NSF Young Investigator: Toward Neural Network Based Modeling, Control and Diagnosis of Semiconductor Manufacturing Processes

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9358163 May The research involves applying implementation of computer- integrated manufacturing (IC-CIM) techniques to two critical fabrication processes used in the production of high performance multichip modules and other integrated circuits: plasma-enhanced chemical vapor deposition (PECVD) and reactive ion etching (RIE). These precesses are used in the process sequence to deposit and etch silicon dioxide dielectric layers. Several aspects of computer-integrated manufacturing successfully applied to other etching and deposition processes. For these applications, traditional statistically-based approaches to process modeling, control and diagnosis represent the predominant methods to achieve IC-CIM objectives. However, more recent efforts have shown that neural networks offer great promise in process modeling. In fact, neural process models have been shown to be both more accurate and more robust than their statistical counterparts. This is due to the high degree of parallelism and connectivity in these networks, which crudely mimics the architecture of the human brian. It is anticipated that the innovative use of neural networks will also have a significant impact in process monitoring, control, and equipment diagnosis as well. This program of research will investigate and implement several novel neural network based approaches to the monitoring, modeling, control, and diagnosis of RIE and PECVD. These techniques will be applied specifically to a high performance multichip module program, but the methodology developed will be generic and readily applicable to other electronics manufacturing processes and equipment.