CAREER: Multi-Functional Moire Synaptic Transistors Using 2D Materials for Intelligent Sensing and Computing

About This Grant

Realizing Artificial Intelligence (AI) requires modern hardware that is capable of processing complex analog data with an efficiency comparable to the human brain. However, existing semiconductor devices struggle to process large volumes of data due to fundamental limitations like high-power consumption, limited functionality, and low number of memory states. This project seeks to develop a new type of neuromorphic device called moiré synaptic transistors to address these limitations. By putting multiple atomically-thin layers of two-dimensional materials together with specific alignments, these devices are able to achieve lower power consumption, higher memory state density, more tunability and more biomimicking functionalities. These devices can be used to meet the precision and energy demands of modern computing in AI and enable applications like AI-powered robots, autonomous drones, and personalized wearable electronics. The educational outreach goal of this project will aim to increase awareness of some of the most exciting concepts and challenges at the intersection of semiconductor manufacturing and neuromorphic computing applications among all levels of learners (i.e., pre-school, K-12, undergraduate, graduate, and working adults), with a special focus on providing research opportunities to K-12 and undergraduate students. The research program will be tightly integrated with training future technical leaders to have the capabilities to tackle interdisciplinary problems in these fields. This project aims to address critical challenges in advancing AI hardware by developing moiré synaptic transistors (MSTs), a new generation of devices built by stacking and twisting two-dimensional materials. These devices capitalize on excitonic ferroelectricity, a unique phenomenon in correlated 2D moiré heterostructures, to achieve high memory density, low power consumption, high operation speed, high reconfigurability, and novel biomimicking functionality. Advanced fabrication techniques will be explored to design new MSTs with optimized performance and scalability. These will be applied to innovative computing-in-memory-and-sensor applications using neural-network-based MSTs. Integration with CMOS circuits and neuromorphic computing frameworks will allow MSTs to achieve unprecedented functionality and efficiency in analog computing systems. This project will create simulation and circuit design models of MSTs and establish a co-design framework that links excitonic ferroelectric physics, MST devices, and circuit design. These innovations are expected to enrich the fundamental understanding of excitonic ferroelectricity, create MST device innovations, and advance circuit-level applications based on MSTs for AI hardware and wearable electronics. The education plan will synchronize with the research plan by enhancing education in semiconductor device fabrication, characterization, and device-circuit-algorithm co-design techniques to foster the development of the next generation of interdisciplinary STEM leaders. 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.