient η of the accelerating voltage of the f-th semiconductor sample ion implanter f , where D f represents the lattice damage area when the f-th semiconductor sample is doped, D′ f represents the doping area of ​​the fth semiconductor sample, L′ f represents the doping depth of the f-th semiconductor sample, L represents the target doping depth of the semiconductor, f represents the number of each semiconductor sample, f=1, 2, 3, ..., g, g represents the total number of semiconductor samples, and both f and g are positive integers.

[0043] The doping control module is used to set each collection moment according to a preset time interval when doping the semiconductor according to the planned ion liquid and the parameter values ​​of the ion implanter, analyze the doping effect of the semiconductor at each collection moment, and issue an early warning if the effect is not good, and make adjustments and remedial measures.

[0044] In a specific embodiment, the doping control module has the following specific process: dividing the semiconductor into regions according to a preset area threshold, setting each collection moment according to a preset time interval, collecting each electrical parameter and lattice diffraction pattern in each region at each collection moment, and obtaining the electrical index and lattice index of each region at each collection moment, and analyzing the doping effect of the semiconductor at each collection moment. When the doping effect of the semiconductor at a certain collection moment is not good, the electrical index and lattice index of each region at the collection moment are obtained. If the electrical index of a certain region is 1, the values ​​of each doping parameter are adjusted. If the lattice index of one or more regions is 1, annealing operation is performed on each region with a lattice index of 1.

[0045] It should be noted that the preset area threshold is the critical value used to evaluate whether the regional division is reasonable and is set by the staff.

[0046] It should also be noted that the preset time interval is a boundary value used to determine whether the setting of the collection time is standard, and is set by the staff.

[0047] Among them, various electrical parameters including carrier concentration and resistivity are collected using sensors such as Hall sensors and four-probe testers.

[0048] It is also necessary to explain that the lattice diffraction pattern is collected using an electron diffractometer.

[0049] In the above, the specific process of analyzing the doping effect of the semiconductor at each acquisition moment is as follows: S31, obtaining the values ​​of each electrical parameter of each region at each acquisition moment, and normalizing them, and inputting them into the electrical analysis model at the same time, outputting the electrical indicators of each region at each acquisition moment. If the electrical indicator of a certain region at a certain acquisition moment is 1, it means that the electrical properties of the region do not meet the requirements. If the electrical indicator of a certain region at a certain acquisition moment is 0, it means that the electrical properties of the region meet the requirements.

[0050] It should be noted that the electrical analysis model: In the formula A′ represents the value of the bth electrical parameter of the ath region at the tth acquisition time. tb represents the standard value of the bth electrical parameter at the tth acquisition moment, Represents the electrical index of the ath region at the tth acquisition moment, represents the preset electrical parameter difference threshold, a represents the number of each area, a=1,2,3,...,d, d represents the total number of areas, b represents the number of each electrical parameter, b=1,2,3,...,c, c represents the total number of electrical parameters, t represents the number of each acquisition moment, t=1,2,3,...,u, u represents the total number of acquisition moments, and a, d, b, c, t and u are all positive integers.

[0051] It should also be noted that the standard values ​​of various electrical parameters at each collection time are set by the staff.

[0052] Among them, the preset electrical parameter difference threshold is a critical value used to evaluate whether each electrical parameter meets the requirements, and is set by the staff.

[0053] S32. Obtain the lattice diffraction pattern of each region at each acquisition moment, and compare it with the standard lattice diffraction pattern. If the lattice diffraction pattern of a region at a certain acquisition moment is different from the standard lattice diffraction pattern, it means that the lattice index of the region at the acquisition moment is 1, and the lattice in the region is damaged. If the lattice diffraction pattern of a region at a certain acquisition moment is the same as the standard lattice diffraction pattern, it means that the lattice index of the region at the acquisition moment is 0, and the lattice in the region is not damaged.

[0054] It should be noted that the standard lattice diffraction pattern was obtained from the database.

[0055] S33. Obtain the electrical index and lattice index of each region at each acquisition moment. When the electrical index or lattice index of one or more regions is 1 or 1 at a certain acquisition moment, it means that the doping effect of the semiconductor at the acquisition moment is poor. When the electrical index and lattice index of each region are 0 at a certain acquisition moment, it means that the doping effect of the semiconductor at the acquisition moment is good.

[0056] In the above, the adjustment of each doping parameter is as follows: S41. When the electrical index of a certain area is 1, a virtual test bench is constructed, and a virtual semiconductor and ion implanter are generated, the electrode spacing range and the magnetic field strength range are set, and from the ion mixing ratio set, acceleration voltage set, electrode spacing range and magnetic field strength range, a number of ion mixing ratios, acceleration voltages, electrode spacings and magnetic field strengths are randomly selected and randomly combined to obtain a number of doping combinations, each doping combination including each ion mixing ratio, acceleration voltage, electrode spacing and magnetic field strength.

[0057] It should be noted that the electrode spacing and magnetic field strength of the ion implanter during this doping are obtained and used as the median of the electrode spacing range and the magnetic field strength range, a preset electrode spacing threshold and a preset magnetic field strength threshold are set, and the electrode spacing and magnetic field strength are added or subtracted from the preset electrode spacing threshold and the preset magnetic field strength threshold to obtain the maximum and minimum values ​​of the electrode spacing range and the magnetic field strength range.

[0058] It should also be noted that the preset electrode spacing threshold is a critical value used to evaluate whether the electrode spacing range setting is reasonable, and is obtained by deep learning based on the historical electrode spacing.

[0059] Among them, the preset magnetic field strength threshold is the critical value used to evaluate whether the magnetic field strength range setting is reasonable, and is obtained by deep learning based on historical magnetic field strengths.

[0060] S42. Apply each doping combination on a virtual test bench, and obtain the doping effect of each doping combination at the same time. Select each doping combination with good doping effect, and call it each available doping combination. Calculate the doping coefficient of each available doping combination, and compare them. Select the doping combination with the highest doping coefficient, and use the ion mixing ratio, acceleration voltage, electrode spacing and magnetic field strength in the doping combination as the adjusted ion mixing ratio, acceleration voltage, electrode spacing and magnetic field strength, and adjust them.

[0061] It should be noted that when obtaining the available doping combinations for each sub-application, the values ​​of the electrical parameters of each region of the semiconductor are In the formula A′ represents the bth electrical parameter value of the ath region when the fth available doping combination is applied.b Represents the standard value of the bth electrical parameter, δ f represents the doping coefficient of the f-th available doping combination, a represents the number of each region, a=1,2,3,...,d, d represents the total number of regions, b represents the number of each electrical parameter, b=1,2,3,...,c, c represents the total number of electrical parameters, f represents the number of each available doping combination, f=1,2,3,...,g, g represents the total number of available doping combinations, and a, d, b, c, f and g are all positive integers.

[0062] It should also be noted that the standard values ​​of various electrical parameters are obtained from the database.

[0063] In the above, the annealing operation is performed on each region with a lattice index of 1. The specific process is as follows: obtaining each region with a lattice index of 1, which is referred to as each annealing region, and obtaining the lattice diffraction pattern of each annealing region, analyzing the damage coefficient of the lattice in each annealing region, and at the same time obtaining the damage coefficient of the lattice during each historical annealing in the database, and comparing them. If the damage coefficient of the lattice during a certain historical annealing is the same as the lattice damage coefficient of a certain annealing region, then obtaining the annealing parameters during the historical annealing from the database, and using them as the annealing parameters of the annealing region, in this way, a set of annealing parameters of each annealing region is obtained, each element in the set represents the value of each annealing parameter, calculating the average value of each annealing parameter in the set of each annealing region, and using it as the standard value of each annealing parameter of each region, and at the same time controlling the annealing device to perform annealing operations on each region according to the annealing parameters of each region.

[0064] In the above, the damage coefficient of the lattice in each annealing area is analyzed, and the specific process is as follows: the width of the diffraction peak and the position of the diffraction peak in the lattice diffraction pattern of each annealing area, as well as the width of the diffraction peak and the position of the diffraction peak in the standard lattice diffraction pattern are obtained, and the difference in the width of the diffraction peak and the displacement of the diffraction peak in the lattice diffraction pattern of each annealing area are calculated, and based on the difference in the width of the diffraction peak and the displacement of the diffraction peak in the lattice diffraction pattern of each annealing area, the damage coefficient of the lattice in each annealing area is calculated.

[0065] It should be noted that the damage coefficient of the lattice in each annealing area is obtained by dividing the width difference of the diffraction peak in the lattice diffraction pattern of each annealing area by the width of the diffraction peak in the standard lattice diffraction pattern, and adding the obtained value to the displacement. Assuming that the width of the diffraction peak in the standard lattice diffraction pattern is χ and the position of the diffraction peak is (x′, y′), the width of the diffraction peak in the lattice diffraction pattern of a certain annealing area is χ′ and the position of the diffraction peak is (x″, y″), then the width difference of the diffraction peak in the lattice diffraction pattern of the annealing area is |χ-χ′|, and the displacement is The damage coefficient of the lattice in the annealed area is

[0066] The early warning terminal is used to issue an early warning when the doping effect is not good.

[0067] The database is used to store various information of various historical doping times, standard values ​​of various electrical parameters and standard lattice diffraction patterns.

[0068] It should be noted that the information at each historical doping time includes the purpose of the semiconductor, the lattice structure of the semiconductor, the mixing ratio of each ion in the ionic liquid used, the structure of each ion in the ionic liquid, the concentration of each ion, the band gap, state density, doping depth, lattice damage area during doping and doping area.

[0069] See also Figure 2 As shown, the present invention provides an industrial automatic control method based on artificial intelligence, comprising the following steps: Step 1, obtaining data: obtaining the purpose and lattice structure of the semiconductor from a database.

[0070] Step 2: Plan the ionic liquid: Obtain the lattice structure, purpose, and ionic liquid used by the semiconductor during each historical doping from the database, and configure the ionic liquid based on the purpose and lattice structure of the semiconductor at that time.

[0071] Step 3: Plan the ion implanter: Obtain the usage of the semiconductor and the ion structure of each ion in the ionic liquid used during each historical doping from the database, and analyze the parameter values ​​of the ion implanter.

[0072] Step 4. Control doping: When doping the semiconductor according to the planned ion liquid and the parameter values ​​of the ion implanter, set each collection moment according to the preset time interval, analyze the doping effect of the semiconductor at each collection moment, and if the effect is not good, issue an early warning, and make adjustments and remedial measures.

[0073] The embodiment of the present invention obtains the parameter values ​​of the ion liquid and ion implanter used during doping based on the purpose and lattice structure of the semiconductor and the information of the semiconductor during each historical doping. When controlling the semiconductor doping according to the obtained data, the doping effect of the semiconductor is analyzed at each acquisition moment. If the doping effect is not good, the electrical properties and lattice of each region of the semiconductor are analyzed. If the electrical properties of a certain region do not meet the requirements, the values ​​of each doping parameter are adjusted. If there is a region with damaged lattice, the damage coefficient of the lattice is analyzed and an annealing operation is performed, thereby ensuring the effectiveness of the ion liquid configuration and the parameters of the ion implanter, and also ensuring the doping efficiency of the semiconductor.

[0074] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. An industrial automatic control system based on artificial intelligence, characterized by: Includes the following modules: The data acquisition module is used for acquiring the usage and lattice structure of semiconductors from a database; The doping planning module includes the ion liquid planning unit and the ion implanter planning unit: The ionic liquid planning unit is used to obtain the lattice structure, purpose and ionic liquid used by the semiconductor during each historical doping from the database, and configure the ionic liquid based on the purpose and lattice structure of the semiconductor at that time; The ion implanter planning unit is used to obtain the uses of semiconductors and the structures of ions in the ionic liquid used in each historical doping from the database, and analyze the values ​​of various parameters of the ion implanter; The doping control module is used to set each collection time according to the preset time interval when doping the semiconductor according to the planned ion liquid and the various parameter values ​​of the ion implanter, analyze the doping effect of the semiconductor at each collection time, and issue an early warning if the effect is not good, and make adjustments and remedial measures; The early warning terminal is used to issue an early warning when the doping effect is not good; The database is used to store various information of each historical doping, standard values ​​of various electrical parameters and standard lattice diffraction patterns.

2. The artificial intelligence-based industrial automatic control system according to claim 1, characterized in that: The specific process of configuring the ionic liquid is as follows: S11. Obtaining the purpose and lattice structure of the semiconductor at each historical doping event from the database, and comparing them with the purpose and lattice structure of the semiconductor doped this time; if the purpose and structure of the semiconductor at a certain historical doping event are the same as the purpose and lattice structure of the semiconductor doped this time, the semiconductor doped at that historical time is referred to as a marker semiconductor, and each marker semiconductor is obtained in this way; S12. Obtain the energy band gap and state density of each labeled semiconductor before doping, as well as the energy band gap and state density after doping from the database, calculate the energy band gap difference and state density difference of each labeled semiconductor, and compare them, select the labeled semiconductor with the smallest energy band gap difference and the smallest state density difference, obtain the mixing ratio of each ion in the ionic liquid used for the labeled semiconductor from the database, and use it as the mixing ratio of each ion in the ionic liquid used for this doping, and calculate the volume of each ion stock solution in the ionic liquid according to the required ionic liquid volume, the mixing ratio of each ion and the concentration of each ion stock solution, and configure it.

3. The artificial intelligence-based industrial automatic control system according to claim 1, characterized in that: The specific process of analyzing the parameter values ​​of the ion implanter is as follows: S21. Compare the usage of the semiconductor during each doping cycle in history with the usage of the semiconductor being doped this time. If the usage of the semiconductor during a certain doping cycle in history is the same as the usage of the semiconductor being doped this time, the semiconductor doped at that time in history is referred to as a semiconductor sample. Using this method, obtain each semiconductor sample, analyze the availability coefficient of the ion implanter acceleration voltage of each semiconductor sample, and obtain the ion implanter acceleration voltage of the semiconductor sample with the highest availability coefficient, which is used as the ion implanter acceleration voltage for the current doping cycle. S22. Obtain the structure and concentration of each ion in the ionic liquid used for each semiconductor sample, and compare them with the structure and concentration of each ion in the ionic liquid used during doping at this time. If the structure and concentration of each ion in the ionic liquid used for a certain semiconductor sample are the same as the structure and concentration of each ion in the ionic liquid used during doping at this time, then the semiconductor sample is called a control semiconductor. Use this method to obtain each control semiconductor, and obtain the ion concentration range on each control semiconductor from the database. Calculate the difference between the maximum and minimum values ​​of the ion concentration range of each control semiconductor, and call it the ion concentration range difference of each control semiconductor. Select the ion implanter electrode spacing and magnetic field strength of the control semiconductor with the smallest difference, and use it as the electrode spacing and magnetic field strength of the ion implanter this time.

4. The artificial intelligence-based industrial automatic control system according to claim 3, characterized in that: The specific process of analyzing the availability coefficient of the accelerating voltage of the ion implanter of each semiconductor sample is as follows: The doping depth, lattice damage area and doping area of ​​each semiconductor sample are obtained from the database and normalized. The analysis formula is as follows: Get the availability coefficient η of the accelerating voltage of the f-th semiconductor sample ion implanter f , where D f represents the lattice damage area when the f-th semiconductor sample is doped, D′ f represents the doping area of ​​the fth semiconductor sample, L′ f represents the doping depth of the f-th semiconductor sample, L represents the target doping depth of the semiconductor, f represents the number of each semiconductor sample, f=1, 2, 3, ..., g, g represents the total number of semiconductor samples, and both f and g are positive integers.

5. The artificial intelligence-based industrial automatic control system according to claim 1, characterized in that: The doping control module has the following specific process: The semiconductor is divided into regions according to a preset area threshold, and each collection moment is set according to a preset time interval. At each collection moment, the electrical parameters and lattice diffraction patterns in each region are collected, and the electrical index and lattice index of each region at each collection moment are obtained. At the same time, the doping effect of the semiconductor at each collection moment is analyzed. When the doping effect of the semiconductor at a certain collection moment is not good, the electrical index and lattice index of each region at the collection moment are obtained. If the electrical index of a certain region is 1, the values ​​of the doping parameters are adjusted. If the lattice index of one or more regions is 1, the regions with the lattice index of 1 are annealed.

6. The artificial intelligence-based industrial automatic control system according to claim 5, characterized in that: The specific process of analyzing the doping effect of the semiconductor at each acquisition moment is as follows: S31. Obtain the values ​​of the electrical parameters of each region at each acquisition time, perform normalization processing, and input them into the electrical analysis model to output the electrical index of each region at each acquisition time. If the electrical index of a region at a certain acquisition time is 1, it means that the electrical performance of the region does not meet the requirements. If the electrical index of a region at a certain acquisition time is 0, it means that the electrical performance of the region meets the requirements. S32. Obtaining the lattice diffraction pattern of each region at each acquisition time, and comparing it with the standard lattice diffraction pattern. If the lattice diffraction pattern of a region at a certain acquisition time is different from the standard lattice diffraction pattern, it means that the lattice index of the region at the acquisition time is 1, and the lattice in the region is damaged. If the lattice diffraction pattern of a region at a certain acquisition time is the same as the standard lattice diffraction pattern, it means that the lattice index of the region at the acquisition time is 0, and the lattice in the region is not damaged. S33. Obtain the electrical index and lattice index of each region at each acquisition moment. When the electrical index or lattice index of one or more regions is 1 or 1 at a certain acquisition moment, it means that the doping effect of the semiconductor at the acquisition moment is poor. When the electrical index and lattice index of each region are 0 at a certain acquisition moment, it means that the doping effect of the semiconductor at the acquisition moment is good.

7. The artificial intelligence-based industrial automatic control system according to claim 5, characterized in that: The specific process of adjusting the values ​​of the doping parameters is as follows: S41. When the electrical property index of a certain area is 1, a virtual test bench is constructed, and a virtual semiconductor and ion implanter are generated. The electrode spacing range and the magnetic field strength range are set. From the set of ion mixing ratios, the set of acceleration voltages, the electrode spacing range, and the magnetic field strength range, a number of ion mixing ratios, a number of acceleration voltages, a number of electrode spacings, and a number of magnetic field strengths are randomly selected and randomly combined to obtain a number of doping combinations, each of which includes a certain ion mixing ratio, acceleration voltage, electrode spacing, and magnetic field strength. S42. Apply each doping combination on a virtual test bench, and obtain the doping effect of each doping combination at the same time. Select each doping combination with good doping effect, and call it each available doping combination. Calculate the doping coefficient of each available doping combination, and compare them. Select the doping combination with the highest doping coefficient, and use the ion mixing ratio, acceleration voltage, electrode spacing and magnetic field strength in the doping combination as the adjusted ion mixing ratio, acceleration voltage, electrode spacing and magnetic field strength, and adjust them.

8. The artificial intelligence-based industrial automatic control system according to claim 5, characterized in that: The annealing operation is performed on each region with a lattice index of 1, and the specific process is as follows: Obtain each region with a lattice index of 1, which is called each annealing region, and obtain the lattice diffraction pattern of each annealing region, analyze the damage coefficient of the lattice in each annealing region, and at the same time obtain the damage coefficient of the lattice during each historical annealing in the database and compare them. If the damage coefficient of the lattice during a certain historical annealing is the same as the lattice damage coefficient of a certain annealing region, then obtain the annealing parameters of the historical annealing from the database and use them as the annealing parameters of the annealing region. In this way, a set of annealing parameters of each annealing region is obtained, and each element in the set represents the value of each annealing parameter. Calculate the average value of each annealing parameter in the set of each annealing region and use it as the standard value of each annealing parameter of each region. At the same time, control the annealing device to perform annealing operations on each region according to the annealing parameters of each region.

9. The artificial intelligence-based industrial automatic control system according to claim 8, characterized in that: The specific process of analyzing the damage coefficient of the lattice in each annealing area is as follows: The width of the diffraction peak and the position of the diffraction peak in the lattice diffraction pattern of each annealing area, as well as the width of the diffraction peak and the position of the diffraction peak in the standard lattice diffraction pattern are obtained, and the width difference of the diffraction peak and the displacement of the diffraction peak in the lattice diffraction pattern of each annealing area are calculated, and based on the width difference of the diffraction peak and the displacement of the diffraction peak in the lattice diffraction pattern of each annealing area, the damage coefficient of the lattice in each annealing area is calculated.

10. An industrial automatic control method for executing the artificial intelligence-based industrial automatic control system according to any one of claims 1 to 9, characterized in that: include: Step 1: Obtain data: Obtain the uses and lattice structures of semiconductors from the database; Step 2: Plan the ionic liquid: Obtain the lattice structure, application, and ionic liquid used by the semiconductor at each historical doping event from the database, and configure the ionic liquid based on the application and lattice structure of the semiconductor at that time; Step 3: Plan the ion implanter: Obtain the usage of the semiconductor and the ion structure of each ion in the ionic liquid used in each historical doping from the database, and analyze the parameter values ​​of the ion implanter; Step 4: Control doping: When doping the semiconductor according to the doping planning module, set each collection moment according to the preset time interval, analyze the doping effect of the semiconductor at each collection moment, and if the effect is not good, issue an early warning, and make adjustments and remedial measures.

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