Agnes Roussy

Efficient FDC based on hierarchical tool condition monitoring scheme

Tool condition evaluation and prognosis has been an arduous challenge in modern semiconductor manufacturing environment, especially for the foundry and analog companies with high product-mix and complicated technology nodes. More and more embedded and external sensors are installed to capture the genuine tool status for tool fault identification and, thus, tool condition analysis based on real-time equipment data becomes promising but also much more complex with the rapidly-increased number of sensors. In this paper, the feasibility of Generalized Moving Variance (GMV) technique is validated to consolidate the pure variations within tool Fault Detection and Classification (FDC) data into one indicator. Based on GMV, a hierarchical tool condition monitor scheme is developed by analyzing the GMV within functional clusters of sensors. With the introduction of this hierarchy, abnormal tool condition can be diagnosed and drilled down into sensor level for an efficient root cause analysis.

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

Tool behavior modeling and diagnosis is a big challenge in modern semiconductor fabrication, in particular for the foundry and analog companies with high product-mix and complicated technology nodes. Tool condition monitoring has been practiced by implementing the FDC (Fault Detection and Classification) system and analyzing large amount of real-time equipment data. This paper continues the work of tool condition hierarchy, where the excursions can be detected in one single overall tool indicator and are intuitively drilled down to the level of sensor groups. A R2R (Run-to-Run) variation monitoring technique is developed in order to correlate the tool faults with single sensor and thus completes the diagnostic gap of the hierarchy. The tool condition monitoring then becomes efficient and tool fault diagnosis can be systematically top-down.

The light behavior from Shallow Trench Isolation profiles at Chemical Mechanical Planarization step and correlation with optical endpoint system by interferometry

In the semiconductor manufacturing, the control of Chemical-Mechanical Planarization (CMP) process time for Shallow Trench Isolation (STI) is important. A wafer under- or over-polishing causes leakage and short-circuits making the chips defective. The CMP process control by interferometry is one of the most used systems to monitor the polishing time. In some cases, the interferometry process control is not possible because the wafer patterns cause some unwanted effects such as scattering, diffraction, and absorption. Consequently the signal is affected. In this paper, we apply a theoretical and experimental approach on the light reflected from different STI stacks in order to interpret the observed optical phenomenon. The experimental study is done to get close to the light measurement conditions within the manufacturing environment. With this experiment, we evidence that the trench pattern inside memory zones is responsible for the diffraction effect on the signal. In a production environment, this pattern results in a lower measured intensity when the size of memory area increases. Besides, numerical calculations are performed based on differential method in order to predict the diffracted intensity, which depends on the chip design parameters and the incident wavelengths tuning. By using STI models, this method helps to determine the wavelengths with the highest reflected intensity.

Run-to-run sensor variation monitoring for process fault diagnosis in semiconductor manufacturing

Tool behavior modeling and diagnosis is a big challenge in modern semiconductor fabrication, in particular, with high product-mix and complicated technology nodes. Tool condition monitoring has been long conducted by implementing the Fault Detection and Classification (FDC) system and analyzing the large amount of real-time sensor data collected during the process. The tool condition hierarchy developed in the previous work proposed that the excursions can be firstly detected by an overall condition indicator and then intuitively traced down to the level of sensor groups. In this paper, a Run-to-Run (R2R) variation monitoring technique is developed in order to correlate the tool excursions with individual sensors, instead of sensor groups, and thus to close the diagnostic gap in the hierarchy. Therefore, the tool condition can be efficiently monitored by one overall indicator and the detected tool faults can be systematically diagnosed at the sensor level.

Optimizing the Critical Dimension of the STI etch process by integrating inline scatterometry measurements and Feedback R2R control

As features sizes continue to decrease, process control has become essential to control profile and Critical Dimension (CD) uniformity across the wafer. In order to reduce the CD of the Shallow Trench Isolation (STI) process, we propose a method to model the plasma etch process of the STI module in CMOS technology by integrating a Feedback R2R control loop and inline scatterometry measurements.