A low power etching method for reducing plasma damage

Abstract

The application relates to the field of semiconductor etching technology, and provides a low-power etching method for reducing plasma damage, characterized by comprising the following steps: providing a to-be-etched substrate in a chamber of a plasma processing device, wherein a surface of the to-be-etched substrate at least comprises a to-be-etched layer and a mask layer; introducing a process gas into the chamber; applying pulsed source power to generate plasma in the chamber, wherein the pulsed source power comprises alternating on periods and off periods; applying bias power to the substrate during the on periods; establishing an etching model and obtaining an optimal etching parameter combination by using a genetic algorithm; and etching the to-be-etched layer by using the plasma. By using the pulsed source power and the genetic algorithm to optimize the etching parameters, the effect of reducing plasma damage and improving etching uniformity is achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of semiconductor etching, and particularly relates to a low-power etching method for reducing plasma damage. BACKGROUND

[0002] In the field of semiconductor manufacturing, plasma etching is a key process widely used in pattern transfer. However, the traditional etching method usually uses a continuous power plasma source, which is easy to cause substrate heat accumulation, charge accumulation and physical bombardment damage, thereby affecting device performance, reliability and yield. Especially for advanced node devices, plasma damage can cause material defects, critical dimension variation or electrical performance degradation. Therefore, it is urgent to develop a low-power etching method to minimize plasma damage while maintaining high etching efficiency and uniformity. SUMMARY

[0003] In view of the above defects of the prior art, the present application provides a low-power etching method for reducing plasma damage. The technical solution of the present application comprises the following steps:

[0004] S1: providing a substrate to be etched in a chamber of a plasma processing device, wherein the surface of the substrate to be etched comprises at least a layer to be etched and a mask layer;

[0005] S2: introducing a process gas into the chamber;

[0006] S3: applying a pulsed source power to generate plasma in the chamber, wherein the pulsed source power comprises alternating on and off periods;

[0007] S4: applying a bias power to the substrate during the on period;

[0008] S5: establishing an etching model and obtaining an optimal etching parameter combination using a genetic algorithm;

[0009] S6: etching the layer to be etched using the plasma.

[0010] Preferably, the ratio of the on period to the off period in S3 is adjusted based on the optimal etching parameter combination to minimize plasma damage.

[0011] Preferably, S5 comprises:

[0012] S51: collecting historical etching process parameters and corresponding etching result data;

[0013] S52: establishing an etching model based on the etching result data and calculating etching energy density;

[0014] S53: performing parameter optimization operation on etching energy density;

[0015] S54: Calculate the optimized parameters using a genetic algorithm to obtain the best etching parameter combination.

[0016] Preferably, the etching model in S52 includes:

[0017] A finite element model of a single etching process is established using finite element analysis software. The substrate size of the model is 100mm x 100mm x 10mm, and the etching area size is 5mm x 5mm x 1mm.

[0018] Preferably, the calculation of the etching energy density in S52 is as follows:

[0019]

[0020] In the formula, is the etching energy density, is the comprehensive process coefficient, is the source power, is the pulse on time, is the etching area, is a dimensionless function describing the spatial distribution of plasma in the etching area.

[0021] Preferably, the parameter optimization operation on the etching energy density in S53 includes:

[0022] The etching energy density is converted into a function expression of process parameters, as follows:

[0023]

[0024] In the formula, is the etching energy density, is the average source power, is the total etching time, is the etching area.

[0025] Preferably, the parameter optimization operation on the etching energy density in S53 further includes:

[0026] The source power and bias power are taken as independent variables, and the etching uniformity error is taken as the response value to establish a response surface model, as follows:

[0027]

[0028] In the formula, is the constant term coefficient, is the first-order term coefficient, is the second-order term coefficient, is the cross term coefficient, is the number of independent variables, is the standardized value of the i th etching process parameter, is the standardized value of the j th etching process parameter.

[0029] Preferably, the parameter optimization operation on the etching energy density in S53 further comprises:

[0030] The variance of the response surface model is verified, and the formula is as follows:

[0031]

[0032]

[0033] In the formula, is the adjustment determination coefficient, is the prediction determination coefficient, is the error sum of squares, is the total sum of squares, is the error degree of freedom, is the prediction error sum of squares.

[0034] Beneficial effects:

[0035] 1. The present application reduces the risk of damage caused by heat and charge accumulation by applying pulsed source power, including alternating on and off periods, during which the plasma density drops sharply or is extinguished, effectively reducing the time the substrate is exposed to plasma.

[0036] 2. The present application automatically collects historical process data and optimizes etching parameters based on etching models and genetic algorithms to minimize etching uniformity error and damage indicators, improving process precision and repeatability.

[0037] 3. The present application combines finite element analysis, etching energy density calculation and response surface model to realize accurate simulation and control of the etching process, ensuring uniform etching energy distribution, further improving etching quality and reducing local damage. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flowchart of a preferred embodiment of the present application. DETAILED DESCRIPTION

[0039] The embodiments of the present application are described in detail below. The following embodiments are implemented on the premise of the technical solutions of the present application, and detailed implementation methods and specific operation processes are given, but the scope of protection of the present application is not limited to the following embodiments.

[0040] The present application designs a low-power etching method for reducing plasma damage, such as Figure 1As shown, the technical solution comprises the following steps, specifically comprising:

[0041] S1: providing a substrate to be etched in a chamber of a plasma processing device, the surface of the substrate to be etched comprising at least a layer to be etched and a mask layer;

[0042] S2: introducing a process gas into the chamber;

[0043] S3: applying pulsed source power to generate plasma in the chamber, the pulsed source power comprising alternating on-periods and off-periods;

[0044] S4: applying a bias power to the substrate during the on-periods;

[0045] S5: establishing an etching model and obtaining an optimal etching parameter combination using a genetic algorithm;

[0046] S6: etching the layer to be etched using the plasma.

[0047] Specifically, the mask layer covers part of the area of the layer to be etched. During the etching process, the mask layer can resist the bombardment and chemical reaction of the plasma, thereby protecting the layer to be etched below from being etched. The area of the layer to be etched not covered by the mask layer is gradually removed by the plasma. Therefore, the pattern of the mask layer is finally transferred to the layer to be etched below, forming the required structure. Common mask layer materials include photoresist, silicon dioxide, silicon nitride, etc. The off-periods of the pulsed source power are crucial for reducing plasma damage. During the off-periods, the plasma density in the chamber drops sharply or is completely extinguished.

[0048] Preferably, the ratio of the on-periods and off-periods in S3 is adjusted based on the optimal etching parameter combination to minimize plasma damage.

[0049] Preferably, S5 comprises:

[0050] S51: collecting historical etching process parameters and corresponding etching result data;

[0051] S52: establishing an etching model based on the etching result data and calculating etching energy density;

[0052] S53: performing parameter optimization operation on the etching energy density;

[0053] S54: calculating the optimized parameters using a genetic algorithm to obtain the optimal etching parameter combination.

[0054] Preferably, the etching model in S52 comprises:

[0055] A finite element model of a single etching process was established using finite element analysis software. The substrate size of the model was 100mm×100mm×10mm, and the etching area size was 5mm×5mm×1mm.

[0056] Preferably, the formula for calculating the etching energy density in S52 is as follows:

[0057]

[0058] In the formula, For etching energy density, For the comprehensive process coefficient, For source power, For pulse on-time, The area of ​​the etched region. This is a dimensionless function describing the spatial distribution of plasma within the etched region.

[0059] Preferably, the parameter optimization operation for etching energy density in S53 includes:

[0060] The etching energy density is converted into a function of process parameters, as shown in the following formula:

[0061]

[0062] In the formula, For etching energy density, For average source power, Total etching time The area represents the etched region.

[0063] Preferably, the parameter optimization operation for etching energy density in S53 further includes:

[0064] Using source power and bias power as independent variables and etching uniformity error as the response value, a response surface model is established, as shown in the following formula:

[0065]

[0066] In the formula, The coefficient of the constant term, The coefficient of the linear term, The coefficient of the quadratic term, The cross term coefficient, The number of independent variables. Let be the standardized value of the i-th etching process parameter. This is the standardized value of the j-th etching process parameter.

[0067] Preferably, the parameter optimization operation for etching energy density in S53 further includes:

[0068] The variance of the response surface model is validated using the following formula:

[0069]

[0070]

[0071] In the formula, To adjust the coefficient of determination, To predict the coefficient of determination, For the sum of squared errors, For the total sum of squares, For the error degrees of freedom, This is the sum of squared prediction errors.

[0072] Specifically, for variance validation of the response surface model, the coefficient of determination is adjusted. First, we need to check whether the model's ability to interpret the known data is sufficient; this is fundamental. Second, we need to check the predictive determination coefficient. The key is to check whether the model's predictive ability for unknown data is reliable; only when a model simultaneously possesses high... and Only when we are certain that it is a robust and valuable model can we use it as an evaluation criterion for genetic algorithms to search for truly effective optimal process parameters. If the validation fails, we need to go back to the starting point, check the data, and adjust the model until the validation is successful.

[0073] In addition, a genetic algorithm is used to calculate the optimal combination of etching parameters, including: considering etching uniformity error. As the primary optimization objective, multi-objective optimization is performed in conjunction with other objectives such as etching rate or damage index. The etching process parameters are encoded as chromosomes in a genetic algorithm, and a set of parameter combinations is randomly generated as the initial population. For each individual (parameter combination), the etching uniformity error is calculated using a response surface model. The fitness function is defined as follows: Based on the fitness value, select and retain excellent individuals, cross select individuals with a certain probability (e.g., 0.8) to generate new individuals, and mutate individuals with a low probability (e.g., 0.1) to introduce random perturbation and avoid local optima. Repeat the steps until the maximum number of iterations is reached, and output the etching parameter combination corresponding to the best individual, that is, the parameter setting that minimizes the etching uniformity error.

[0074] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A low-power etching method for reducing plasma damage, characterized in that, include: S1: A substrate to be etched is provided in the cavity of a plasma processing apparatus, wherein the surface of the substrate to be etched includes at least a layer to be etched and a mask layer; S2: Introduce process gas into the chamber; S3: Apply pulsed source power to generate plasma in the chamber, the pulsed source power including alternating on and off periods; S4: During the turn-on period, a bias power is applied to the substrate; S5: Establish an etching model and use a genetic algorithm to obtain the optimal combination of etching parameters; S6: Use the plasma to etch the layer to be etched; The ratio of the on-time to the off-time in S3 is adjusted based on the optimal etching parameter combination to minimize plasma damage. Wherein, S5 includes: S51: Collect historical etching process parameters and corresponding etching result data; S52: Based on the etching result data, establish an etching model and calculate the etching energy density; S53: Perform parameter optimization operation on etching energy density; S54: The genetic algorithm is used to calculate the optimized parameters and obtain the best combination of etching parameters; The parameter optimization operation for etching energy density in S53 further includes: The variance of the response surface model is validated using the following formula: In the formula, To adjust the coefficient of determination, To predict the coefficient of determination, For the sum of squared errors, For the total sum of squares, For the error degrees of freedom, This is the sum of squared prediction errors; The parameter optimization operation for etching energy density in S53 further includes: Using source power and bias power as independent variables and etching uniformity error as the response value, a response surface model is established, as shown in the following formula: In the formula, The coefficient of the constant term, The coefficient of the linear term, The coefficient of the quadratic term, The cross term coefficient, The number of independent variables. Let be the standardized value of the i-th etching process parameter. This is the standardized value of the j-th etching process parameter.

2. The low-power etching method for reducing plasma damage according to claim 1, characterized in that, The etching model in S52 includes: A finite element model of a single etching process was established using finite element analysis software. The substrate size of the model was 100mm×100mm×10mm, and the etching area size was 5mm×5mm×1mm.

3. The low-power etching method for reducing plasma damage according to claim 1, characterized in that, The formula for calculating the etching energy density in S52 is as follows: In the formula, For etching energy density, For the comprehensive process coefficient, For source power, For pulse on-time, The area of ​​the etched region. This is a dimensionless function describing the spatial distribution of plasma within the etched region.

4. The low-power etching method for reducing plasma damage according to claim 1, characterized in that, The parameter optimization operation for etching energy density in S53 includes: The etching energy density is converted into a function of process parameters, as shown in the following formula: In the formula, For etching energy density, For average source power, Total etching time This represents the area of ​​the etched region.

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