An artificial neural network is a machine learning technique that mimics the operation of a human brain. These networks are typically implemented using electronics, and they have been responsible for many recent technological advancements. Neural networks that use light—optical neural networks—could potentially perform calculations even faster, potentially at the speed of light. However, full-scale optical networks competitive with electronics have not been demonstrated, as they lack the critical element that allows artificial neural networks to make decisions. In this program, new optical devices will be developed that fill in this missing puzzle piece. By growing atomically-thin layers of different materials on one another, the first intersubband neurons will be created. These are devices that will be able to make decisions based on the amount of light that hits them, and they will eventually allow for ultrafast optical neural networks to be developed. This could directly benefit many fields, as it could provide direct speed-up of many computing tasks. In addition, it could allow for information processing that does not use electronics at all! This program integrates research and education, having broader impacts on the community. It will develop an optics outreach program for a middle school in South Bend, one that introduces students to important concepts and will allow them the opportunity to see a real research lab in action. It will also develop a summer research program for undergraduates from underrepresented groups, as well as a new graduate course on nonlinear optics.
The main goal of this program is to develop new intersubband photonic devices for information processing, ultimately culminating in the first optical neural networks capable of high-speed operation. Deep learning based on neural networks has revolutionized computation. By cascading linear matrix multiplications with nonlinear activation functions, a deep neural network can learn many tasks. In principle, optical neural networks could perform calculations at the speed of light, thousands of times faster than electronic networks. Unfortunately, while light is excellent at computing the linear part of the network, it cannot so easily compute the nonlinear part. Optical nonlinearities are fast but notoriously small. In this program, a nanostructure will instead be designed that blends an optical element with a nonlinear electronic element. This program will utilize the physics of intersubband transitions to make intersubband neurons, nonlinear devices expected to operate at speeds much faster than existing devices and with lower optical powers. Several novel design strategies have been developed that can implement low-threshold, low power consumption, high-speed artificial neurons, and in this program, they will be experimentally demonstrated and characterized. Neurons will be also be developed at shorter wavelengths using an emerging material system in order to improve the scalability and long-term viability of this concept. The intellectual merit of this program is that it will lay the groundwork for a completely new approach to optical neural networks, one that seamlessly blends the best features of electronics and the best features of photonics. Though intersubband physics have previously been exploited to make sources, detectors, and sensors, they have yet to make an impact in computation—this program will do just that. It will make important contributions at the intersection of optics, electrical engineering, and computer science.
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.
