A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence

A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable “smart” nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this

Algorithmic design of photonic structures with deep learning

The advent and development of photonics in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of photonic structures and devices, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell’s equations until a locally optimized solution can be attained. Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. The objective of this PhD thesis is to explore deep learning models and optimization approaches for the design of future photonic devices, with various applications such as imaging, hologram, sensing, and display. In specific, the theme of thesis is to utilize various deep generative models to find simple representations for highly complex photonic structures, such that optional optimization algorithms can be efficiently applied to identify the photonic structures with optimal performance. The developed design framework has potential applications in the optimization of future highly compact optical systems such as photonic computing, LIDAR, telecommunications, and virtual/augmented reality display.Ph.D