P. S. Krishnaprasad - US grants

; R o o t E n t r y F W g C o m p O b j b W o r d D o c u m e n t O b j e c t P o o l 3 g 3 g - . / 0 1 2 3 4 5 6 7 F Microsoft Word 6.0 Document MSWordDoc Word.Document.6 ; 9527576 Krishnaprasad This award is the University of Maryland with a sub-contract to North Carolina State University at Raleigh. The overall goal of this effort is to demonstrate a methodology for sensor-integrated control of rapid thermal chemical vapor deposition (RTCVD) of polycrystalline silicon (poly Si) from silane with focus on controlling deposition thickness and across-wafer uniformity. The project exploits advances in real-time sensors, including pyrometry for temperature, thermal imaging for temperature uniformity, and sampling mass spectrometry for thickness metrology and process ambient monitoring. Reduced-order process models constructed from high fidelity heat and fluid flow simulations, together with physically-based dynamic equipment, process, and sensor simulations, are the basis for control models. Resulting run-to-run control methodologies for controlling deposition thickness and across-wafer uniformity are being developed and validated experimentally, and real-time control approaches are being explored. These run-to-run control approaches will be extendible to real-time c ontrol. An architecture to support a basic supervisory control component is being demonstrated, using physically-based dynamic simulation to determine sensor signatures of specific equipment failure modes, together with advanced algorithms as interference tools for detecting sensor signal correlations and identifying indicated equipment/process malfunction. The investigators at the University of Maryland provide the effort on simulation and control, while the investigators at North Carolina State University provide the effort on sensors and on rapid thermal chemical vapor deposition of polycrystalline silicon. The experimental proof of concept of the control system will be performed in the cluster tool deposition apparatus at North Carolina State University. *** 0 0 Oh +' 0 $ H l D h , \\CLM15\SMURPHY$\WWUSER\TEMPLATE\NORMAL.DOT S u m m a r y I n f o r m a t i o n ( , 9527576 SHERONDA MURPHY SHERONDA MURPHY @ X g @ @ X g @ Microsoft Word 6.0 2 ; e = e d d l l l l l l l 1 % D T G 9 l l l l l l l l l s 9527576 Krishnaprasad This award is the University of Maryland with a sub-contract to North Carolina State University at Raleigh. The overall goal of this effort is to demonstrate a methodology for sensor-integrated control of rapid thermal chemical vapor deposition (RTCVD) of polycrystalline silicon (poly Si) from silane with focus on controlling deposition thickness and across-wafer uniformity. The project exploits advances in real-time sensors, including pyrometry for temperature, thermal imaging for temperature uniformity, and sampling mass spectrometry for thickness metrology and process ambient monitoring. Reduced-order process models constructed from high fidelity heat and fluid flow simulations, together with physically-based dynamic equipment, process, and sensor simulations, are the basis for control models. Resulting run-to-run control methodologies for controlling deposition thickness and across-wafer uniformity are being developed and validated experimentally, and real-time control approaches are being explored. These run-to-run control approaches wi

9720334 Krishnaprasad The goal of this research project is to investigate time coding in the central nervous system, specifically the auditory system of the barn owl, the early development of such codes, the learning of associated maps, and the exploitation of such sound codes and maps in source localization and sound separation. The approach consists of electrophysiological and anatomical study, coupled with mathematical modeling of neural circuitry, the rigorous investigation of the structure and performance of relevant learning algorithms and the creation of an experimental robotic testbed. This testbed, a binaural head, is intended to be capable of orienting itself to sound sources in complex acoustic environments through pure auditory servoing, by utilizing the development of control architectures capable of learning maps of the auditory space of the robot, and drawing upon an evolving understanding of barn owl auditory system. The results of this research will provide insights into the design of novel roles for auditory sensing, interpretation and discrimination in autonomous robotic systems. This research could lead to applications in hands-free human-machine communications in acoustically cluttered environments and in monitoring complex environments such as highly automated manufacturing plants.

0243803 Gupta

This award funds a five-year Research Experience for Undergraduates (REU) Site at the University of Maryland for fifteen students each summer for twelve weeks for research opportunities at the university's Institute for Systems Research. Students at colleges, universities, and community colleges will be recruited nationwide through a process involving efforts to reach students who would otherwise not have access to a research experience. The program incorporates activities that will involve participants in the following research directions of the institute: global communications systems, sensor-actuator networks, next-generation product realization systems, societal infrastructure systems, and cross-disciplinary systems education. Through the program students will be able to (1) establish a basis for systems thinking by conducting research and thus understand systems engineering as a discipline; (2) acquire broader and deeper understanding of both the research process and the practice of engineering and how new knowledge is created and communicated; (3) develop multicultural understanding and team competence and become aware of the societal implications of research; and (5) successfully seek admission in a four-year program and/or graduate school.

Autonomous systems in general and self-driving cars in particular, hold the promise to be one of the most disruptive technologies emerging in recent years. However, the safety and resilience of these systems, if not proactively addressed, will pose a significant threat potentially impairing our relationship with these technologies and may lead to a societal rejection of adopting them permanently. This project seeks to address such concerns by equipping autonomous systems with an additional layer of intelligence allowing them to be resilient-by-cognition.

The research addresses resilience for autonomous cyber-physical systems (CPS) by integrating concepts from game theory, formal methods, and controls. Our proposed approach includes: (i) a principled framework for formally reasoning about cognitive CPS; that is, given a set of strategies captured in a formal language (e.g., temporal logic), the proposed framework builds on ideas from evolutionary game theory to understand which strategies lead to the best fit when operating in adversarial environments (ii) On-the-fly, correct-by-design feedback controller synthesis that executes the chosen strategy while satisfying physical constraints imposed by the micro-dynamics of the underlying CPS (iii) a data-driven strategy-mining approach that addresses the fundamental problem of designing the library of strategies from human demonstrations. We will illustrate our approach over key applications including self-driving cars and autonomous drone swarms. Our educational plan engages not only graduate students but also high school and undergraduate students. It also reaches out to engineers and the lay public, by providing open source implementations of our algorithms making them available both to industry and independent developers.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.