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Suggested Citation:"FEATURE SCALE MODELS." National Research Council. 1996. Database Needs for Modeling and Simulation of Plasma Processing. Washington, DC: The National Academies Press. doi: 10.17226/5434.
Page 16

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16 DATABASE NEEDS FOR MODELING AND SIMUI.ATION OF PI.ASMA PROCESSING FEATURE SCALE MODELS It is clear that the evaluation of etching or deposition reactors is eventually governed by the feature profiles they develop, in addition to questions of uniformity that can be addressed by tool scale simulations. All choices of equipment type, reactor conditions, and chemistry are judged by etch profiles and/or deposition conformality. Thus, the ideal simulation, from the process engineer's point of view, would produce this profile as an output. The inputs would include the feature geometry, as well as the reactor operating conditions. It should be recognized that it is expensive and often inconvenient to measure feature profiles directly with cross sectional views through scanning electron microscopy (SEM) images. This is especially true when many different runs must be made to determine the effects of tool operating conditions on profiles. It is therefore an important goal to link tool scale models with feature scale models to satisfy the needs of the process engineer. At the current time, it is not yet clear what the weakest link in this chain is. It is possible that the output from the tool scale model is not sufficiently accurate or detailed to provide the feature scale model with input information that will allow accurate predictions of feature profiles. The feature scale models may not be able to use all of the information concerning species composition and velocities coming from the tool scale model because of a lack of a good model for surface processes. One possible scheme to couple reactor or tool scale input into the profile simulator is shown in Figure 2.2. The reactor scale information is sent to a model of the sheath region. The sheath region is the thin (- 102 µm to 5 mm) boundary layer just above the surface through which ions are accelerated by relatively strong electric fields. Depending on the conditions (frequency and magnitude of the sheath voltage, sheath thickness, gas pressure, and so on), the ion composition and velocity distribution will be affected. A model of these effects is therefore required since the feature evolution will be affected by these parameters. Given the transport mechanisms within the feature (e.g. ballistic transport and/or surface diffusion) and a model of the surface reaction kinetics, the feature profile shape can then be predicted. Usually, however, the model needs to be calibrated with specific data under conditions similar to the ones that are of interest in the particular simulation. Obviously, one would hope that this calibration step would eventually be minimized or avoided altogether. In the process shown in Figure 2.2, there are several external inputs to three of the Reactor Scale Input steps. The transport mechanism requires information from a test structure. These are specially designed features such that, Sheath Model (MC. ..) when processed in the plasma reactor, the changes in their shape can be conveniently translated into information regarding transport and surface kinetics. In effect, these test structures are used to calibrate the models for other feature shapes. In Test Structures Dominant Surface Transport addition to information from test structure experiments, data for Mechanism, Modeling surface reaction kinetics can be obtained from surface science experiments, and from atomistic simulations such as molecular dynamics. 3 Surface Reaction Kinetics An example of a feature scale simulation is illustrated in Figure 2.3. In this example, the goal is to use a combination of plasma-enhanced chemical vapor deposition (PECVD) with tetraethoxysilane (TEOS) to deposit a silicon oxide film, and Process Data Calibration with Specific Data argon sputtering to control the film shape to avoid the formation of a void. The sequence begins with a step (Figure 2.3a) in which the PECVD film has been deposited nearly conformally Prediction in the metal trench. The second step (Figure 2.3b) is to sputter (with Ar+ in this case) part of the oxide film to open the trench. FIGURE 2.2 Strategy for feature-scale The third step (Figure 2.3c) is to deposit the oxide again, modeling and characterization. (Courtesy resulting in a film that does not have the undesirable void space ofV. Singh, Intel Corporation.)

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In spite of its high cost and technical importance, plasma equipment is still largely designed empirically, with little help from computer simulation. Plasma process control is rudimentary. Optimization of plasma reactor operation, including adjustments to deal with increasingly stringent controls on plant emissions, is performed predominantly by trial and error. There is now a strong and growing economic incentive to improve on the traditional methods of plasma reactor and process design, optimization, and control. An obvious strategy for both chip manufacturers and plasma equipment suppliers is to employ large-scale modeling and simulation. The major roadblock to further development of this promising strategy is the lack of a database for the many physical and chemical processes that occur in the plasma. The data that are currently available are often scattered throughout the scientific literature, and assessments of their reliability are usually unavailable.

Database Needs for Modeling and Simulation of Plasma Processing identifies strategies to add data to the existing database, to improve access to the database, and to assess the reliability of the available data. In addition to identifying the most important needs, this report assesses the experimental and theoretical/computational techniques that can be used, or must be developed, in order to begin to satisfy these needs.

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