Based on the consideration of manufacturing costs, a random sampling method is commonly used at present for inspecting the product quality of a production machine inmost semiconductor manufacturing processes. Two to three pieces of wafers are selected from each process batch (each bath contains 25 pieces of wafers) for the actual measurements, thereby monitoring whether or not the quality of the manufacturing process is stable, and consequently the quality of the products is determined. Therefore, if there is any problem in the manufacturing process for a certain process batch, the problem will not be found until the inspection is completed. As a result, the manufacturing process may have continued manufacturing several defectives products already. Therefore, there is a time difference between the occurrence of a variation of the operating variables of the manufacturing process and the occurrence of a defective product. Thus, it is an important subject for the semiconductor manufacturers to predict the quality issue of a product within the shortest time duration.
Virtual metrology is a major technical measure for solving the aforementioned problem, and whose basic concept uses a large number of predicting variables for an online measurement. For example, the techniques of fault detection and data classification are used for estimating the production quality of the product, such that any abnormality occurred at the production machines can be discovered timely. While the defective products can be determined and detected, the energy sources and resources for the later coming processes can be saved, and the production yield can be improved. However, a huge number of data may be involved in fault detection and data classification, and a high correlation exists between certain variables. Consequently, the conventional processing method adopts the statistical regression method, and the principal component regression (PCR) and partial least squares (PLS) are used extensively. Both PCR and PLS can project high-dimensional and collinear data to a space defined by orthogonal major factors or potential variables, such that newly obtained variables are independent of each other. In addition, certain unknown interfering factors always affect the manufacturing process. Both PCR and PLS those are generally in a stable status are not applicable for the semiconductor manufacturing process.
In addition, for the PCR and PLS, data are usually compressed to replace the original variables, and at-site engineers are unable to understand the influence of each variable on the product quality immediately, or find out the key factors of the abnormality.
In currently known patent applications, for example, “Quality prediction system for a production process and method thereof” as disclosed in R.O.C. Pat. Application No. 093115993 is applied in the semiconductor manufacturing industry, wherein the properties of a machine are used to select a corresponding estimation model and predict the quality of a future manufacturing product. But, such method limits the machine from having any maintenance and adjustments, and fails to point out the key variables for a certain batch product produced in the manufacturing process.
A “virtual metrology for quality control applied for a semiconductor manufacture” as disclosed in R.O.C. Pat. Application No. 095120601 is applied in the semiconductor manufacturing industry, wherein the sampling frequency of wafers is set, and the difference between the actual measured value and the estimated value is used for correcting a control chart to determine the next action taken. This method reflects the variation of the machine, but it cannot reflect the source causing the variation of the machine. Furthermore, the misjudgment rate is relatively high if there is any interference.
An “Instant predicting and measuring system, manufacturing information integrated in instant predicting system, and method of predicting at least one output in a virtual measuring tool” as disclosed R.O.C. Pat. Application No. 094121585 is applied in the semiconductor manufacturing industry, wherein an instant predicting and measuring architecture is provided. This method requires at least one output variation for each information system related to the manufacturing process, and suitable related variables cannot be used according to the characteristics of the equipments.
A “Method of monitoring and/or controlling a semiconductor manufacturing apparatus and a system” as disclosed in U.S. Pat. No. 6,616,759 is applied for semiconducting manufacturing processes, wherein a method is based on the PLS method and used for calculating new parameter values of the manufacturing process, but this method does not allow engineers to know about the influence of each variable on the product quality.
A method of predicting a wafer manufacturing temperature as disclosed in U.S. Pat. No. 6,666,577 entitled “Method for predicting temperature, test wafer for use in temperature prediction and method for evaluating lamp heating system” is applicable for a certain specific types of machines only, and lack of universality.
In summary, the aforementioned R.O.C. and U.S. patents or patent applications just taught the method of how to predict the machines, but the precision of the prediction is not high and the prediction may be affected by other factors easily. Furthermore, the key parameters of the manufacturing result of the finished goods in the manufacturing process cannot be found. In the present semiconductor manufacturing process, a method capable of predicting and analyzing key factors of a manufacturing process is absolutely necessary.