Control equipment and control method of stepper

A control equipment and a control method of a stepper are provided. The control equipment of the stepper includes an input device, a generating device and a processing device. The input device is configured to input a plurality of sample development patterns. The sample development patterns are obtained according to a plurality of sample focal length values. The generating device is configured to generate a plurality of generative categories corresponding to a plurality of generative focal length values by using a depth learning algorithm. The processing device is configured to analyze an estimated focal length value of the online development pattern according to the generative categories.

This application claims the benefit of People's Republic of China application Serial No. 201910999042.3, filed Oct. 21, 2019, the subject matter of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

The invention relates in general to a control equipment and a control method, and more particularly to a control equipment and a control method of a stepper.

Description of the Related Art

Along with the development in the semi-conductor technology, various electronic devices are provided one after another. The manufacturing process of chip includes an exposure process, a development process, and an etching process. After a photoresist layer is exposed in an exposure process, a development pattern is shown in the development process. In an etching process, a metal layer is etched, by using the development pattern as a mask, to form a circuit layer.

Therefore, the precision of controlling the exposure process greatly affects the circuit layer. Conventionally, an operator can adjust the exposure energy of the stepper to precisely develop the development pattern. However, as the trace width is getting smaller and smaller, the stepper needs more accurate calibration to form high precision circuits.

SUMMARY OF THE INVENTION

The present invention relates to a control equipment and a control method of a stepper, which calibrates the beam focal length and/or the exposure energy of the stepper to increase the precision of the stepper by using the artificial intelligence technology.

According to an embodiment of the present invention, a control equipment of a stepper is provided. The control equipment of the stepper includes an input device, a generating device and a processing device. The input device is configured to input a plurality of sample development patterns. The sample development patterns are obtained according to a plurality of sample focal length values. The generating device is configured to generate a plurality of generative categories corresponding to a plurality of generative focal length values by using a depth learning algorithm. The processing device is configured to analyze an estimated focal length value of an online development pattern according to the generative categories.

According to another embodiment of the present invention, a control method of a stepper is provided. The control method of the stepper includes the followings steps. A plurality of sample development patterns are obtained according to a plurality of sample focal length values. A plurality of generative categories corresponding to a plurality of generative focal length values are generated by using a depth learning algorithm. An online development pattern is obtained. An estimated focal length value of the online development pattern is analyzed according to the generative categories.

The above and other aspects of the invention will become better understood with regards to the following detailed description of the preferred but non-limiting embodiment(s). The following description is made with reference to the accompanying drawings.

DETAILED DESCRIPTION OF THE INVENTION

Referring toFIG. 1, a schematic diagram of a control equipment100of a stepper700according to an embodiment is shown. The control equipment100includes an input device110, a generating device120and a processing device130. The input device110is configured to input various types of data, and can be realized by such as a wired network connection, a wireless network transceiver module, a transmission line, or a USB port. The generating device120is configured to perform various training/generating procedures. The processing device130is configured to perform various processing/analyzing procedures. The generating device120and the processing device130can be realized by such as a circuit, a circuit board, a chip, a plurality of programming codes or a recording device for storing programming codes.

In the present embodiment, after the stepper700exposes and develops the photoresist layer on the production line, the scanning electron microscope (SEM)800captures and inputs an online development pattern P70to the control equipment100. The control equipment100can analyze the online development pattern P70to obtain an estimated focal length value F7by using the artificial intelligence technology. Thus, the beam focal length of the stepper700can be calibrated according to the estimated focal length value F7, such that the beam of the stepper700can be precisely focused on the photoresist layer and the precision of the stepper700can be increased. The operations of each element disclosed above are described below with accompanying flowcharts.

Refer toFIG. 2andFIG. 3.FIG. 2is a flowchart of a control method of the stepper700according to an embodiment.FIG. 3is a schematic diagram of each step ofFIG. 2. First, the method begins at step S110, a plurality of sample development patterns P10are obtained according to a plurality of sample focal length values F1(illustrated inFIG. 3) by the input device110. The sample focal length values F1are set by the stepper700, and different sample development patterns P10can be obtained according to different sample focal length values F1. The sample development patterns P10are obtained by the scanning electron microscope800by capturing the images of the photoresist layer which has been exposed and developed by the stepper700. Since the stepper700has limited number of calibration scales with respect to the sample focal length values F1, the number of the sample development patterns P10corresponding to the sample focal length values F1is also limited.

Then, the method proceeds to step S120, a plurality of generative categories CF1corresponding to a plurality of generative focal length values F1′ are generated by the generating device120using a depth learning algorithm. The depth learning algorithm includes an auto-encoder algorithm and a generative adversarial network algorithm (GAN algorithm). As indicated inFIG. 3, in the present step, the sample development patterns P10are converted into a plurality of encoded patterns P11by using the auto-encoder algorithm and the generative adversarial network algorithm. During the process of generating the encoded patterns P11, the generative categories CF1corresponding to different generative focal length values F1′ are categorized and stored in a database900.

That is, the generative focal length values F1′ are not set by the stepper700but are generated by using the auto-encoder algorithm and the generative adversarial network algorithm. The scale of the generative focal length values F1′ is finer than that of the sample focal length values F1. That is, the number of the generative focal length values F1′ is larger than that of the sample focal length values F1.

For example, the setting of the stepper700is limited to 10 sets of sample focal length values F1, and only 10 sample development patterns P10are obtained in step S110. In step S120, 100 generative categories CF1corresponding to 100 generative focal length values F1′ can be generated by the generating device120.

Then, the method proceeds to step S130, a to-be-tested online development pattern P70is obtained by the input device110operating on the production line. The online development pattern P70is obtained by the scanning electron microscope800by capturing the images of the photoresist layer which has been exposed and developed by the stepper700.

Then, the method proceeds to step S140, the estimated focal length value F7of the online development pattern P70is analyzed by the processing device130according to the generative category CF1. As indicated inFIG. 3, in the present step, the processing device130performs analysis according to the approximation between a hidden layer of the online development pattern P70and a hidden layer of each generative category CF1. That is, the online development pattern P70is converted into an encoded pattern P71by using the encoder algorithm and the generative adversarial network algorithm, and during the conversion process, the characteristics of the hidden layer HL71is compared with the characteristics of the hidden layer of the generative category CF1to find a set of best approximated generative category CF1. After the set of best approximated generative category CF1is obtained, its corresponding generative focal length value F1′ is regarded as an estimated focal length value F7.

Then, the method proceeds to step S150, a control command CS1is outputted to the stepper700by the processing device130according to the estimated focal length value F7to calibrate the beam focal length of the stepper700.

With the said artificial intelligence technology, the beam focal length of the stepper700can be calibrated to the most accurate value, such that the precision of the stepper700can be greatly increased.

Apart from calibrating the beam focal length of the stepper700, the artificial intelligence technology of the present disclosure can be used to calibrate the exposure energy of the stepper700as well. Refer toFIG. 4andFIG. 5.FIG. 4is a flowchart of a control method of the stepper700according to another embodiment.FIG. 5is a schematic diagram of each step ofFIG. 4. First, the method begins at step S210, a plurality of sample development patterns P10are obtained by the input device110according to a plurality of sample focal length values F1and a plurality of sample energy values E1. The sample focal length values F1and the sample energy values E1are set by the stepper700. Different sample development patterns P10can be obtained according to different sample focal length values F1and sample energy values E1. The sample focal length values F1and the sample energy values E1form a matrix. Since the stepper700has limited number of calibration scales with respect to the sample focal length values F1and the sample energy values E1, the number of the sample development patterns P10corresponding to the sample focal length values F1and the sample energy values E1are also limited.

Then, the method proceeds to step S220, a plurality of generative categories CF2corresponding to a plurality of generative focal length values F1′ and a plurality of generative energy values E1′ are generated by the generating device120using a depth learning algorithm. In the present step, the sample development patterns P10are converted into a plurality of encoded patterns P12by using the auto-encoder algorithm and the generative adversarial network algorithm. During the process of generating the encoded patterns P12, a plurality of generative categories CF2corresponding to the generative focal length values F1′ and the generative energy values E1′ are classified. That is, the generative focal length values F1′ and the generative energy values E1′ are not set by the stepper700but are generated by using the auto-encoder algorithm and the generative adversarial network algorithm. The scale of the generative focal length values F1′ is finer than that of the sample focal length values F1. That is, the number of the generative focal length values F1′ is larger than that of the sample focal length values F1. The scale of the generative energy values E1′ is finer than that of the sample energy values E1. That is, the number of the generative energy values E1′ is larger than that of the sample energy values E1.

For example, the setting of the stepper700is limited to 10 sets of sample focal length values F1and 10 sets of sample energy value E1(form a 10*10 matrix), and only 100 sample development patterns P10can be obtained in step S210. In step S120, 10000 generative categories CF2corresponding to 100 generative focal length values F1′ and 100 sets of generative energy values E1′ (form a 100*100 matrix) can be generated by the generating device120.

Then, the method proceeds to step S230, a to-be-tested online development pattern P70is obtained by the input device110operating on the production line. The online development pattern P70is obtained by the scanning electron microscope800by capturing the images of the photoresist layer which has been exposed and developed by the stepper700.

Then, the method proceeds to step S240, the estimated focal length value F7and an estimated energy value E7of the online development pattern P70are analyzed by the processing device130according to the generative category CF2. As indicated inFIG. 5, in the present step, the processing device130performs analysis according to the approximation between a hidden layer of the online development pattern P70and a hidden layer of each generative category CF2. That is, the online development pattern P70is converted into an encoded pattern P72by using the encoder algorithm and the generative adversarial network algorithm. During the process, the characteristics of the hidden layer HL72is compared with of the characteristics of the hidden layer of the generative category CF2to find a set of best approximated generative category CF2. After the set of best approximated generative category CF2is obtained, the generative focal length value F1′ and the generative energy value E1′ corresponding to the set of best approximated generative category CF2are regarded as the estimated focal length value F7and the estimated energy value E7.

Then, the method proceeds to step S250, the control command CS2is outputted to the stepper700by the processing device130according to the estimated focal length value F7and the estimated energy value E7to calibrate the beam focal length and the exposure energy of the stepper700.

With the artificial intelligence technology, the beam focal length and the exposure energy of the stepper700can be calibrated to the most accurate value, such that the precision of the stepper700can be greatly increased.

Besides, the artificial intelligence technology of the present disclosure can be used to calibrate the exposure energy of the stepper700only. Refer toFIG. 6andFIG. 7.FIG. 6is a flowchart of a control method of the stepper700according to another embodiment.FIG. 7is a schematic diagram of each step ofFIG. 6. Firstly, the method begins at step S310, a plurality of sample development patterns P10are obtained by the input device110according to a plurality of sample energy values E1. The sample energy values E1are pre-set by the stepper700, and different sample development patterns P10can be obtained according to different sample energy values E1. Since the stepper700has limited number of calibration scales with respect to the sample energy value E1, the number of the sample development patterns P10corresponding to the sample energy value E1is also limited

Then, the method proceeds to step S320, a plurality of generative categories CF3corresponding to a plurality of generative energy values E1′ are generated by the generating device120using a depth learning algorithm. In the present step, the sample development patterns P10are converted into a plurality of encoded patterns P13by using an auto-encoder algorithm and a generative adversarial network algorithm. During the process of generating encoded patterns P13, a plurality of generative categories CF3corresponding to the generative energy values E1′ are classified. That is, the generative energy values E1′ are not set by the stepper700but are generated by using the auto-encoder algorithm and the generative adversarial network algorithm. The scale of the generative energy values E1′ is finer than that of the sample energy values E1. That is, the number of the generative energy values E1′ is larger than that of the sample energy values E1.

For example, the setting of the stepper700is limited to 10 sets of sample energy values E1, and only 10 sample development patterns P10can be obtained in step S310. In step S320, 100 generative categories CF3corresponding to 100 sets of generative energy values E1′ can be generated by the generating device120.

In step S330, a to-be-tested online development pattern P70is obtained by the input device110operating on the production line. The online development pattern P70is obtained by the scanning electron microscope800by capturing the images of the photoresist layer which has been exposed and developed by the stepper700.

Then, the method proceeds to step S340, the estimated energy value E7of the online development pattern P70is analyzed by the processing device130according to the generative category CF3. As indicated inFIG. 5, in the present step, the processing device130performs analysis according to the approximation between a hidden layer of the online development pattern P70and a hidden layer of each generative category CF3. That is, the online development pattern P70is converted into an encoded pattern P73by using the encoder algorithm and the generative adversarial network algorithm. During the process, the characteristics of the hidden layer HL73is compared with the characteristics of the hidden layer of the generative category CF3to find a set of best approximated generative category CF3. After the set of best approximated generative category CF3is obtained, the generative energy value E1′ corresponding to the set of best approximated generative category CF3is regarded as an estimated energy value E7.

Then, the method proceeds to step S350, the control command CS3is outputted to the stepper700by the processing device130according to the estimated energy value E7to calibrate the beam focal length and the exposure energy of the stepper700.

With the artificial intelligence technology, the exposure energy of the stepper700can be calibrated to the most accurate value, such that the precision of the stepper700can be greatly increased.