Wafer classification method and device for wafer rapid heating process

Abstract

The invention discloses a wafer classification method and device for a wafer rapid heating process, and the method comprises the steps: obtaining a plurality of types of wafers, carrying out the heating for a period of time, obtaining temperature change data, building a data set, enabling the temperature change data to comprise radiance, a temperature value after heating, and a temperature difference value with resolution in a heating process, training a support vector machine based on the data set; and heating the wafers needing to be classified for the same time, inputting the obtained temperature change data into the trained support vector machine to obtain the wafer category, and implementing a rapid heating process according to the obtained wafer category. According to the method, the classification accuracy and the production efficiency in the rapid wafer heating process are remarkably improved, the production cost is reduced, the consistency and the quality of products are improved, and the method has wide application prospects in the field of semiconductor manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of wafer sorting, and in particular to a wafer sorting method and device for a wafer rapid heating process. Background Art

[0002] With the advancement of semiconductor manufacturing technology, the rapid wafer heating process (RTP) is becoming increasingly important in integrated circuit manufacturing. The RTP process can quickly heat the wafer to the required temperature, improving manufacturing efficiency and product quality. However, wafers with different transmittance and surface coatings have different thermal response characteristics when heated by tungsten lamps. Accurately distinguishing and classifying these wafers is crucial to ensure process stability and product consistency.

[0003] Traditional wafer sorting methods mainly rely on physical identification and empirical judgment, such as etching codes, visual identification and RFID tags, etc. These methods have problems such as easy wear, complex identification and reliance on manual operation, resulting in low efficiency and frequent errors.

[0004] In the domestic semiconductor manufacturing process, inaccurate thermometer measurement at low wafer temperatures is a common problem. This is because the transmittance of the wafer material affects the temperature measurement, resulting in inaccurate traditional temperature-based classification methods, which affects the accuracy of wafer classification and subsequent process control. Summary of the invention

[0005] The present invention aims to address the deficiencies of the prior art and to provide a wafer sorting method and device for a wafer rapid heating process.

[0006] The object of the present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a wafer sorting method for a wafer rapid heating process, the method comprising the following steps:

[0007] (1) Obtaining several types of wafers, heating them for a period of time to obtain temperature change data to construct a data set, wherein the temperature change data includes emissivity, temperature value after heating, and temperature difference with resolution during the heating process, and training a support vector machine based on the data set;

[0008] (2) The wafers to be classified are heated for the same time as in step (1), and the acquired temperature change data is input into the trained support vector machine to obtain the wafer category, and a rapid heating process is implemented according to the obtained wafer category.

[0009] Furthermore, in step (1), the heating power is controlled to remain unchanged during heating, and the heating time is 10 seconds, wherein the heating is performed for 5 seconds during the pin up stage and then for 5 seconds after the pin down stage.

[0010] Furthermore, the temperature difference with resolution is the temperature difference between the pin down moment of the multi-channel experiment and the temperature 2 seconds after the pin down; the temperature value after heating is the temperature value data 5 seconds after the pin down.

[0011] Furthermore, the data set includes the emissivity, 7-way temperature difference and 7-way temperature value of the wafer, and all the data are divided into a training set and a test set at a ratio of 3:1. 5. The wafer classification method for wafer rapid heating process according to claim 1 is characterized in that the temperature change data in the data set is subjected to median filtering and preprocessing, and then input into a support vector machine for training with the corresponding wafer category as the label.

[0012] Furthermore, the wafer rapid heating process is confirmed based on the thermal response characteristics of different wafer types.

[0013] In a second aspect, the present invention also provides a wafer classification device for a wafer rapid heating process, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it implements the wafer classification method for a wafer rapid heating process.

[0014] In a third aspect, the present invention further provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the wafer classification method for a wafer rapid heating process is implemented.

[0015] In a fourth aspect, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the wafer classification method for a wafer rapid heating process.

[0016] Beneficial effects of the present invention:

[0017] (1) High-precision classification: Using the SVM algorithm and temperature change data, high-precision identification of different types of wafers can be achieved.

[0018] (2) Automated identification: Integrate with automated equipment to achieve fully automated operation and reduce human intervention and errors.

[0019] (3) Real-time response: Real-time classification based on temperature change data without waiting for complex identification process.

[0020] (4) Wide applicability: Applicable to various wafer materials, including silicon wafers and compound semiconductor wafers.

[0021] (5) Solve the problem of inaccurate low-temperature measurement: By collecting temperature change data within 10 seconds before heating, the influence of wafer transmittance on low-temperature measurement is avoided, thereby improving classification accuracy.

[0022] The present invention significantly improves the classification accuracy and production efficiency in the wafer rapid heating process, reduces production costs, improves product consistency and quality, and has broad application prospects in the field of semiconductor manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a schematic diagram of the linear SVM principle;

[0025] Figure 2 This is the temperature response curve of the bare wafer under the condition of air pressure of 5 torr, inner circle heating voltage of 15%, and outer circle heating voltage of 5%.

[0026] Figure 3 This is the temperature response curve of a patterned wafer at an air pressure of 5 torr, an inner ring heating voltage of 15%, and an outer ring heating voltage of 5%.

[0027] Figure 4 This is a flow chart of the training process;

[0028] Figure 5 This is a schematic diagram of the 5-fold cross validation results;

[0029] Figure 6 It is a structural diagram of a wafer sorting device used in a wafer rapid heating process of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] like Figure 1 As shown, the present invention provides a wafer classification method for a wafer rapid heating process, and the specific implementation process of the method is as follows:

[0032] 1. Technical principle:

[0033] The basic concept of the Support Vector Machine (SVM) algorithm can be derived from a simple linear binary classification problem: Assuming that the goal of the algorithm is to use a single straight line to distinguish two types of sample points of different colors, then it is necessary to use a straight line passing through the edge points as a reference so that the two sample sets are strictly distinguished by two straight lines, such as Figure 1 As shown by the blue line in . In the actual use of the algorithm model, in order to obtain the best classification effect, it is also necessary to make the distance between the two dividing lines as far as possible through rotation and other methods, and select the classification hyperplane between the two dividing lines to avoid the situation where the collection point slightly deviates from the sample set and causes misclassification.

[0034] Therefore, the above geometric problem can be transformed into a mathematical problem of finding the maximum value of the minimum function, which can be described by the formula derived by the Lagrangian function method:

[0035]

[0036] Among them, α i represents the non-negative Lagrange multiplier, y i Represents the decision value of the sample point, such as Figure 1 In , the decision value of the purple sample point is set to 1, and the decision value of the green sample point is -1. At the same time, due to the basic assumption of the Lagrangian method for saddle points: the function calculates the partial derivative function of each variable separately, and the value of each partial derivative function is 0 at the optimal solution, it can be deduced that α i y i The weighted sum of is zero; Φ(x i ) represents the kernel function of the input vector. In solving conventional linear problems, Φ(x)=x is generally taken. Different kernel functions can also be selected to correct the weights of the input vector for nonlinear problems.

[0037] It is not difficult to see from the above introduction that SVM is an algorithm with strict mathematical theory support. It is good at finding key sample points as support vectors for classification. And because the classification judgment process is only linked to a small number of support vectors, it avoids complex calculations when calling, and is very convenient to use.

[0038] 2. Implementation Cases:

[0039] (1) Set the recipe, such as Figure 2 and Figure 3As shown, different wafers will have different temperature responses under the same basic voltage. Therefore, the present invention controls the heating power to remain unchanged, uses the initial voltage outer ring power of 5%, the inner ring power of 20%, and according to the process requirements, first heats for 5s in the pin up stage, and then heats for 5s after pin down. Obtain the temperature data at the pin down moment of the multi-channel experiment, the temperature difference at 2s after pin down, and the temperature data at 5s after pin down. Since these two sets of data are more resolvable than other time nodes, they are selected to form a data set with the emissivity parameters of the wafer, and the data shown in Table 1 are obtained.

[0040] Table 1

[0041]

[0042] (2) The experimental data are median filtered and preprocessed. The target label is the type of wafer, and the data labels are the emissivity of the wafer, the 7-channel temperature difference, and the 7-channel temperature value. All the data are divided into a training set and a test set with a ratio of 3:1.

[0043] (3) Use support vector machine (SVM), neural network (NN), RF (random forest), and KNN (K nearest neighbors) algorithms to train the data.

[0044] (4) Deploy the trained model. The results are as follows: Figure 5 As shown in Table 2.

[0045] Table 2

[0046] algorithm 5-fold cross validation results Neural Network (NN) 20.67％(+ / -6.46％) Random Forest (RF) 93.33％(+ / -13.33％) Support Vector Machine Linear (SVM) 96.67％(+ / -6.67％) K-nearest neighbors (KNN) 79.33％(+ / -16.25％)

[0047] Finally, the support vector machine was selected as the wafer type recognition model. The wafers that needed to be classified were heated for 10 seconds, and the acquired data labels were input into the trained support vector machine to obtain the wafer category. The rapid heating process was implemented according to the obtained wafer category.

[0048] Corresponding to the aforementioned embodiment of a wafer classification method for a wafer rapid heating process, the present invention also provides an embodiment of a wafer classification device for a wafer rapid heating process.

[0049] See also Figure 6 A wafer classification device for a wafer rapid heating process provided in an embodiment of the present invention includes a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it is used to implement a wafer classification method for a wafer rapid heating process in the above embodiment.

[0050] An embodiment of a wafer sorting device for a wafer rapid heating process provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From a hardware perspective, if Figure 6 As shown, it is a hardware structure diagram of a wafer sorting device for wafer rapid heating process provided by the present invention, in which any device with data processing capability is located, except Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0051] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0052] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0053] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a wafer classification method for a wafer rapid heating process in the above embodiment is implemented.

[0054] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0055] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the wafer classification method for a wafer rapid heating process.

[0056] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A wafer sorting method for a wafer rapid heating process, characterized in that: The method comprises the following steps: (1) Obtaining several types of wafers, heating them for a period of time to obtain temperature change data to construct a data set, wherein the temperature change data includes emissivity, temperature value after heating, and temperature difference with resolution during the heating process, and training a support vector machine based on the data set; (2) The wafers to be classified are heated for the same time as in step (1), and the acquired temperature change data is input into the trained support vector machine to obtain the wafer category, and a rapid heating process is implemented according to the obtained wafer category.

2. The wafer sorting method for wafer rapid heating process according to claim 1, characterized in that: In step (1), the heating power is controlled to remain unchanged during heating, and the heating time is 10 seconds, including heating for 5 seconds in the pin up stage and heating for 5 seconds after pin down.

3. The wafer sorting method for wafer rapid heating process according to claim 2, characterized in that: The temperature difference with resolution is the temperature difference between the pin down moment of the multi-channel experiment and the temperature 2s after the pin down; the temperature value after heating is the temperature value data 5s after the pin down.

4. The wafer sorting method for wafer rapid heating process according to claim 1, characterized in that: The data set includes the emissivity of the wafer, 7-way temperature difference and 7-way temperature value. All data are divided into training set and test set in a ratio of 3:

1.

5. The wafer sorting method for wafer rapid heating process according to claim 1, characterized in that: After median filtering and preprocessing, the temperature change data in the data set are input into the support vector machine for training with the corresponding wafer categories as labels.

6. The wafer sorting method for wafer rapid heating process according to claim 1, characterized in that: Confirm the wafer rapid heating process based on the thermal response characteristics of different wafer types.

7. A wafer sorting device for a wafer rapid heating process, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a wafer classification method for a wafer rapid heating process according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a wafer classification method for a wafer rapid heating process as described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, a wafer classification method for a wafer rapid heating process as described in any one of claims 1 to 6 is implemented.

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