Multi-stage semiconductor process virtual metering method based on convolutional neural network
A convolutional neural network and virtual metrology technology, applied in the field of probabilistic virtual metrology for multi-stage semiconductor processes, can solve problems such as information loss and the impact of model prediction capabilities, so as to improve economic benefits, improve detection accuracy and reliability, reduce Calculate the effect of the load
Pending Publication Date: 2020-11-06
ZHEJIANG UNIV
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However, this method will cause the loss of certain information, and the predictive ability of the model may be affected.
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[0052] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention, but do not limit it in any way.
[0053] Taking the estimation of the deposition process results of a domestic factory as an example, the virtual measurement of the wafer height value through the multi-stage chemical process is carried out.
[0054]During production, the chemical vapor deposition process is similar to the process of applying solid thin film coatings on surfaces often used in the semiconductor industry. This process is complex because it involves many chemical reactions, and the reactors in a multi-reactor system are independently controlled to allow the film to be deposited in the process chamber under various conditions. Chemical vapor deposition equipment is equipped with a considerable number of sensors. Thr...
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The invention discloses a multi-stage semiconductor process virtual metering method based on a convolutional neural network. The method comprises the following steps: (1), collecting an output signalof a process variable sensor related to a to-be-detected variable in a to-be-detected control process; (2) preprocessing the acquired signal data of the process variable, and removing an abnormal value; (3) rearranging the preprocessed data and reserving stage information of the data; (4) performing feature extraction on all data and establishing a regression model; (5) storing the current parameter weight, calculating the final minimum error value, If the final minimum error value does not meet the stop condition, updating the parameter and repeating the step (4) until the stop condition is met; and (6) storing the parameter value of each layer, and recalculating the new prediction point. According to the invention, a virtual metering result with higher precision can be obtained.
Description
technical field [0001] The invention relates to the field of data mining in industrial systems, in particular to a probabilistic virtual metering method for multi-stage semiconductor processes based on convolutional neural networks. Background technique [0002] Semiconductor manufacturing involves many stages. For example, in the production of electronic chips, a wire saw first cuts a silicon ingot into segments, then goes through several flat stages, including cleaning, polishing and grinding, and then transfers the treated wafer to the front and back where the final chip is formed. end process. Due to the high-throughput nature of semiconductor manufacturing and the high cost of measuring wafers, it is not possible to measure all quality variables of produced wafers at every stage. Due to the limitations of physical measurements, wafer-to-wafer modeling is increasingly being used to predict the quality of the final product, allowing timely adjustment of tools and equipm...
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IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06N3/045
Inventor 谢磊吴小菲徐浩杰陈启明苏宏业
Owner ZHEJIANG UNIV