CPS: Medium: Real-Time Learning and Control of Stochastic Nanostructure Growth Processes Through in situ Dynamic Imaging

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This Cyber-Physical Systems (CPS) grant will support research that will contribute new knowledge related to emerging monitoring and control techniques of the growth of nanomaterials, which are crucial for applications such as new types of batteries and photovoltaic devices, because precise structuring of matter is essential to realize the desired charge, mass, and energy flow patterns that underpin energy conversion and storage. With the fast arrival of tremendous amount of data produced by dynamic nanoscale imaging, the National Nanotechnology Initiative has identified the lack of in-process monitoring and control as a grand challenge impeding the design and discovery of new materials, because "existing methods are time-consuming, expensive, and require high-tech infrastructure and high skill levels to perform." This grant supports a multidisciplinary team, comprising experts from data science, control, circuit design, and material sciences, aiming to tackle this challenge by designing a cyber-physical system that can reliably convert dynamic imaging data to machine intelligible information for process monitoring and control. The results from this research will benefit nanomaterial discovery and pave a path to scalable production. The multidisciplinary approach will help broaden participation of underrepresented groups in research and positively impact science and engineering education. The intellectual core of this research is to address the foundational problem of real-time learning and control of a multivariate, nonparametric model of probability density functions that reflect the collective stochastic behavior of evolving nano objects. Several scientific and engineering barriers are to be overcome, including efficient methods for nonparametric learning, adaptive stochastic control of evolving empirical distributions of nano objects, and hardware/software co-designs for boosting computational efficiency and meeting real-time requirements. The research team will test randomized shallow architectures, which are a novel approach for addressing real-time nonparametric learning issues, explore structured-shallow-networks-enabled process control, which will be one of the early applications of neural networks in real-time dynamic settings, and design hardware accelerators for select algorithms that enables on-the-fly solution for high-precision scalable nanomanufacturing.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.