Brain inspired computing

Neuromorphic computing falls in one of my sweet spots. From a theoretical point of view, one of the key questions is to understand the types of computations that can be carried out in dynamical systems, and how the restrictions in architecture affect our ability to implement algorithms. Then there is the problem of how to best implement such systems, and the tradeoff between using new materials or taking advantage of conventional CMOS to design neuromorphic chips.

The insect brain as inspiration to design smart, dynamic sensors

While a lot of focus has been on the brain, my research is heavily inspired by the central nervous system of insects, and the integration of sensing, computing, and execution using neural architectures. This can have a tremendous impact for instance in the area of advanced sensors and robotics.

One of the interesting aspects of insects is that, from an evolutionary standpoint, they have already developed a lot of the structural complexity and diversity found in vertebrates, yet the number of neurons and connections is more manageable, in some cases within the range of existing technology.

Neuromorphic computing as a materials design problem

There are thousands of research papers on various types of emergent devices (usually referred to as memristors) that implement behaviors that are inspired on those of biological neurons. Likewise, from a circuit perspective, the so-called cross-bar arrays have been explored in depth in the literature as a way of incorporating these devices into more complex architectures.

However, a key challenge is that we do not know what ideal properties these devices should have for practical applications. In fact, we don’t even have a good way of quantifying the advantages these emergent devices could bring to general purpose computing applications. This is an important barrier that hinders the tech transfer of any lab-scale breakthroughs.

My research seeks to strengthen this connection between application and the ideal device and materials properties. Through research projects funded both internally at Argonne as part of DOE’s microelectronics program, we leverage AI and machine learning techniques to connect applications in the area of detectors with novel materials.

In parallel to this effort, we are exploring the potential of agentic AI integrated with semiconductor manufacturing technique such as atomic layer deposition to accelerate our ability to do science and explore a wider range of materials.