This paper describes the development and the actual utilization of fab-wide fault detection and classification (FDC) for the advanced semiconductor manufacturing using big data. In the fab-wide FDC, the collection of equipment's big data for the FDC judgment is required; hence we developed the equipment monitoring system that handles the data in a superior method in high speed and in real time. We succeeded in stopping equipment and lots automatically when the equipment was detected as fault condition. In addition we developed the environment that enables immediate data collection for analysis by the data aggregation and merging functions, which extracts keys correlating to yield from the equipment's parameter. Furthermore, we succeeded in development of the high-speed and high-accuracy process control system that implemented virtual metrology (VM) and the run-to-run (R2R) function for the purpose to reduce process variation.Index Terms-Advanced process control (APC), big data, chemical mechanical polishing (CMP), equipment engineering system (EES), fault detection and classification (FDC), machine-to-machine (M2M), non-production wafer (NPW), run-to-run (R2R), virtual metrology (VM). 0894-6507 (c)

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This paper is a description of how to predict and control the transistor threshold voltage (Vth) for an advanced system on chip (SoC) by virtual metrology (VM), which we call virtual process control module (PCM), by using equipment data. Impact analysis for Vth variation by using a Virtual PCM model indicates that the impact of source and drain (S/D) resistance and extension resistance are comparable with that of the shape factor (i.e., gate length, sidewall width). Virtual metrology models for the resistance were developed to control and predict Vth. As a result of the VM control, Vth variation was reduced by 28%. Moreover, in-process prediction of Vth was put into practical use.Index Terms-Control, equipment data, prediction, system on chip (SoC), transistor threshold voltage (Vth), virtual metrology (VM).

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