Virtual Metrology (VM) is an emerging capability being driven by IC manufactures need for increased equipment productivity and lower costs. The VM is defined as the prediction of metrology variables (measurable or non-measurable) using process and wafer state information. In this way, direct measurements from the wafer can be minimized or eliminated altogether, hence the term “virtual” metrology. Challenges include the selection of the appropriate sensors to measure the variation in the process, the pre-treatment of the raw data from the sensors, the selection of the appropriate modelling method and the deployment of a method that would allow this prediction to be robust enough to allow deployment to a manufacturing environment.
The development of VM methods does not aim to replace the real metrology tools, but to assist in achieving total quality management and process control. However, the research progress is still tardy because of the lack of relevant theory. VM could be applied as one of the strategies to improve semiconductor industry’s OEE (Overall Equipment Effectiveness). The prediction ability of VM allows the quality of a wafer to be known immediately after being processed and, the objective of real-time wafer-to-wafer (W2W) quality monitoring can be achieved. Important wafer yield information can be obtained to ensure that the equipment anomaly can be detected promptly and only wafers worth processing continue down the line.
In WP2, models and tools will be developed to predict the physical and electrical characteristics of the wafers from the process tools parameters and advanced sensor data.
This will lead to a better understanding and control of the process and reduction of process variation and enable improvement in Cycle Time through non-value added measurement steps. In addition this capability will be an enabler for remaining completive with the challenges of Moore’s law and market cost pressures. It will also increase the productivity of equipment and people in European SC manufactures.
The main focus will be on: