Photoresist process parameter compensation method and compensation control system

Abstract

The invention provides a photoresist process parameter compensation method and a compensation control system, and the method comprises the steps: building a Lasso regression model through collected process parameters in a plurality of batches of photoresist processes, and carrying out the data training, the method comprises the following steps: reversely analyzing a nonlinear influence mechanism of an external environment temperature on the photoresist thickness through a model, then obtaining a compensation rule of a real-time environment temperature through a Lasso regression model, and dynamically adjusting a photoresist spin-coating rotating speed in a current batch photoresist process so as to maintain the thickness of the current batch photoresist to be constant; according to the method, the stability of the critical dimension (CD) of the photoetching process is improved, a photoetching process window is expanded (the exposure dose fluctuation range of a photoetching machine is widened and the debugging cost of the photoetching process is reduced), accurate temperature compensation of the photoetching process is realized, so that the no-load energy consumption of the photoetching machine is reduced, and the product yield and the reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor photolithography technology, and in particular to a photoresist process parameter compensation method and a compensation control system. Background Art

[0002] In the current photoresist process, process parameters are usually black-boxed. This is mainly reflected in the following: manufacturers treat key parameters such as photoresist spin-coating speed, exposure energy, and external ambient temperature as black boxes. The optimization of the target photoresist spin-coating thickness relies on empirical trial and error, which is inefficient and difficult to break through the limit of CD stability (±1.5nm).

[0003] In addition, there is still a problem of missing external ambient temperature effect in the current photoresist process. The existing photoresist parameter model does not systematically reveal the nonlinear coupling effect of external ambient temperature on photoresist thickness. It only focuses on the polynomial relationship between photoresist spin coating speed and photoresist thickness, and ignores the quadratic effect of external ambient temperature on photoresist thickness distribution. Figure 1 , Figure 1 This is a schematic diagram comparing the nonlinear coupling effect of external ambient temperature (TEM) on photoresist thickness (THK) under two groups of photoresist spin-coating speed scenarios in the prior art, where speed is the photoresist spin-coating speed, TEM is the external ambient temperature, and THK is the photoresist thickness. The photoresist spin-coating speed of the left group is 985 rpm / min; the photoresist spin-coating speed of the right group is 996 rpm / min. It can be seen from the figure that the photoresist thickness shrinks significantly under low temperature environment. Summary of the Invention

[0004] The present application provides a photoresist process parameter compensation method and compensation control system, which can solve at least one of the following problems: the optimization of the target spin coating thickness of the photoresist in the current photoresist process relies on empirical trial and error; and the existing photoresist parameter model ignores the influence of the external ambient temperature on the photoresist thickness.

[0005] On the one hand, an embodiment of the present application provides a method for compensating photoresist process parameters, comprising:

[0006] Collecting process parameters in multiple batches of photoresist processes, the process parameters including at least: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness;

[0007] According to the process parameters, a Lasso regression model is established and data training is performed, and a nonlinear effect of the real-time ambient temperature on the photoresist thickness is quantified through multiple features to obtain a predicted value of the photoresist thickness;

[0008] The compensation rule of the real-time ambient temperature is obtained according to the Lasso regression model, and the photoresist spin coating speed in the current batch of photoresist processes is dynamically adjusted to maintain a constant photoresist thickness in the current batch of photoresist processes.

[0009] Optionally, in the photoresist process parameter compensation method, in any batch of photoresist processes, the real-time ambient temperature fluctuates.

[0010] Optionally, in the photoresist process parameter compensation method, in the process of training the process parameters using a Lasso regression model, the Lasso regression model is:

[0011] THK prediction =β0+β1×Speed collect +β2×TEM collect +β3×(Speed collect ×

[0012] TEM collect )+β4×TEM collect 2 ,

[0013] Among them, THK prediction is the predicted value of the photoresist thickness; β0 is the intercept term; β1, β2, β3, and β4 are all characteristic coefficients; Speed collect The photoresist spin coating speed collected in multiple batches of photoresist processes; TEM collect Real-time ambient temperature collected for multiple photoresist batches.

[0014] Optionally, in the photoresist process parameter compensation method, in the process of dynamically adjusting the photoresist spin coating speed in the current batch of photoresist processes by obtaining the compensation rule of the real-time ambient temperature according to the Lasso regression model, the dynamic adjustment formula of the photoresist spin coating speed in the current batch of photoresist processes is:

[0015] Speed actual =(THK target -β0-β2×TEM current -β4×TEM current 2 ) / (β1+β3

[0016] ×TEM current ),

[0017] Among them, β0 is the intercept term; β1, β2, β3, and β4 are characteristic coefficients; Speed actual is the actual photoresist spin coating speed in the current batch photoresist process; THK targetis the target thickness of the photoresist in the current batch photoresist process; TEM current It is the current real-time ambient temperature in the current batch of photoresist process.

[0018] Optionally, in the photoresist process parameter compensation method, in the Lasso regression model, the characteristics of the non-real-time ambient temperature are suppressed by L1 regularization.

[0019] Optionally, in the photoresist process parameter compensation method, the response delay time of dynamically adjusting the photoresist spin coating rotation speed in the current batch photoresist process is less than 100ms.

[0020] On the other hand, an embodiment of the present application further provides a photoresist process parameter compensation control system, including:

[0021] Acquisition module, used to collect real-time ambient temperature in all batches of photoresist process;

[0022] A prediction engine module is used to establish a Lasso regression model and perform data training based on the collected process parameters of multiple batches of photoresist processes, quantify the nonlinear effect of the real-time ambient temperature on the photoresist thickness through multiple features, and obtain a predicted value of the photoresist thickness, wherein the process parameters include at least: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness;

[0023] A dynamic compensation module is used to obtain a compensation rule for the real-time ambient temperature according to the Lasso regression model, and dynamically adjust the photoresist spin coating speed in the current batch of photoresist processes to maintain a constant photoresist thickness in the current batch of photoresist processes.

[0024] Optionally, in the photoresist process parameter compensation control system, the response delay time of the dynamic compensation module is less than 100ms.

[0025] The technical solution of this application has at least the following advantages:

[0026] The present application provides a photoresist process parameter compensation method and a compensation control system. In the photoresist process parameter compensation method, a Lasso regression model is established and data training is performed by collecting process parameters from multiple batches of photoresist processes. The nonlinear influence mechanism of the external ambient temperature on the photoresist thickness is reversely analyzed through the model. Subsequently, the compensation rule of the real-time ambient temperature is obtained through the Lasso regression model, and the photoresist spin coating speed in the current batch of photoresist processes is dynamically adjusted to maintain the current batch of photoresist thickness constant, thereby improving the critical dimension (CD) stability of the photolithography process, expanding the photolithography process window (relaxing the exposure dose fluctuation range of the photolithography machine and reducing the photolithography process debugging cost), realizing precise temperature compensation of the photolithography process, thereby reducing the no-load energy consumption of the photolithography machine, and improving product yield and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 1 is a schematic diagram comparing the nonlinear coupling effect of the external environment temperature (TEM) on the photoresist thickness (THK) under two sets of photoresist spin coating speed scenarios in the prior art;

[0029] Figure 2 is a flow chart of a method for compensating photoresist process parameters according to an embodiment of the present invention;

[0030] Figure 3 It is a block diagram of a photoresist process parameter compensation control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following is a clear and complete description of the technical solutions in this application in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0032] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0033] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to internal connections between two components; they can refer to wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0034] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0035] The present invention provides a method for compensating photoresist process parameters. Figure 2 , Figure 2 4 is a flow chart of a method for compensating photoresist process parameters according to an embodiment of the present invention, wherein the method comprises:

[0036] First, step S1 is performed: collecting process parameters in multiple batches of photoresist processes, wherein the process parameters at least include: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness.

[0037] It is worth noting that in any batch of photoresist process, the real-time ambient temperature fluctuates.

[0038] Then, step S2 is performed: according to the process parameters, a Lasso regression model is established and data training is performed, and a nonlinear effect of the real-time ambient temperature on the photoresist thickness is quantified through multiple features to obtain a predicted value of the photoresist thickness.

[0039] In the process of training the process parameters using the Lasso regression model, the Lasso regression model is:

[0040] THK prediction =β0+β1×Speed collect +β2×TEMcollect +β3×(Speed collect ×

[0041] TEM collect )+β4×TEM collect 2 ,

[0042] Among them, THK prediction is the predicted value of the photoresist thickness; β0 is the intercept term; β1, β2, β3, and β4 are all characteristic coefficients; Speed collect The photoresist spin coating speed collected in multiple batches of photoresist processes; TEM collect Real-time ambient temperature collected for multiple photoresist batches.

[0043] It can be seen that in the above Lasso regression model, the features include: Speed collect TEM collect 、Speed collect ×TEM collect and TEM collect 2 , the target variable is THK prediction .

[0044] Preferably, in the Lasso regression model, the feature of the non-real-time ambient temperature is suppressed by L1 regularization.

[0045] Finally, step S3 is performed: a compensation rule of the real-time ambient temperature is obtained according to the Lasso regression model, and the photoresist spin coating speed in the current batch of photoresist processes is dynamically adjusted to maintain a constant photoresist thickness in the current batch of photoresist processes.

[0046] Specifically, in the process of dynamically adjusting the photoresist spin coating speed in the current batch of photoresist processes by obtaining the compensation rule of the real-time ambient temperature according to the Lasso regression model, based on the formula of the above-mentioned Lasso regression model, the dynamic adjustment formula of the photoresist spin coating speed in the current batch of photoresist processes is:

[0047] Speed actual =(THK target -β0-β2×TEM current -β4×TEM current 2 ) / (β1+β3

[0048] ×TEM current ),

[0049] Among them, β0 is the intercept term; β1, β2, β3, and β4 are characteristic coefficients; Speed actualis the actual photoresist spin coating speed in the current batch photoresist process; THK target is the target thickness of the photoresist in the current batch photoresist process; TEM current It is the current real-time ambient temperature in the current batch of photoresist process.

[0050] Preferably, the response delay time of dynamically adjusting the photoresist spin coating speed in the current batch photoresist process is less than 100ms.

[0051] Based on the same inventive concept, the embodiment of the present application also provides a photoresist process parameter compensation control system, referring to Figure 3 , Figure 3 : is a block diagram of a photoresist process parameter compensation control system according to an embodiment of the present invention, wherein the photoresist process parameter compensation control system comprises:

[0052] The acquisition module 10 is used to collect the real-time ambient temperature of all batches of photoresist processes;

[0053] A prediction engine module 20 is configured to establish a Lasso regression model and perform data training based on the collected process parameters of multiple batches of photoresist processes, quantify the nonlinear effect of the real-time ambient temperature on the photoresist thickness through multiple features, and obtain a predicted value of the photoresist thickness, wherein the process parameters include at least: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness;

[0054] The dynamic compensation module 30 is used to obtain the compensation rule of the real-time ambient temperature according to the Lasso regression model, and dynamically adjust the photoresist spin coating speed in the current batch of photoresist processes to maintain a constant photoresist thickness in the current batch of photoresist processes.

[0055] Preferably, the response delay time of the dynamic compensation module 30 is less than 100ms.

[0056] In this application, a Lasso regression model is established and data training is performed by collecting process parameters from multiple batches of photoresist processes. The Lasso regression model is used to reversely analyze the nonlinear influence mechanism of the external ambient temperature on the photoresist thickness. Subsequently, the Lasso regression model is used to obtain the compensation rule for the real-time ambient temperature, and the photoresist spin coating speed in the current batch of photoresist processes is dynamically adjusted to maintain the current batch of photoresist thickness constant, thereby improving the critical dimension (CD) stability of the photolithography process and expanding the photolithography process window: widening the exposure dose fluctuation range of the photolithography machine and reducing the photolithography process debugging cost, realizing precise temperature compensation of the photolithography process, thereby reducing the no-load energy consumption of the photolithography machine, and improving product yield and reliability.

[0057] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of this application.

Claims

1. A method for compensating photoresist process parameters, characterized in that: include: Collecting process parameters in multiple batches of photoresist processes, the process parameters including at least: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness; According to the process parameters, a Lasso regression model is established and data training is performed, and a nonlinear effect of the real-time ambient temperature on the photoresist thickness is quantified through multiple features to obtain a predicted value of the photoresist thickness; The compensation rule of the real-time ambient temperature is obtained according to the Lasso regression model, and the photoresist spin coating speed in the current batch of photoresist processes is dynamically adjusted to maintain a constant photoresist thickness in the current batch of photoresist processes.

2. The photoresist process parameter compensation method according to claim 1, characterized in that: In any batch of photoresist process, the real-time ambient temperature fluctuates.

3. The photoresist process parameter compensation method according to claim 1, characterized in that: In the process of training the process parameters using the Lasso regression model, the Lasso regression model is: THK prediction =β0+β1×Speed collect +β2×TEM collect +β3×(Speed collect × HE HAS collect )+β4×TEM collect 2 , Among them, THK prediction is the predicted value of the photoresist thickness; β0 is the intercept term; β1, β2, β3, and β4 are all characteristic coefficients; Speed collect The photoresist spin coating speed collected in multiple batches of photoresist processes; TEM collect Real-time ambient temperature collected for multiple photoresist batches.

4. The photoresist process parameter compensation method according to claim 3, characterized in that: In the process of dynamically adjusting the photoresist spin coating speed in the current batch of photoresist processes by obtaining the compensation rule of the real-time ambient temperature according to the Lasso regression model, the dynamic adjustment formula of the photoresist spin coating speed in the current batch of photoresist processes is: Speed actual =(THK target -β0-β2×TEM current -β4×TEM current 2 ) / (β1+β3 ×TEM current ), Among them, β0 is the intercept term; β1, β2, β3, and β4 are characteristic coefficients; Speed actual is the actual photoresist spin coating speed in the current batch photoresist process; THK target is the target thickness of the photoresist in the current batch photoresist process; TEM current It is the current real-time ambient temperature in the current batch of photoresist process.

5. The photoresist process parameter compensation method according to claim 1, characterized in that: In the Lasso regression model, the features of non-real-time ambient temperature are suppressed by L1 regularization.

6. The photoresist process parameter compensation method according to claim 1, characterized in that: The response delay time for dynamically adjusting the photoresist spin coating speed in the current batch photoresist process is less than 100ms.

7. A photoresist process parameter compensation control system, characterized in that: include: Acquisition module, used to collect real-time ambient temperature in all batches of photoresist process; A prediction engine module is used to establish a Lasso regression model and perform data training based on the collected process parameters of multiple batches of photoresist processes, quantify the nonlinear effect of the real-time ambient temperature on the photoresist thickness through multiple features, and obtain a predicted value of the photoresist thickness, wherein the process parameters include at least: photoresist spin coating speed, real-time ambient temperature, and photoresist thickness; A dynamic compensation module is used to obtain a compensation rule for the real-time ambient temperature according to the Lasso regression model, and dynamically adjust the photoresist spin coating speed in the current batch of photoresist processes to maintain a constant photoresist thickness in the current batch of photoresist processes.

8. The photoresist process parameter compensation control system according to claim 7, characterized in that: The response delay time of the dynamic compensation module is less than 100ms.

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