CN115202163B - Method, apparatus and computer readable storage medium for selecting a photoresist model - Google Patents

Abstract

The present invention relates to the technical field of semiconductor manufacturing, and more particularly, to a method, device and computer-readable storage medium for selecting a photoresist model. The method includes: collecting the sampling position of the photoresist model on the mask layout; obtaining signal quantities of multiple types of signals at the sampling positions; for each type of signal, obtaining effect signals according to the signal quantities and the coordinates of the sampling positions; Calculate the total signal value of this type of signal based on the obtained effect signal; calculate the effect contribution ratio of this type of signal based on the total signal value of each type of signal; select the photoresist model according to the effect contribution ratio. The embodiments of the present invention can effectively analyze the effect contribution proportions of various signals, which is beneficial for the user to analyze the behavior of the current model, and iteratively obtain a better model.

Description

Method, device and computer-readable storage medium for selecting a photoresist model

technical field

Embodiments of the present invention mainly relate to the technical field of semiconductor manufacturing, and more particularly, to a method, device and computer-readable storage medium for selecting a photoresist model.

Background technique

OPC (Optical Proximity Correction) is a lithography enhancement technology. OPC is mainly used in the production process of semiconductor chips. The purpose is to ensure that the actual graphics obtained on the silicon wafer after exposure are consistent with the design graphics. If OPC optical proximity correction is not performed, the actual exposure graphics will be significantly different from the design graphics, such as the actual line width is narrower or wider than the design, these can be compensated for imaging by changing the mask; other distortions, such as rounded corners , Light intensity, restricted by the resolution of optical tools, it is even more difficult to compensate. These distortions, if not corrected, can significantly alter the electrical performance of the produced circuit. OPC optical proximity correction compensates for these distortions by moving the edges of the pattern on the reticle or adding additional polygons.

The OPC photoresist model simulates a series of optical effects and photoresist effects that occur in the lithography machine. Among them, the photoresistance effect is also divided into multiple different effects such as physics/chemistry, and there are corresponding effect signals respectively. Under the combined effect of these effects, the OPC photoresist model can simulate the actual behavior of the entire process from the light source to the mask, through the prism, and into the photoresist in the lithography machine as much as possible. However, in the prior art, the proportion of each effect in the total effect cannot be displayed intuitively to the user. For users, the proportion of each effect in the total effect is completely unknown, which is very unfavorable for users to analyze the behavior of the current model, iteratively obtain a better model, and master and accumulate relevant experience of each effect.

Contents of the invention

According to an exemplary embodiment of the present invention, a scheme for selecting a photoresist model is provided.

In the first aspect of the present invention, a method for selecting a photoresist model is provided, the method includes: collecting the sampling positions of the photoresist model on the mask layout; obtaining signal quantities of various types of signals at the sampling positions; For each type of signal, the effect signal is obtained according to the signal quantity and the coordinates of the sampling position; based on the obtained effect signal, the total signal value of this type of signal is calculated; based on the total signal value of each type of signal, the The effect contribution ratio of the signal; select the photoresist model according to the effect contribution ratio.

In a second aspect of the invention there is provided an electronic device comprising: a processor; and a memory coupled to the processor, the memory having instructions stored therein which when executed by the processor cause the device to perform actions. These actions include: collecting the sampling position of the photoresist model on the mask layout; obtaining the semaphore of multiple types of signals at the sampling position; for each type of signal, obtaining the effect signal according to the semaphore and the coordinates of the sampling position; based on Calculate the total signal value of this type of signal based on the obtained effect signal; calculate the effect contribution ratio of this type of signal based on the total signal value of each type of signal; select a photoresist model according to the effect contribution ratio.

In a third aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to the first aspect of the present invention.

In some embodiments, selecting the photoresist model according to the effect contribution ratio includes: determining a target signal among multiple types of signals; and selecting the photoresist model based on the effect contribution ratio of the target signal.

In some embodiments, selecting the photoresist model based on the effect contribution ratio of the target signal includes: selecting the photoresist model when the effect contribution ratio of the target signal is greater than or equal to a first preset threshold.

In other alternative embodiments, the photoresist model is selected based on the effect contribution ratio of the target signal, including:

Based on the following formula:

，

,

，

,

Taking the effect contribution ratio of the target signal as the input value, calculate the function value of the preset function; when the function value is less than or equal to the second preset threshold, select the photoresist model, where Rmse is the root mean square error, which is the light The square root of the ratio of the square sum of the deviation between the simulation value of the key dimension of the resistance model at each sampling position and the historical true value and the sampling number; Rmse_weight is the root mean square error weight; NT1 is the effect contribution ratio of the target signal, Da and Db is a preset constant, F(NT1) is an exponential function, and F(NT1)_weight is the weight of the exponential function.

In some embodiments, for each type of signal, obtaining the effect signal according to the semaphore and the sampling position includes: determining position coordinates of critical dimension (CD) positions at both ends of the sampling position; based on the determined position coordinates and each type A three-dimensional semaphore matrix corresponding to the signal of the type is formed; based on each three-dimensional semaphore matrix, the effect signal corresponding to the sampling position of the signal of the type is obtained.

In some embodiments, based on the determined position coordinates and the semaphore of each type of signal, forming a three-dimensional semaphore matrix corresponding to the type of signal includes: obtaining a central position of a critical dimension (CD) of the sampling location; Forming a rectangular matrix range based on the central position; dividing the matrix range at equal intervals to form a plurality of reference point coordinates; based on each type of signal, obtaining semaphore corresponding to the plurality of reference point coordinates; based on the acquired semaphore and The matrix range forms a three-dimensional semaphore matrix for signals of that type.

In a preferred embodiment, based on each three-dimensional semaphore matrix, obtaining the effect signal corresponding to the sampling position of this type of signal includes: for each sampling position, an interpolation algorithm is used to perform interpolation fitting based on the reference point coordinates to obtain The semaphores corresponding to the two ends of the sampling position; the average value of the semaphores corresponding to the two ends is used as the effect signal of the sampling position.

In some embodiments, calculating the total signal value of the type of signal based on the obtained effect signal includes: for each type of signal, obtaining the sum of the effect signals of all sampling positions; taking the sum of the effect signals as the type The total signal value of the signal.

In some embodiments, calculating the effect contribution ratio of each type of signal based on the total signal value of each type of signal includes: calculating the contribution value of each type of signal based on the total signal value; The contribution value of all signals is calculated as the total contribution value of all signals; the ratio of the contribution value of each type of signal to the total contribution value is taken as the effect contribution ratio of this type of signal.

In some embodiments, calculating the contribution value of each type of signal based on the total signal value includes: determining the effect coefficient of each type of signal based on the light resistance model; calculating the effect coefficient of each type of signal and the total signal value The product of is used as the contribution value of this type of signal.

In some embodiments, the target signal is an optical type signal.

In some embodiments, obtaining the target signal amount of the target signal includes: obtaining the target signal amount based on Fourier operations at least according to the light source type, mask diffraction and lens transfer function.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of this Summary, nor is it intended to limit the scope of this Summary. It should be understood that the content described in the Summary of the Invention is not intended to limit the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description.

Description of drawings

The above and other objects, features and advantages of embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, embodiments of the invention are shown by way of example and not limitation, in which:

Figure 1 shows a schematic diagram of an exemplary environment in which embodiments according to the invention can be implemented;

FIG. 2 shows a flow chart of a method for selecting a photoresist model based on a signal contribution ratio according to some embodiments of the invention;

FIG. 3 shows a top view of one region of a mask layout according to some embodiments of the present invention;

Fig. 4 shows the effect contribution ratio distribution diagram of various types of signals of photoresist model A according to some embodiments of the present invention;

Fig. 5 shows a signal graph of photoresist model A according to some embodiments of the present invention;

Fig. 6 shows the effect contribution ratio distribution diagram of various types of signals of photoresist model B according to some embodiments of the present invention;

Fig. 7 shows a signal graph of photoresist model B according to some embodiments of the present invention;

Fig. 8 shows a schematic block diagram of an example device 700 for implementing embodiments of the present invention.

detailed description

Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for exemplary purposes only, and are not intended to limit the protection scope of the present invention.

As used herein, the term "comprise" and its variants mean open inclusion, ie "including but not limited to". The term "or" means "and/or" unless otherwise stated. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment." The term "another embodiment" means "at least one further embodiment". The terms "first", "second", etc. may refer to different or the same object. Other definitions, both express and implied, may also be included below.

As briefly mentioned above, OPC optical proximity correction is mainly used in the production process of semiconductor chips to ensure that the actual graphics obtained on the silicon wafer after exposure are consistent with the design graphics. If OPC optical proximity correction is not performed, the actual exposure graphics will be significantly different from the design graphics, such as the actual line width is narrower or wider than the design, these can be compensated for imaging by changing the mask; other distortions, such as rounded corners , Light intensity, restricted by the resolution of optical tools, it is even more difficult to compensate. These distortions, if not corrected, can significantly alter the electrical performance of the produced circuit. OPC optical proximity correction compensates for these distortions by moving the edges of the pattern on the reticle or adding additional polygons. For this reason, it is necessary to simulate a series of optical effects and photoresist effects in the lithography machine in the OPC photoresist model, and present the effect contribution ratio of each type of effect signal to the user intuitively, which is very helpful for the user to analyze the current model. Behavior, obtain a better model iteratively, or choose a better model among multiple different models, and master and accumulate relevant experience of each effect.

According to an embodiment of the present invention, there is provided a method for selecting a photoresist model based on the effect contribution ratio, the method comprising: collecting sampling positions of the photoresist model on the mask layout; obtaining various types of signals at the sampling positions semaphore; for each type of signal, obtain the effect signal according to the semaphore and the coordinates of the sampling position; based on the obtained effect signal, calculate the total signal value of this type of signal; based on the total signal value of each type of signal , calculate the effect contribution ratio of this type of signal; select the photoresist model according to the effect contribution ratio.

The signals at the sampling positions of the mask layout usually include multiple types of signals as optical signals ai and non-optical signals, and by analyzing the proportions of optical signals and non-optical signals in the entire effect signal, each The proportion of effect contribution of similar signals is visually and directly presented to the user, so that some users can select a photoresist model suitable for the production process based on the analysis results of the proportion.

Embodiments of the present invention will be specifically described below with reference to the accompanying drawings. Referring to FIG. 1 , a schematic diagram of an exemplary environment 100 in which various embodiments of the present invention can be implemented is shown. As shown in FIG. 1 , the exemplary environment 100 includes a computing device 110 and a client 120 .

In some embodiments, computing device 110 may receive input messages from client 120 and output feedback messages to client 120 . In some embodiments, the photoresist model is input through the client 120 . The calculation device 110 can perform simulation calculations on the input photoresist model to determine the effect contribution ratios of various signals at the sampling positions, and output the analysis results to the client 120 .

In some embodiments, computing device 110 may include, but is not limited to, personal computers, server computers, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDAs), media players, etc.), consumer electronics, small Computers, mainframe computers, cloud computing resources, etc.

It should be understood that the structure and functionality of the exemplary environment 100 are described for illustrative purposes only and are not intended to limit the scope of the subject matter described herein. The subject matter described herein can be implemented in different structures and/or functions.

The technical solutions described above are only for example, not limiting the present invention. It should be understood that the exemplary environment 100 may also have various other implementations. In order to explain the principle of the solution of the present invention more clearly, the specific technical solution of the present invention will be described in more detail below with reference to FIG. 2 .

FIG. 2 shows a flowchart of a method for selecting a photoresist model according to some embodiments of the present invention. For example, the method 200 can be implemented by the computing device 110 as shown in FIG. 1 . It should be appreciated that method 200 may include additional blocks not shown and/or that certain blocks shown may be omitted. The scope of the invention is not limited herein.

At step 202, sampling positions of the photoresist model on the mask layout are collected. In a specific exemplary embodiment, taking the IMP layer (implant layer) as an example, it usually has more than 1000 sampling positions, and these sampling positions are distributed everywhere in the mask layout, and the The exact behavior of the model is undefined. In the following embodiments of the present invention, the 1865 sampling positions of the IMP layer are taken as an example. In specific use, they can be used in other layers, or other numbers of sampling positions can be selected, and the actual sampling positions of the present invention are not specifically limited. In an embodiment of the invention, all sampling locations on the mask layout are collected. It should be noted here that the number of sampling locations is not fixed, and the number of sampling locations and the selection of layers can be selected according to actual conditions. This embodiment is only exemplary and should not limit the present invention.

The sampling positions in the mask layout will be described below with reference to FIG. 3 . FIG. 3 shows a top view of one region of the mask layout, which is only exemplary. Those skilled in the art can foresee that the mask layout also includes other regions not shown, and the arrangement and position of the mask elements in the mask layout are not limited to the pattern shown in FIG. 3 . In this embodiment, for the sake of clarity, only one of the sampling positions is marked with Gauge_0, and the sampling position is only selected for the purpose of illustrating the two ends of the critical dimension (CD) position of the sampling position. The sampling position at this position is marked by a solid line, which, as can be seen from the figure, has two end positions at the sampling position, the distance between which represents the critical dimension.

Return to Figure 2 below. At step 204, semaphores of multiple types of signals at the sampling positions are obtained. As mentioned above, the signal at the sampling position usually includes multiple types of signals such as optical signal ai and non-optical signal. For example, in this embodiment, the signal is mainly divided into optical signal ai and multiple non-optical signals Signals, namely, colloid physics continuum A signal, colloid physics continuum B signal, colloid physics continuum C signal, colloid physics edge A signal and colloid physics B signal, colloid chemistry acid-base car signal. The enumeration of the above non-optical signals in this embodiment is only for the purpose of illustration, and may include other types of non-optical signals in addition to the above signals.

In some embodiments, the optical signal ai among these types of signals is used as the target signal, wherein calculating the target signal amount of the target signal includes: at least according to the light source type, mask diffraction and lens transfer function are calculated based on Fourier operations Output the target semaphore. In a specific embodiment, the target semaphore I(x, y) of the target signal can be calculated by the following formula (1).

（1），

(1),

Among them, x, y are the coordinates of the sampling position in the mask, j (f, g) is the type of light source, f, g are the abscissa and ordinate of the light source respectively, M is the mask diffraction, H is the lens transfer function, M* and H* are the complex conjugates of the mask diffraction M and the lens transfer function H, f', g' are the first offset of the abscissa f of the light source and the first offset of the ordinate g of the light source, f '', g'' are the second offset of the abscissa f of the light source and the second offset of the ordinate g of the light source, and i is the imaginary unit in the Fourier transformation formula;

由以下公式（2）计算获得。in

It is calculated by the following formula (2).

（2）

(2)

Then, in one embodiment, step 206 further includes: for each type of signal, obtaining an effect signal according to the signal quantity and the coordinates of the sampling position. Specifically, for the optical signal as the target signal, obtaining the effect signal according to the signal amount calculated above and the coordinates of the sampling position includes: determining the position coordinates of the critical dimension (CD) positions at both ends of the sampling position; based on the determined The location coordinates of each type of signal and the semaphore of each type of signal form a three-dimensional semaphore matrix corresponding to the type of signal; based on each three-dimensional semaphore matrix, the effect signal corresponding to the sampling position of the type of signal is obtained. The three-dimensional semaphore matrix describes the relationship between the coordinates of the sampling positions of various types of signals and their semaphore.

In order to form a three-dimensional semaphore matrix, it is first necessary to obtain the center position of the key dimension of the sampling position. Then, with this center position as a center, a predetermined distance is extended in the up-down and left-right directions orthogonal to each other. It should be noted that the sampling position is confirmed by the position of gauge_0, then the CDs at both ends of gauge_0 also correspond to the coordinate positions, therefore, the coordinates of the center position can be obtained according to the coordinate positions at both ends. In an exemplary example, a distance of 50 nm is extended from the central position, thereby obtaining a square matrix area with a side length of 100 nm. Next, each side length is divided with a pitch of 5nm, so as to obtain 400 reference points within the range of the matrix. The reference point coordinates of these reference points in the mask layout are determined, so the coordinates of the reference points in the mask layout and The coordinates in the three-dimensional semaphore matrix are in a one-to-one correspondence. When the coordinate position (x, y) is known, the semaphore of the target signal (optical signal ai) corresponding to these reference point coordinates can be calculated by the above formula Therefore, after measuring the sampling position, the coordinates of the corresponding reference point can be found in the three-dimensional semaphore matrix through the position coordinates of the sampling position of the target signal, so as to obtain the semaphores at both ends of the sampling position. It should be noted here that the selection of the above numerical values is only exemplary and not intended to limit the present invention, and other appropriate numerical values may be selected to limit the range of the three-dimensional semaphore matrix in an actual process.

In order to obtain the respective corresponding three-dimensional semaphore matrices for signals of these classes, it is necessary to base the previously determined reference point coordinates of the three-dimensional signal matrix for the target signal. Taking colloidal chemical acid-base car signal as an example, in order to calculate the signal amount of this type of signal, it is necessary to calculate the signal amount at each reference point of the three-dimensional signal matrix according to the following method, so that the coordinates of the three-dimensional signal matrix based on optical signals and through Each semaphore calculated in the following manner forms a three-dimensional semaphore matrix of the car signal. However, it should be understood that the method for calculating the semaphore shown here is only for the purpose of illustration, and the semaphore of other types of signals may be calculated by a suitable method, which will not be repeated here.

In colloidal chemical acid-base car signaling, exposure produces acid, but does not directly affect dissolution. The acid catalyzes a reaction during PEB (called amplification), which changes the solubility.

During the exposure stage, direct photon absorption by the photoacid generator PAG releases acid in a first-order reaction, which is calculated by the following equation (3):

；

;

；

;

（3）

(3)

Among them, G is the PAG concentration; G ₀ is the initial PAG concentration; Q is the acid concentration; I is the target signal value (calculated by the target signal value I(x, y) formula); C is the exposure rate constant; t is the exposure time.

In the amplification stage, the acid catalyzes a reaction that consumes a reactive site (protected/locked site) to produce a reacted site (deprotected/unlocked site), which is calculated by the following equation (4):

；

;

（4）Among them, since the acid concentration Q is a locally constant value after being stabilized by the baking process, then

(4)

Among them, Z is the concentration of locking bits; Z ₀ is the initial concentration of locking bits; k ₄ is the amplification constant; t _PEB is the baking time.

Then, the concentration can be normalized to the initial value, which is given by the following equation (5):

；

;

；

;

；

;

（5）

(5)

为放大因数。放大因数随烘烤时间线性变化，其随着时间T发生显著变化，其通过以下阿仑尼乌斯方程（6）计算：Among them, I is the target signal value; C is the exposure rate constant; t is the exposure time, q is the remaining ratio of acid concentration, K _amp = G ₀ K ₄ is the normalized rate constant,

is the magnification factor. The amplification factor varies linearly with the baking time, which varies significantly with the time T, which is calculated by the following Arrhenius equation (6):

（6）

(6)

Among them, A _γ is the Arrhenius coefficient, E _a is the activation energy, and R is other common constants.

The z value can be calculated from the above formula, and the z value is the semaphore of the car signal.

Reference is now made to FIG. 3 for further description. As mentioned above, only a partial area of the mask layout is shown in Figure 3, and one of the sampling positions is marked with Gauge_0, which is located in the center of the drawing (for illustrative purposes only, not limited to In the middle, other locations can also be selected for illustration. In specific applications, the sampling location can be selected according to the needs of process measurement). Further, a solid line is also shown at the sampling position, and the solid line represents the position of the critical dimension (CD) at both ends of the sampling position. After the position is measured, the position coordinates at both ends of the sampling position are obtained, and then the semaphore corresponding to the position coordinates is searched in the three-dimensional semaphore matrix, and the average value of the two signal values is used as the effect of the sampling position Signal, the effect signal of a type of signal at a sampling position is represented by Rij, where i is the type of signal and j is the sampling position. For example, if there are 10 types of signals, the value of i is 1-10.

However, in the actual sampling process, the position coordinates of the key size positions at both ends of the sampling position may not exactly correspond to the reference coordinates in the three-dimensional semaphore matrix, and the key size positions may fall between the reference points of the semaphore matrix . Therefore, it is further proposed in a preferred embodiment that an interpolation algorithm is used to perform interpolation fitting based on the reference point coordinates based on each sampling position, so as to obtain the signal amounts corresponding to the two ends of the sampling position respectively.

In this embodiment, a bicubic interpolation (BiCubic) algorithm is used for interpolation fitting. In a specific example, the corresponding position of the end position of the sampling position in the three-dimensional semaphore matrix is firstly found, and then the 16 reference points closest to the end position are found in the three-dimensional semaphore matrix, and these 16 The reference points are used as parameters for calculating the signal value of the end position, and the weights of these 16 reference points are obtained by using the BiCubic basis function, and the signal value of the end position is equal to the weighted superposition of these 16 reference points. Bicubic interpolation algorithm is the most commonly used interpolation method in two-dimensional space, which has high interpolation accuracy and low image loss quality. However, the present invention is not limited to the bicubic interpolation algorithm, and other interpolation algorithms may also be sampled, such as bilinear interpolation, nearest neighbor interpolation algorithms, and the like.

It can be seen in Figure 3 that after the semaphores at both ends of the sampling position Gauge_0 are obtained from the three-dimensional semaphore matrix by the above method, the average value Rij of these two semaphores is taken as the effect signal of the sampling position, so that all samples can be obtained Position effect signal. Therefore, for a sampling position, it is necessary to use two interpolation algorithms at both ends of Gauge_0 to obtain the effect signal of the sampling position.

Returning to Fig. 2 again, at step 208, based on the obtained effect signals, the total signal value of this type of signals is calculated. Specifically, for each type of signal, the sum of the effect signals of all adopted positions is obtained, and the sum of the effect signals is taken as the total signal value of the effect signal. The first step is to obtain the average value of the effect signal at all sampling positions for each type of signal. The total signal value Si of each type of signal at all sampling positions is then obtained based on the average value of the effect signal according to formula (7).

（7）

(7)

At step 210 , based on the total signal value Si of each type of signal, the effect contribution ratio of each type of signal is calculated.

Specifically, based on the total signal value Si of each type of signal, the contribution value Ti of each type of signal is calculated according to formula (8).

（8），

(8),

Among them, Ci is the effect coefficient, which is a fixed effect coefficient preset according to the photoresist model, that is to say, after the photoresist model is determined, the effect coefficient of each signal corresponding to the photoresist model is uniquely determined , which is determined by the nature of the photoresist model.

Then according to formula (9), based on the contribution value Ti of each type of signal, the total contribution value T of all signals is calculated.

（9）

(9)

Finally, according to formula (10), the ratio of the contribution value Ti of each type of signal to the total contribution value T is taken as the effect contribution ratio NTi of this type of signal.

（10）

(10)

Based on the above results, at step 212, a photoresist model is selected according to the calculated effect contribution ratio NTi of this type of signal. The calculated effect contribution ratios of various signals are exemplarily shown in FIG. 4 and FIG. 6 .

In a preferred embodiment of the present invention, a target signal among multiple types of signals is determined, that is, the optical signal ai, and a photoresistive model is selected based on the effect contribution ratio of the target signal. Specifically, when the effect contribution ratio of the target signal is greater than or equal to a first preset threshold, a photoresist model is selected. The threshold is a preset value based on experience and the desired effect of the mask layout. In this embodiment, the threshold may be selected as 70%, preferably 75%; more preferably 80%. In this embodiment, the threshold is preset as 80%.

In another alternative embodiment, at step 212, selecting a photoresist model based on the effect contribution ratio of the target signal includes: based on formulas (11) and (12), taking the effect contribution ratio of the target signal The ratio is an input value, and a function value of a preset function is calculated; when the function value is less than or equal to a second preset threshold, a photoresist model is selected.

（11）

(11)

（12）

(12)

Among them, Rmse is the root mean square error, which is the square root of the ratio of the square sum of the deviation between the simulation value of the key dimension of the photoresist model at each sampling position and the historical true value and the sampling number; Rmse_weight is the root mean square error weight; NT1 is The effect contribution ratio of the target signal; Da and Db are preset constants; F(NT1) is an exponential function, and F(NT1)_weight is the weight of the exponential function. In this embodiment, the root mean square error is quoted, which can well reflect the accuracy of the measurement.

It should be noted that, for a photoresist model, it has the capability of simulation at a given mask and sampling position, and the CD value at each sampling position is obtained, that is, the simulation value of the CD. In the case of n sampling times, it is necessary to obtain n CD simulation values of the photoresist model, as well as n historical true values (historical true values are values obtained through historical simulation of the model), so as to obtain n simulation values The difference between the value and the historical true value, that is, the deviation, the n deviations are squared, and then the sum of n squares is obtained, and the square sum of n is compared with n, and then the square root is obtained, so as to obtain the photoresist The root mean square error of the model.

In a specific example, if the root mean square error Rmse of a photoresist model is calculated as 1.4, its weight Rmse_weight is 1 (fixed preset value), the exponential function F(NT1) of the target signal is 1, and the weight of the exponential function If F(NT1)_weight is 1, the calculated Cost value is 1.2. If the second preset threshold is 1.4, then the Cost value of the photoresist model is smaller than the second preset threshold, then the photoresist model is selected.

From the above formulas (11) and (12), it can be seen that when the effect contribution ratio NT1 of the target signal exceeds a predetermined threshold, as NT1 increases, the exponential function F(NT1) of the target signal will decrease rapidly , so that the Cost value decreases rapidly, making the current photoresist model more selective. Conversely, when the effect contribution ratio NT1 of the target signal is lower than the predetermined threshold, the exponential function F(NT1) of the target signal remains basically unchanged, thus basically having no impact on the Cost value.

It should be understood that the various thresholds described in this embodiment are preset values based on experience and the effect to be achieved by the mask layout. The present invention is not limited to the above exemplary listed thresholds.

In some embodiments of the invention, Fourier operations, Arrhenius equations and interpolation algorithms are applied. However, the foregoing manners are merely exemplary, and embodiments of the present invention are not limited to the foregoing manners. Based on the teaching of the content of the present invention, those skilled in the art will understand that the proportion of effect contribution can also be determined by other appropriate mathematical operations.

The ratio of each signal is described below with reference to FIGS. 4-7 .

Figure 4 shows the proportion of each type of effect contribution in the case of model A. The model is exemplified by the IMP layer, which has 1865 sampling locations. Figure 4 shows the proportion of each type of effect contribution in model A. It can be seen from the figure that the signals are mainly divided into optical signal ai and multiple non-optical signals, namely, colloid physics continuous A signal, colloid physics continuous B signal, colloid physics continuous C signal, colloid physics edge A signal and colloid physics B Signal, car signal. It can be seen from Figure 4 that the effect contribution of the optical signal ai is 61.3286%, while the sum of the effect contribution of non-optical signals is 38.6714%.

In this embodiment, the preset threshold is selected as 80%, and the effect contribution ratio of the optical signal is significantly lower than this threshold, so the model A should be discarded.

FIG. 5 shows a signal cross-sectional view of the sampling position Gauge_0 of the model A. Line a in the figure is the signal threshold for measuring the critical dimension (CD), and line b is the simulated signal. It can be seen from the figure that the simulated signal is very close to the threshold (80% is chosen in this embodiment), and entanglement occurs above the threshold. In this case, if the observation window of the simulation is shifted so that line b moves up while line a remains stationary, the critical dimension cannot be measured, or line b moves downward while line a remains stationary , then the critical dimensions can be measured. In the figure shown, a critical dimension CD of 262.28nm is obtained for only one sampling location due to the simulated signal entanglement above the threshold, but if the line b is moved up, this critical dimension increases, which leads to exceeding The preset range of key dimensions, so that key dimensions cannot be output. This situation is not allowed to happen, so the model needs to be abandoned.

Figure 6 shows the proportion of effect contribution in the case of model B. This model is also based on the IMP layer of 14nm (in specific applications, it can also be used in 7nm, 28nm, 40nm, 65nm and other process nodes) (the embodiment of the present invention can be used in other layers of other process nodes, such as metal layers metal layer, CT contact layer, etc.) are examples, which also have 1865 sampling positions. It can be seen from the figure that the signals are mainly divided into optical signal ai and multiple non-optical signals, namely, colloid physics continuous A signal, colloid physics continuous B signal, colloid physics continuous C signal, colloid physics edge A signal and colloid physics B Signal, car signal. However, unlike model A shown in Figure 4, the effect contribution of model B's optical signal ai is 81.9965%, while the sum of effect contributions of other non-optical signals is 18.0035%. Compared with model A, the effect contribution of optical signals of model B is about 20% higher than that of model A. The effect contribution of the optical class signal of model B is above the threshold of 80%, so this model can be selected.

Referring to FIG. 7 , it shows a signal cross-sectional view of the same sampling position Gauge_0 in the case of model B. It can be seen from the figure that the signal does not stay near the threshold (no entanglement), and is safely distributed on both sides of the threshold. Actually, in Fig. 7, the key dimensions CD of 5 sampling positions are obtained, which are respectively CD=72.6329, CD=19.3748, CD=78.9942, CD=20.1299, CD=72.0586 from left to right. When the line b moves up or down to a certain extent, the critical dimension can be measured, which makes the signal more stable and reliable, so model B should be selected.

By selecting models with a high proportion of optical effects, the proportion of unstable models can be significantly reduced and the proportion of more stable models can be increased. This greatly saves labor costs and resource consumption, and provides effective help for users to select models.

Fig. 8 shows a schematic block diagram of an example device 700 that may be used to implement embodiments of the present invention. For example, the electronic device of the present invention may be implemented by device 700 . As shown, the device 700 includes a central processing unit (CPU) 701 that can be programmed according to computer program instructions stored in a read-only memory (ROM) 702 or loaded from a storage unit 708 into a random-access memory (RAM) 703 program instructions to perform various appropriate actions and processes. In the RAM 703, various programs and data necessary for the operation of the device 700 can also be stored. The CPU 701 , ROM 702 , and RAM 703 are connected to each other via a bus 704 . An input/output (I/O) interface 705 is also connected to the bus 704 .

Multiple components in the device 700 are connected to the I/O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc. ; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 709 allows the device 700 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.

The processing unit 701 executes various methods and processes described above, such as the method 200 . For example, in some embodiments, method 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708 . In some embodiments, part or all of the computer program may be loaded and/or installed on the device 700 via the ROM 702 and/or the communication unit 709 . When a computer program is loaded into RAM 703 and executed by CPU 701, one or more steps in method 200 described above may be performed. Alternatively, in other embodiments, the CPU 701 may be configured to execute the method 200 in any other suitable manner (for example, by means of firmware).

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate array (FPGA), application specific integrated circuit (ASIC), application specific standard product (ASSP), system on a chip (SOC), load programmable logic device (CPLD), etc.

Program codes for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partially on the machine, as a stand-alone software package and partially on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

In addition, while operations are depicted in a particular order, this should be understood to require that such operations be performed in the particular order shown, or in sequential order, or that all illustrated operations should be performed to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims (13)

1. A method for selecting a photoresist model, comprising: Collect the sampling positions of the photoresist model on the mask layout; obtaining semaphores of multiple types of signals at the sampling position; For each type of signal, obtaining an effect signal according to the signal amount and the coordinates of the sampling position; calculating a total signal value for signals of this type based on the obtained effect signal; Based on the total signal value of each type of signal, calculating the effect contribution ratio of the type of signal; determining a target signal among the plurality of types of signals; The photoresist model is selected according to the comparison between the effect contribution ratio of the target signal and a preset threshold. 2. The method according to claim 1, selecting a photoresist model based on the comparison of the effect contribution ratio of the target signal and the preset threshold, comprising: When the effect contribution ratio of the target signal is greater than or equal to a first preset threshold, a photoresist model is selected. 3. The method according to claim 1, selecting a photoresist model based on the ratio of the effect contribution of the target signal and the preset threshold, comprising: Based on the following formula:

Using the effect contribution ratio of the target signal as an input value, calculating the function value of the preset function; When the function value is less than or equal to a second preset threshold, select a photoresist model; Among them, Rmse is the root mean square error, which is the square root of the ratio of the square sum of the deviation between the simulation value of the key dimension of the photoresist model at each sampling position and the historical true value and the sampling number; Rmse_weight is the root mean square error weight; NT1 is The effect contribution ratio of the target signal; Da and Db are preset constants, F(NT1) is an exponential function, and F(NT1)_weight is the weight of the exponential function. 4. The method according to any one of claims 1 to 3, wherein, for each type of signal, obtaining an effect signal according to the signal quantity and the coordinates of the sampling position comprises: determining the position coordinates of the critical dimension (CD) positions at both ends of the sampling position; Based on the determined position coordinates and the semaphore of each type of signal, forming a three-dimensional semaphore matrix corresponding to the type of signal; Based on each of the three-dimensional semaphore matrices, an effect signal of the type of signal corresponding to the sampling position is obtained. 5. The method according to claim 4, wherein, based on the determined position coordinates and the semaphore of each type of signal, forming a three-dimensional semaphore matrix corresponding to the type of signal comprises: Obtain the central position of the critical dimension (CD) of the sampling position; forming a rectangular matrix range based on the center position; dividing the range of the matrix at equal intervals to form a plurality of reference point coordinates; Acquiring semaphores corresponding to a plurality of reference point coordinates based on each type of signal; A three-dimensional semaphore matrix for signals of this type is formed based on the acquired semaphores and the matrix range. 6. The method according to claim 5, wherein, based on each of the three-dimensional semaphore matrices, obtaining the effect signal corresponding to the sampling position of the type of signal comprises: For each sampling position, an interpolation algorithm is used to perform interpolation fitting based on the coordinates of the reference point, so as to obtain signal quantities corresponding to both ends of the sampling position; The average value of the signal quantities corresponding to the two ends is used as the effect signal of the sampling position. 7. The method of claim 6, wherein, based on the obtained effect signal, calculating the total signal value for the type of signal comprises: For each type of signal, obtain the sum of the effect signals at all sampling positions; The sum of the effect signals was taken as the total signal value for this type of signal. 8. The method according to claim 7, wherein, based on the total signal value of each type of signal, calculating the effect contribution ratio of this type of signal comprises: calculating a contribution value for each type of signal based on the total signal value; Calculate the total contribution of all signals based on the contribution of each type of signal; The ratio of the contribution value of each type of signal to the total contribution value is taken as the effect contribution ratio of this type of signal. 9. The method of claim 8, wherein, based on the total signal value, calculating a contribution value for each type of signal comprises: Based on the photoresistance model, determine the effect coefficient of each type of signal; Calculate the product of the effect coefficient of each type of signal and the total signal value, and use the product as the contribution value of the type of signal. 10. The method according to any one of claims 1 to 3, wherein the target signal is an optical signal. 11. The method according to claim 10, wherein obtaining the target signal amount of the target signal comprises: obtaining the target signal amount based on Fourier operations at least according to light source type, mask diffraction and lens transfer function. 12. An electronic device comprising: processor; and a memory coupled to the processor, the memory having stored therein instructions that when executed by the processor cause the device to perform actions, the actions comprising: Collect the sampling positions of the photoresist model on the mask layout; obtaining semaphores of multiple types of signals at the sampling position; For each type of signal, obtaining an effect signal according to the signal amount and the coordinates of the sampling position; calculating a total signal value for signals of this type based on said effect signal obtained; Based on the total signal value of each type of signal, calculating the effect contribution ratio of the type of signal; determining a target signal among the plurality of types of signals; A photoresist model is selected according to the comparison between the effect contribution ratio of the target signal and a preset threshold. 13. A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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