Semiconductor device manufacturing system, manufacturing method, and server

Abstract

The purpose of the present invention is to provide a system for detecting shape defects that cannot be obtained only by top-view geometric dimension data and deriving optimal processing conditions in the depth direction of an etched shape. Therefore, the semiconductor device manufacturing system of the present invention is characterized in that the optimization of the etching parameter is performed in at least one etching step based on correlation data between the etching parameter and the change amount of the feature amount and the acquired change amount of the feature amount. The feature amount is a value obtained from secondary electron data or interference light data from the surface of the sample, the change amount of the feature amount is the difference between the feature amount and a target value of the feature amount, and the acquired change amount of the feature amount is the change amount of the feature amount acquired for each depth of the etched shape formed by the semiconductor manufacturing apparatus. The etching steps are respective steps of processing conditions of the semiconductor manufacturing apparatus corresponding to each depth of an etching shape formed by the semiconductor manufacturing apparatus.

Description

Technical Field

[0001] The present invention relates to a semiconductor device manufacturing system, a manufacturing method and a server. Background Art

[0002] At present, the plasma etching system is part of the semiconductor manufacturing tools used to manufacture semiconductor devices that are miniaturized close to the atomic level. In the device manufacturing device, one of the requirements is to improve uniformity. It is known that in order to manufacture semiconductor devices close to the atomic level, it is not sufficient to measure the lateral dimensions of the semiconductor device only by observing from the upper surface of the wafer and to perform uniformization based on the measured data. For example, the above-mentioned measuring device has a CD-SEM, but only the geometric dimension data of the top view obtained from the CD-SEM can obtain the surface dimensions of the pattern, so the arch shape and notch shape of the part slightly deeper from the surface of the pattern cannot be measured. Therefore, even with the same CD (Critical Dimension) value, the three-dimensional geometric defects of the pattern cannot be detected. Obviously, if this cannot be detected, defects will occur in the ion implantation process and CVD process after etching. In addition, these defective shapes are not only related to the temperature of the sample during etching, but also to the plasma characteristics, so complex etching parameter control is required.

[0003] As shown in Patent Document 1, the prior art is as follows: based on the CD value as geometric dimension data of the top view, the CD value deviation within the chip surface is fed back to the temperature value of the electrostatic chuck, thereby achieving a uniform CD value within the chip surface and improving the chip surface yield.

[0004] Prior Art Literature

[0005] Patent Literature

[0006] Patent Document 1: JP Patent No. 5925943 Summary of the invention

[0007] -Problems to be solved by the invention-

[0008] However, the prior art is a technology for making CD values ​​uniform, and therefore cannot cope with the problem that a bow or notch shape is generated in the depth direction despite showing the same CD value. In addition, since it is a technology for feeding back the temperature of the electrostatic chuck, it cannot cope with the problem of pattern shape caused by plasma characteristics.

[0009] In addition, the bow and notch shapes are greatly affected by the processing conditions corresponding to the depth direction of the etching shape, so it is necessary to optimize the processing conditions at each depth. However, since the existing technology cannot infer the cross-sectional shape, it is impossible to cope with the optimization of the processing conditions at each depth.

[0010] Therefore, an object of the present invention is to provide a system for detecting shape defects that cannot be obtained only from geometric dimensional data of a top view, and deriving optimal processing conditions in the depth direction of an etching shape.

[0011] -Methods for solving problems-

[0012] In order to solve the above-mentioned problems, one of the representative semiconductor device manufacturing systems of the present invention controls etching parameters so as to obtain the desired processing results of the semiconductor manufacturing device, characterized in that the etching parameters are optimized in at least one etching step based on the correlation data between the etching parameters and the change amount of the characteristic quantity, and the obtained change amount of the characteristic quantity, wherein the characteristic quantity is a value obtained from secondary electron data or interference light data from the surface of the sample, the change amount of the characteristic quantity is the difference between the characteristic quantity and a target value of the characteristic quantity, the obtained change amount of the characteristic quantity is the change amount of the characteristic quantity obtained for each depth of the etching shape formed by the semiconductor manufacturing device, and the etching steps are each step of the processing conditions of the semiconductor manufacturing device corresponding to each depth of the etching shape formed by the semiconductor manufacturing device.

[0013] -Effects of the Invention-

[0014] According to the present invention, it is possible to detect shape defects that cannot be obtained only from geometric dimension data of top view, and derive the optimal processing conditions in the depth direction of the etching shape. In addition, the user can optimize the etching processing recipe (processing conditions) that does not cause device defects without observing the cross section of the wafer and without destroying it when or before the device defects occur. In addition, by subdividing the etching steps in the etching depth direction to perform the optimization cycle, the user can reduce the number of trials for optimizing the etching parameters until the ideal device electrical characteristics are obtained.

[0015] Other problems, structures, and effects than those described above will become apparent from the following description of embodiments for implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the semiconductor device manufacturing system involved in the first embodiment.

[0017] Figure 2 It is a correlation diagram between pattern shape and secondary electron distribution.

[0018] Figure 3 It is a correlation diagram between pattern shape and secondary electron distribution under given etching parameters.

[0019] Figure 4A This is a flowchart showing an example of an etching parameter optimization cycle.

[0020] Figure 4B : is a flowchart showing another example of the etching parameter optimization cycle.

[0021] Figure 5 It is a correlation diagram between etching steps and optimized cycles.

[0022] Figure 6 It is a correlation diagram between the number of trials and the device failure rate.

[0023] Figure 7 This is a conceptual diagram of a prediction model of optimal etching parameters in the second embodiment. DETAILED DESCRIPTION

[0024] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. In addition, the present invention is not limited to the embodiment. In addition, in the description of the accompanying drawings, the same reference numerals are attached to the same parts.

[0025] In one example of an embodiment of the present invention, a server is provided, which stores the variation of the characteristic quantity obtained from the secondary electron distribution obtained from the top view of a wafer (also referred to as a sample) based on CD-SEM and the etching parameters in the etching step corresponding to the depth direction of the etching shape as relevant data, and performs etching parameter control in a semiconductor manufacturing device based on the relevant data. The following is an explanation based on the embodiment.

[0026] [First embodiment]

[0027] pass Figure 1 A first embodiment of the present invention will be described. Figure 1 1 is a schematic diagram of a semiconductor device manufacturing system according to the first embodiment. The semiconductor device manufacturing system 10 includes: a device group including a semiconductor manufacturing device (etching device) 105, a PC for CD-SEM 101, a PC for semiconductor manufacturing device 102, a semiconductor inspection device 104 (for example, CD-SEM); and a server 103. The purpose of this figure is to explain the performance improvement of the semiconductor device manufacturing system which is important in semiconductor manufacturing.

[0028] The process recipe for etching is input to the semiconductor manufacturing apparatus PC 102 or transferred from the server 103 to the PC 102 for execution. After execution, the etching apparatus 105 transmits instruction values ​​according to the process recipe content to each device in the etching apparatus to perform etching. Figure 1The devices and components described are only the main parts, but the diagram shows a power supply for adjusting the temperature of the wafer, a temperature adjustment electrode (heater / Peltier), an RF power supply for applying high frequency, a microwave power supply for plasma generation, a plasma generation gas, a magnetic field generating coil for adjusting the plasma density, an electrostatic adsorption electrode (electrostatic chuck), and a lower electrode. The wafer as a sample is set on the sample stage and etched by plasma treatment. The implemented treatment recipe can be saved in the server 103 via the semiconductor manufacturing device PC 102.

[0029] In this embodiment, as etching parameter control, two systems related to plasma characteristics such as sample temperature and plasma density that have a great influence on the shape of device defects are described. That is, there is a system for adjusting sample temperature and a system for controlling plasma characteristics. Here, it is the etching parameters that can directly control the equipment, and the plasma characteristics, sample temperature, etc. are the results of adjusting the etching parameters (collectively referred to as the etching environment), which is different in this point.

[0030] The temperature etching parameter related to the sample temperature adjustment system is an indication value given to the system (temperature adjustment system) that controls the temperature of the temperature adjustment elements inside the sample stage, such as the heater element and the Peltier element. For example, it is an indication value for controlling the current value of the power supply connected to the heater element, including the current value, the on / off frequency of the switch for bypassing and releasing the current, etc.

[0031] In addition, the etching parameters of plasma characteristics related to the plasma characteristic control system are roughly divided into the following three types. They are the indication values ​​for the system used to control the power for plasma generation (such as a high-frequency power supply that supplies high-frequency power for plasma generation), the indication values ​​for the distribution control system of plasma after plasma generation, and the indication values ​​for the bias application system that introduces ions and electrons from plasma after plasma generation to the sample stage (such as a high-frequency power supply that supplies high-frequency power to the sample stage). The indication values ​​for the system used to control the power for plasma generation include, for example, the voltage value, frequency value, and application time of microwave power. The indication values ​​for the distribution control system of plasma after plasma generation include, for example, the current value of the coil power supply, the angle of the phase control plate on the microwave path, etc. The indication values ​​for the bias application system that introduces ions and electrons from plasma to the sample stage include, for example, the voltage value, frequency value, and application time of the RF power supply connected to the sample stage, the capacitance value of the variable capacitor for matching integration, and the variable reactance value.

[0032] The sample after etching is transported to a semiconductor inspection device 104 (eg, CD-SEM). Figure 1In the process, the recipe for CD-SEM is input into the CD-SEM PC 101, and a given measurement is performed. Here, an electron beam is used in the measurement, but a characteristic quantity calculation based on the secondary electron quantity (secondary electron data) after the primary electron beam irradiation is performed. Here, as a representative characteristic quantity, a characteristic quantity obtained from the intensity distribution of the secondary electron quantity (secondary electron distribution) is used for explanation. As other characteristic quantities, there are various characteristic quantities such as a characteristic quantity using illumination data and a characteristic quantity obtained by differentiating the above-mentioned intensity distribution (sometimes a characteristic quantity obtained based on the secondary electron distribution derived from the etching shape of the sample is also referred to as a distribution characteristic quantity). The measured results can be saved in the server 103 via PC 101.

[0033] Using the above-stored measurement data, the sample yield is visualized based on the distribution of the characteristic quantities within the sample surface. If the obtained yield value is not satisfied, the processing recipe for etching is corrected. Even if the CD values ​​are the same, if there is a difference in the characteristic quantities, it is considered that the pattern shape is defective. This is explained in more detail below.

[0034] (Secondary electron distribution)

[0035] Figure 2 is a correlation diagram between the pattern shape and the secondary electron distribution. Figure 2 In the figure, a schematic diagram of the pattern shape of the device is recorded on the left, and a schematic diagram of the distribution of the secondary electron intensity (secondary electron distribution) is recorded on the right. Figure 2 The TopCD values ​​of the pattern shapes in are all the same. Since the amount of secondary electrons varies due to the high and low acceleration voltages, there is an optimal acceleration voltage that makes it easy to extract the shape of the undesirable situation. Here, a bow shape is cited as an example of an undesirable situation shape to explain how the distribution of the amount of secondary electrons changes when the acceleration voltage is high and low, and which one is optimal. In addition, regarding the pattern shape of the device, when it is a shape formed by etching, it is sometimes also referred to as an etching shape.

[0036] Figure 2 (a) is an explanation of the acceleration voltage applied to the primary electrons when the acceleration voltage is high (acceleration voltage: Vh1). (a-1) is a pattern shape with a bow shape (bad condition shape), but since the penetration depth of the primary electrons of the acceleration voltage is deep, the amount of secondary electrons that seep out from the side walls of the pattern is large, and with this, the intensity of the secondary electrons also increases. Therefore, the difference (variation) between the characteristic quantity of the pattern shape when there is a bow shape in which the lateral width of the pattern shape decreases (the intensity of the secondary electrons at the center of the top surface of the pattern) and the characteristic quantity when the lateral width of the pattern shape shown in (a-2) is the target shape of a normal value, that is, jk, becomes larger, so the detection of the bow shape is easy.

[0037] on the other hand, Figure 2 (b) is an explanation of the case where the acceleration voltage of the applied voltage of the primary electron is low (acceleration voltage: Vl1). The difference in the intensity of the secondary electrons (the change in the characteristic quantity), i.e., hi, between the case of the bow shape shown in (b-1) and the target shape shown in (b-2) is small, so it is difficult to detect the bow shape.

[0038] As described above, the ease of detecting a pattern shape defect changes by adjusting the acceleration voltage applied to primary electrons during electron beam irradiation, and therefore it is important to adjust the acceleration voltage to an optimum value.

[0039] As described above, since the distribution feature quantity also includes information on the etching depth direction of the etching shape, by calculating the difference in the feature quantity within the sample surface, it is possible to find out the undesirable conditions such as the bow shape in the etching depth direction that cannot be obtained only by the size data of the plane obtained from the top view. In addition, this can also be calculated by using the tilt function in the CD-SEM to tilt the sample to calculate the change in the feature quantity, and calculate the change in more detailed feature quantity. Alternatively, the electron beam irradiated to the sample can be tilted to calculate the change in the feature quantity. In addition, as a method of irradiating the electron beam, the sample stage on which the sample is provided can be tilted, and the electron beam itself can be tilted.

[0040] (Feature quantities and etching parameters)

[0041] Next, the correlation between the characteristic amount and the etching parameter will be described. Figure 3 It is a correlation diagram between pattern shape and secondary electron distribution under given etching parameters. As an example, the pattern shape and characteristic values ​​of the periphery of the sample (sample edge or wafer edge), the pattern shape and characteristic values ​​of the center of the sample (sample center or wafer center), and the temperature environment and plasma density environment of each location are shown. Here, as an example, the device at the center of the sample is the pattern target shape, the temperature at the center of the sample is T2, and the plasma density is P2. At the edge of the sample where the device is in a bow shape and an undesirable situation occurs, the temperature at the edge of the sample is T1, and the plasma density is P1. Figure 3 In the figure, the pattern shape schematically showing the edge and center of the sample is represented by z as the vertical axis and B as the horizontal axis. L1 to L4 are the switching lines of the film types. The oblique line portion represents the shape difference between the edge and the center of the sample. The area of ​​the shape difference portion (area difference) is ∫ΔBdz. Here, there are two methods: optimizing the etching parameters by finding ∫ΔBdz or optimizing without finding the etching parameters, so the following will explain.

[0042] (Method 1)

[0043] Method 1 is a method of controlling etching parameters without determining ∫ΔBdz.

[0044] First, ∫ΔBdz can be expressed as in Formula 1 using function Q.

[0045] ∫ΔBdz=Q((fa)-(ga))…Equation 1

[0046] Next, when the sample temperature difference ΔT= T1 - T2 and the plasma density difference ΔP= P1 - P2 , ∫ΔBdz can also be expressed as in Formula 2 using the function R.

[0047] ∫ΔBdz=R(ΔT,ΔP)…Equation 2

[0048] According to formula 1 and formula 2,

[0049] Q(fg)=R(ΔT, ΔP)

[0050] Therefore, fg can be expressed as in Formula 3 using function Z.

[0051] fg=Z(ΔT,ΔP)…Equation 3

[0052] Here, ideally, fg=0,

[0053] Therefore, the values ​​of T and P that make Z(ΔT, ΔP) = 0 can be obtained using Formula 3.

[0054] (Method 2)

[0055] Method 2 is a method of determining ∫ΔBdz to control etching parameters.

[0056] First, CD measurement is performed in real time in L1 to L4 using OCD (Optical Critical Dimension) to determine ∫ΔBdz. At this time, the measurement points in the depth direction can be stacked films of different film types. In the case of the same film, the samples can be separated and measured using SEM or TEM. SEM and TEM can also be used for stacked films.

[0057] Furthermore, ∫ΔBdz can be expressed as in Formula 4 using a given function R.

[0058] ∫ΔBdz=R(ΔT,ΔP)…Equation 4

[0059] Here, since ∫ΔBdz=0 is ideal, the values ​​of T and P that provide R(ΔT, ΔP)=0 may be obtained from equation 4.

[0060] In addition, the derivation of the function described in the present disclosure is not particularly limited, and can be obtained inductively through experiments.

[0061] In the first embodiment, method 1 is adopted, so there is no need to calculate the geometric size difference. On the other hand, in the second embodiment described later, method 2 can be adopted. As a method for optimizing the etching parameters of method 1, a method of introducing an optimization cycle is described below by a flowchart. As etching parameters, in addition to the following description, the temperature of the sample in the sample temperature adjustment system, the plasma characteristics in the plasma characteristic control system, etc. can also be appropriately selected or combined.

[0062] <Optimization cycle (1)>

[0063] Figure 4A This is a flowchart showing an example of an etching parameter optimization cycle.

[0064] First, after the sample is etched, N measurement points are selected. N is a number that is 1 more than the number of parameters to be determined. For example, in this embodiment, two points are selected, namely, the center of the sample and the edge of the sample. In this embodiment, in order to simplify the description, the plasma density is not changed, and the parameter to be determined is only the temperature, so the above two measurement points are sufficient.

[0065] Next, the sample temperature T and plasma density P at the selected position are measured, and characteristic quantities are also measured.

[0066] Next, the sample temperature difference ΔT and plasma density difference ΔP at the sample center and sample edge are calculated. In addition, the difference in characteristic quantities, i.e., the change (e.g. Figure 3 fg).

[0067] Next, referring to Formula 3, which is the correlation data between the etching parameter and the change amount of the characteristic quantity, in order to calculate the value of the function Z, set fg as the target, T2 and P2 as the sample center as the reference point. As described above, if P1 and P2 at the edge of the sample are the same, T1 at the edge of the sample is calculated according to Formula 3. When the difference fg of the characteristic quantity as the target is 0, the characteristic quantity itself can also be set as a target, and in this case, T1 is also calculated by Formula 3. This is the temperature at the edge of the sample that forms the target shape.

[0068] As an etching parameter Ep satisfying this T1, the temperature near the edge of the sample stage is adjusted. Taking into account the heat input balance from the plasma, the temperature near the edge of the sample stage is adjusted so that the temperature of the sample edge becomes T1, and etching is performed. For example, the temperature near the edge of the sample stage is adjusted by controlling the current value of a heater element or a Peltier element disposed near the edge of the sample inside the sample stage, or by controlling the temperature of a refrigerant disposed near the edge of the sample.

[0069] Next, the electrical characteristics of the device on the sample are checked. If the reference is satisfied, the process ends. If not, the process is repeated from the setting of the difference in the target characteristic quantity to achieve optimization of the etching parameters (etching parameter optimization cycle (1)).

[0070] <Optimization cycle (2)>

[0071] exist Figure 4A In the description, the premise is to measure the sample temperature and plasma density, calculate the correlation with the characteristic quantity, and calculate the etching parameters that can reproduce the optimal sample temperature and plasma density. On the other hand, there is also a method that does not measure the sample temperature and plasma density, but directly calculates the correlation between the difference in characteristic quantities and the etching parameters to directly obtain the etching parameters. Figure 4B : is a flowchart showing another example of the etching parameter optimization cycle.

[0072] First, after etching the sample, N measurement points are selected. N is a number greater than the number of parameters to be determined by 1. For example, in this embodiment, two points are selected: the center of the sample and the edge of the sample.

[0073] Next, the etching parameter Ep at the selected position is stored in the database (DB1) in the server. In addition, the feature quantity at the selected position is measured and stored in DB1.

[0074] Next, the change in the characteristic quantity between the sample center and the sample edge between the selected positions is calculated (e.g. Figure 3 fg).

[0075] DB1 stores data on the distribution of etching parameters Ep and feature quantities in the sample surface. It is possible to calculate the etching parameter Ep with high correlation from DB1 based on the given feature quantity and control it so that the distribution in the surface is uniform. Here, the difference in feature quantity (e.g., fg) and the etching parameter Ep can be expressed using function W as in equation 5.

[0076] fg=W(Ep)…Equation 5

[0077] Next, referring to Equation 5, which is the correlation data of the etching parameter and the change amount of the feature quantity, in order to calculate the value of the function W, fg is set as the target. According to Equation 5, the etching parameter Ep of the target shape of the sample edge is obtained from the sample center Ep0 as the reference point, and etching is performed. In the case where the difference fg of the target feature quantity is 0, the target can also be set for the feature quantity itself, in which case Ep is also obtained by Equation 5. In addition, in the case where W in Equation 5 is unknown, Ep, a feature quantity that can achieve the target, can also be estimated by machine learning based on the combination of Ep and the feature quantity stored in DB1 (refer to the second embodiment).

[0078] Next, the electrical characteristics of the device on the sample are checked. If the standard is met, the process ends. If not, the process is repeated from the setting of the difference in the target characteristic quantity to achieve optimization of the etching parameters (etching parameter optimization cycle (2)).

[0079] In the above flow, the etching parameter Ep can be obtained without going through the process of difficultly measuring the sample temperature and plasma density. However, since the number of parameters for obtaining the correlation between the characteristic quantity difference and Ep increases, parameter estimation is difficult.

[0080] (Etching steps and optimization cycles)

[0081] The above-mentioned optimization cycle of etching parameters can be applied individually to each step (hereinafter referred to as "etching step") of a process recipe of a semiconductor manufacturing apparatus corresponding to each given etching depth (see Figure 3 ). Figure 5 is a correlation diagram between etching steps and optimized cycles. Figure 5 In the example, five etching steps are used. The optimal cycle used can be Figure 4A , Figure 4B Any one of .

[0082] Figure 5 (a) is the case where the etching parameter optimization cycle is not applied. In this case, the undesirable shapes such as the bow shape cannot be controlled and are obvious.

[0083] Figure 5 (b) is the case where the optimization cycle is applied only to etching step 1. If the contribution of etching step 1 to the pattern shape is large, then Figure 5 Compared with (a), a significant improvement can be seen, but the contribution rate needs to be studied separately.

[0084] Figure 5 (c) is the case where the optimized cycle is applied to all etching steps. In this case, there is no need to study the contribution rate. Figure 5 can be greatly improved compared with (a).

[0085] If the etching step with the highest contribution to the etching shape is known in advance, Figure 5 The method of applying the optimization cycle only to a specific etching step as in (b) is preferred in terms of optimizing time efficiency. On the other hand, when the contribution rate is unknown, such as Figure 5 As shown in (c), sometimes it is preferable to deal with it by applying an optimization cycle method in all etching steps in terms of optimizing time efficiency. In short, if it does not take time to determine the contribution rate of each etching step to the etching shape, then Figure 5Method (b) is good, when determining the contribution rate, it takes time. Figure 5 The method of (c) is good.

[0086] Next, the number of trials required for the device's electrical characteristics to meet the benchmark is compared between the case where the etching parameter optimization cycle is used and the case where it is not used. Here, the number of trials refers to the following behavior: the etching process is performed by changing the etching parameters so as to approach the expected electrical characteristics and then measuring the electrical characteristics. Figure 6 This is a correlation diagram between the number of tests and the device failure rate. The vertical axis represents the device failure rate based on the inspection of electrical characteristics, and the horizontal axis represents the number of trials for etching the sample. The relationship between the number of trials for reaching the threshold value for the electrical characteristics to meet the benchmark and be judged as OK and the optimized cycle operation is expressed by equation 6.

[0087] R (optimization cycle fully used) < S (no optimization cycle used) ... Formula 6

[0088] According to Formula 6, the use of the optimization cycle helps to reduce the number of trials and can reduce the total cost including labor costs and equipment operating costs.

[0089] In the first embodiment, an example of optimizing etching parameters for each etching step corresponding to the depth direction of the etching shape using the difference in spatial characteristic quantities is shown. Here, the description is based on the characteristic quantities calculated from the secondary electron distribution in CD-SEM, but the same etching parameter optimization process can also be applied to the characteristic quantities calculated from the interference light distribution in OCD.

[0090] [Second embodiment]

[0091] In the first embodiment, the part that takes the most time is the part that optimizes the etching parameters according to the amount of change in the feature quantity. In the second embodiment, an example in which machine learning is used in the processing of the optimization part is described. Figure 7 This is a conceptual diagram of a prediction model of optimal etching parameters in the second embodiment.

[0092] In the database (DB1), data such as measured feature quantities, feature quantity differences, etching parameters Ep, plasma characteristics, and sample temperature are stored in an interrelated form. The stored data can be used as existing experimental data, and the functions Z and W in the figure can be obtained using a machine learning method. If the change in the feature quantity is input to Z and W, the optimal etching parameter Ep can be obtained. Alternatively, even if Z and W are not directly obtained, the change in the feature quantity Ep that reproduces the target shape can be estimated by effectively using AI.

[0093] As mentioned above, although embodiment of this invention was described, this invention is not limited to the said embodiment, Various changes are possible within the range which does not deviate from the summary of this invention.

[0094] For example, in the above embodiments, the server is described as a structural element for performing etching parameter control, but it may be a virtual area, a PC terminal, or a mobile terminal instead of a server, and the communication method is not only SMB communication, but FTP communication, NFS communication, etc. also have the same effect.

[0095] Furthermore, in the above embodiment, an example in which the optimization of etching parameters is executed by a server has been described, but the optimization may be executed by an application installed on a platform provided in the semiconductor device manufacturing system.

[0096] In the above embodiments, an etching apparatus is described as an example of a semiconductor manufacturing apparatus. However, the same effects are also achieved for other manufacturing apparatuses such as a plasma CVD apparatus, an ashing apparatus, a surface modification apparatus, and the like.

[0097] Furthermore, in the above embodiments, CD-SEM is used as an example of a semiconductor inspection device, but the same effects can be achieved with other inspection devices such as OCD, TEM, and XRR.

[0098] The following describes the embodiments that may be the contents of the present invention, but is not limited thereto.

[0099] (Method 1)

[0100] A semiconductor device manufacturing system controls etching parameters so as to obtain a desired processing result of a semiconductor manufacturing device. The semiconductor device manufacturing system is characterized in that the etching parameters are optimized in at least one etching step based on correlation data between the etching parameters and the change in characteristic quantity, and the obtained change in characteristic quantity, wherein the characteristic quantity is a value obtained from secondary electron data or interference light data from the surface of a sample, the change in characteristic quantity is the difference between the characteristic quantity and a target value of the characteristic quantity, the obtained change in characteristic quantity is the change in characteristic quantity obtained for each depth of an etching shape formed by the semiconductor manufacturing device, and the etching steps are each step of the processing conditions of the semiconductor manufacturing device corresponding to each depth of an etching shape formed by the semiconductor manufacturing device.

[0101] (Method 2)

[0102] A semiconductor device manufacturing system controls etching parameters to obtain a desired processing result of a semiconductor manufacturing device. The semiconductor device manufacturing system is characterized in that the etching parameters are optimized in at least one etching step based on correlation data between the temperature or plasma characteristics of the sample and the change in a characteristic quantity, and the obtained change in the characteristic quantity, wherein the characteristic quantity is a value obtained from secondary electron data or interference light data from the surface of the sample, the change in the characteristic quantity is the difference between the characteristic quantity and a target value of the characteristic quantity, the obtained change in the characteristic quantity is the change in the characteristic quantity obtained for each depth of an etching shape formed by the semiconductor manufacturing device, and the etching steps are each step of the processing conditions of the semiconductor manufacturing device corresponding to each depth of an etching shape formed by the semiconductor manufacturing device.

[0103] (Method 3)

[0104] The semiconductor device manufacturing system according to aspect 1 or 2, characterized in that the optimization of the etching parameters is to optimize an instruction value given to a temperature control system of a sample stage on which the sample is placed.

[0105] (Method 4)

[0106] A semiconductor device manufacturing system according to any one of methods 1 to 3, characterized in that the optimization of the etching parameters is to optimize the indicated value of a high-frequency power supply that supplies high-frequency power for generating plasma, or to optimize the indicated value of a high-frequency power supply that supplies high-frequency power to a sample stage carrying the sample.

[0107] (Method 5)

[0108] The semiconductor device manufacturing system according to any one of aspects 1 to 4, wherein the characteristic amount is obtained while changing a voltage applied to primary electrons of a CD-SEM.

[0109] (Method 6)

[0110] The semiconductor device manufacturing system according to any one of aspects 1 to 5, wherein machine learning is used to estimate an etching parameter corresponding to the amount of change in the feature amount.

[0111] (Method 7)

[0112] The semiconductor device manufacturing system according to any one of aspects 1 to 6, wherein the amount of change in the feature value is a difference between the feature value at an edge of the sample and the feature value at a center of the sample.

[0113] (Method 8)

[0114] The semiconductor device manufacturing system according to any one of aspects 1 to 7, characterized in that the semiconductor device manufacturing system includes a platform on which an application for executing the optimization of the etching parameters is installed.

[0115] (Method 9)

[0116] A semiconductor device manufacturing method controls etching parameters so as to obtain a desired processing result of a semiconductor manufacturing device, the semiconductor device manufacturing method is characterized in that it has the following steps: obtaining a feature value from secondary electron data or interference light data from the surface of a sample; and optimizing the etching parameters in at least one etching step based on correlation data between the etching parameters and the change in the feature value, and the change in the obtained feature value, wherein the change in the feature value is the difference between the obtained feature value and a target value of the feature value, the change in the obtained feature value is the change in the feature value obtained for each depth of an etching shape formed by the semiconductor manufacturing device, and the etching steps are each step of the processing conditions of the semiconductor manufacturing device corresponding to each depth of an etching shape formed by the semiconductor manufacturing device.

[0117] (Method 10)

[0118] A semiconductor device manufacturing method controls etching parameters to obtain a desired processing result of a semiconductor manufacturing device, the semiconductor device manufacturing method is characterized in that it has the following steps: obtaining a characteristic value from secondary electron data or interference light data from the surface of a sample; and optimizing the etching parameters in at least one etching step based on correlation data between the temperature or plasma characteristics of the sample and the change in the characteristic value, and the change in the characteristic value obtained, wherein the change in the characteristic value is the difference between the obtained characteristic value and a target value of the characteristic value, the change in the characteristic value obtained is the change in the characteristic value obtained for each depth of an etching shape formed by the semiconductor manufacturing device, and the etching steps are each step of the processing conditions of the semiconductor manufacturing device corresponding to each depth of an etching shape formed by the semiconductor manufacturing device.

[0119] (Method 11)

[0120] A server is provided in the semiconductor device manufacturing system according to any one of the aspects 1 to 8, and performs the optimization of the etching parameters.

[0121] -Description of Reference Numerals-

[0122] 10 ... semiconductor device manufacturing system, 101 ... PC for CD-SEM, 102 ... PC for semiconductor manufacturing equipment, 103 ... server, 104 ... semiconductor inspection equipment, 105 ... semiconductor manufacturing equipment.

Claims

1. A semiconductor device manufacturing system that controls etching parameters so as to achieve a desired processing result of a semiconductor manufacturing device. The semiconductor device manufacturing system is characterized in that Based on the correlation data between the etching parameters and the variation of the characteristic quantity and the obtained variation of the characteristic quantity, the etching parameters are optimized in at least one etching step. The characteristic quantity is a value obtained from secondary electron data or interference light data from the surface of the sample, The change amount of the feature quantity is the difference between the feature quantity and the target value of the feature quantity. The acquired variation of the characteristic quantity is the variation of the characteristic quantity acquired for each depth of the etching shape formed by the semiconductor manufacturing apparatus. The etching steps are steps of processing conditions of the semiconductor manufacturing apparatus corresponding to each depth of an etching shape formed by the semiconductor manufacturing apparatus.

2. A semiconductor device manufacturing system that controls etching parameters so as to achieve a desired processing result of a semiconductor manufacturing device, The semiconductor device manufacturing system is characterized in that Based on the data related to the temperature or plasma characteristics of the sample and the variation of the characteristic quantity, and the obtained variation of the characteristic quantity, the optimization of the etching parameters is performed in at least one etching step, The characteristic quantity is a value obtained from secondary electron data or interference light data from the surface of the sample, The change amount of the feature quantity is the difference between the feature quantity and the target value of the feature quantity. The acquired variation of the characteristic quantity is the variation of the characteristic quantity acquired for each depth of the etching shape formed by the semiconductor manufacturing apparatus. The etching steps are steps of processing conditions of the semiconductor manufacturing apparatus corresponding to each depth of an etching shape formed by the semiconductor manufacturing apparatus.

3. The semiconductor device manufacturing system according to claim 1 or claim 2, in, The optimization of the etching parameters is to optimize the instruction value given to the temperature control system of the sample stage on which the sample is placed.

4. The semiconductor device manufacturing system according to claim 1 or claim 2, in, The optimization of the etching parameters is to optimize the instruction value of the high frequency power supply for supplying high frequency power for generating plasma, or to optimize the instruction value of the high frequency power supply for supplying high frequency power to the sample stage on which the sample is placed.

5. The semiconductor device manufacturing system according to claim 1 or claim 2, in, The characteristic amount is obtained while changing the voltage applied to the primary electrons of the CD-SEM.

6. The semiconductor device manufacturing system according to claim 1 or claim 2, in, Machine learning is used to estimate etching parameters corresponding to the amount of change in the characteristic amount.

7. The semiconductor device manufacturing system according to claim 1 or claim 2, in, The change amount of the feature amount is the difference between the feature amount at the edge of the sample and the feature amount at the center of the sample.

8. The semiconductor device manufacturing system according to claim 1 or claim 2, in, The semiconductor device manufacturing system includes a platform on which an application for performing the optimization of the etching parameters is installed.

9. A method for manufacturing a semiconductor device, comprising controlling etching parameters so as to obtain a desired processing result of a semiconductor manufacturing device. The semiconductor device manufacturing method is characterized by comprising the following steps: acquiring a characteristic quantity from secondary electron data or interference light data from a surface of a sample; and Based on the correlation data between the etching parameters and the variation of the characteristic quantity and the obtained variation of the characteristic quantity, optimizing the etching parameters in at least one etching step is performed. The change amount of the feature quantity is the difference between the acquired feature quantity and the target value of the feature quantity. The acquired variation of the characteristic quantity is the variation of the characteristic quantity acquired for each depth of the etching shape formed by the semiconductor manufacturing apparatus. The etching steps are steps of processing conditions of the semiconductor manufacturing apparatus corresponding to each depth of an etching shape formed by the semiconductor manufacturing apparatus.

10. A method for manufacturing a semiconductor device, comprising controlling etching parameters so as to obtain a desired processing result of a semiconductor manufacturing device. The semiconductor device manufacturing method is characterized by comprising the following steps: acquiring a characteristic quantity from secondary electron data or interference light data from a surface of a sample; and Based on the data related to the temperature or plasma characteristics of the sample and the change amount of the characteristic quantity, and the change amount of the characteristic quantity obtained, optimizing the etching parameters in at least one etching step, The change amount of the feature quantity is the difference between the acquired feature quantity and the target value of the feature quantity. The acquired variation of the characteristic quantity is the variation of the characteristic quantity acquired for each depth of the etching shape formed by the semiconductor manufacturing apparatus. The etching steps are steps of processing conditions of the semiconductor manufacturing apparatus corresponding to each depth of an etching shape formed by the semiconductor manufacturing apparatus.

11. A server, It is characterized in that The method is provided in the semiconductor device manufacturing system according to claim 1 or claim 2, and performs optimization of the etching parameters.

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