John M. Yamartino

Automated fault detection and classification of etch systems using modular neural networks

Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDC) using tool data in plasma etching. Principal component analysis (PCA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy C-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

Investigations of rate coefficients in the Cl model

Investigations of ion distributions in Cl plasmas in the Applied Materials Decoupled Plasma Source chamber have been undertaken through the use of simulation. The Hybrid Plasma Equipment Model plasma simulation software was employed. These simulations were performed to investigate which reactions may be considered significant in the DPS tool according to the current Cl model. In addition, new reactions that have been proposed internally to explain some experimental results were also tested to determine their significance. One of the reactions that was determined to be significant is Cl/sup +/ to Cl/sub 2//sup +/ charge exchange reaction which influences the ratio of Cl/sup +/ ions to Cl/sub 2//sup +/ ions in the DPS chamber. This reaction may influence the ability of Al etch processes to remove Cu residue.

In-tool process control for advanced patterning based on integrated metrology

Advanced Process Control (APC) and Advanced Equipment Control (AEC) which has come to the foreground with current design nodes is expected that the 65nm node requires implementation of both. The main goal has been to improve overall fab efficiency (OEF) with better tool utilization. This paper has described the necessary aspects of process control to reduce variance by utilizing the tool-level data for both AEC and APC that were available with leading edge etch systems. In terms of AEC, the tool-level data can be analyzed for various important applications including chamber baselining, chamber matching, process monitoring, wafer state monitoring and process optimization. With regards to APC the use of tool-level metrology and process information can be used to control and improve the on-wafer performance and to achieve the tight tolerances for advanced fabrication.

The sensitivity of electron shading damage to electron temperature, electron density and the plasma-to-wafer electron energy threshold

The electron temperature, T/sub e/, is a well known critical parameter affecting electron shading damage (ESD). This parameter is considered important because the damaging part of the electron density comes from high energy tail of the electron energy distribution. As the size of the high energy tail is dependent upon T/sub e/, obtaining low T/sub e/ in plasma processing is considered an important approach to reducing ESD. Recent results in metal etching (Tokashiki et al., 1998) suggest that ESD not only depends on T/sub e/ as previously suspected but also that the electron density, n/sub e/, may play an important role. We introduce a new parameter, the electron energy threshold, and demonstrate that it may be as important as T/sub e/ or n/sub e/ in the mechanism for electron shading damage. This new parameter, E/sub th/, is the energy threshold for electrons to reach the wafer surface and is related to the plasma potential as well as the wafer DC bias. This threshold aspect of electron shading damage could explain why a particular class of recipes (high bias power/low source power) show improved damage performance in inductively coupled plasma (ICP) etching tools (Tokashiki et al, 1998; Tabara, 1997; Hashimoto et al., 1997; Karzhavin and Wu, 1998). In addition, we provide experimental evidence obtained on an Applied Materials DPS Metal Etch Centura which supports this explanation.

Mask etcher data strategy for 45nm and beyond

Mask Etching for the 45nm technology node and beyond requires a system-level data and diagnostics strategy. This necessity stems from the need to control the performance of the mask etcher to increasingly stringent and diverse requirements of the mask production environment. Increasing mask costs and the capability to acquire and consolidate a wealth of data within the mask etch platform are primary motivators towards harnessing data mines for feedback into the mask etching optimization. There are offline and real-time possibilities and scenarios. Here, we discuss the data architecture, acquisition, and strategies of the Applied Materials Tetra IITM Mask Etch System.