In the production process of the chip, many processes such as doping, layering, photolithography and heat treatment are experienced, and each step must meet extremely demanding physical property requirements. However, even the most mature processes have variations between different locations, between different wafers, between different process runs, and between different time periods. Sometimes, this variation can cause the process to go beyond its process boundaries and produce wafers that do not meet process standards, which can seriously affect yield (YiELd). Anyone who has a little understanding of the semiconductor industry knows that the entire industry is extremely concerned about its yield. Therefore, it is particularly important to correctly estimate and control the variation in the production process of the chip, and one of the common methods for studying process variation is process capability analysis. In general, process capability analysis usually refers to the ability to measure the actual production process to meet specifications by comparing the range of customer quality requirements with the actual product quality variation range. Specifically, it is to calculate the process capability index Cp and Cpk values, determine the process capability level, determine whether the process capability is insufficient, acceptable or sufficient, and then take corresponding improvement and maintenance measures. This simple and easy-to-use quality management tool has been widely used in all walks of life and is well received*. Due to the complexity of the semiconductor manufacturing process, the large number of process processes involved in the production of a complete device, and the variety of detection content, etc., it is inevitable that the "process capability index" analysis in chip production must follow the original quality statistics theory. On the basis of development, a set of "process capability index" analysis methods suitable for the semiconductor industry has been created. Throughout the country's conventional quality management consulting and software market, it has not been possible to propose an ideal solution for a long time. Fortunately, the high-end Six Sigma quality management statistical analysis software commonly used by global chip giants such as Intel Intel, National Semiconductor, Semiconductor, and other global chip giants has done a lot of effective work in this area and has become an application standard in the semiconductor industry. . Next, this article will be combined with a case to share this research results on the latest JMP7 platform of JMP software. Figure 1 Wafer Factory Test Data As shown in the above figure, Figure 1 is a data table obtained by a wafer fab in the final electronic test phase. There are 1455 records (limited to the space, Figure 1 shows only the first 30 records), and the quality characteristics of the inspection are 16 (The actual situation will be more, here only a part of it is demo, and limited to the length, Figure 1 shows only the first seven quality characteristics). According to the traditional analysis method, we need to calculate 16 sets of process capability index step by step, and evaluate each quality characteristic one by one, but lack a comprehensive understanding of the interrelationship between them and the overall quality performance of the product. And simply using the numbers to illustrate, it seems a bit boring abstraction.
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