2D Semiconductor Memristors towards Neuromorphic Hardware Applications

This grant supports research that advances key knowledge and techniques for creating electronic devices for fabrication of future artificial intelligence systems to enhance U.S. technological competitiveness and national prosperity. The current artificial intelligence systems, such as artificial neural networks are still based on conventional computing principles, which do not match biological neuronal processes and result in a formidable computing complexity and unacceptable power consumption for scale-up implementation.

To address this challenge, this proposal supports fundamental research to explore critical device physics knowledge for the realization of new memristive switching devices (or memristors) based on 2D nanomaterials which have a high biological similarity, potentially enabling emulation of biological neuronal functions. The hardware-based artificial neural network systems constructed from such devices are anticipated to be capable of executing emerging brain-like neuromorphic computing algorithms and enable superior inference capability as well as power efficiency comparable to those of biological counterparts.

Such neural network systems, if successfully developed could be implemented to a broad range of applications, such as controlling of unmanned vehicles, processing of complicated computer vision data, and rapid diagnosis of illness based on machine learning, thereby greatly improving the data processing capability of the systems. In addition, the scientific and technical results from this work will also promote capability in developing advanced computing and robotic systems.

This research also enhances participation of students and educators from underrepresented groups in the education activities related to electronics, integrated circuit chips, advanced controlling and computing techniques.

The newly proposed 2D semiconductor memristors are anticipated to exhibit several advantageous properties in comparison with state-of-the-art memristors based on bulk materials, including dangling-bond-free surfaces that potentially enable cost-efficient production of device structures with the higher device integration density, the lower threshold voltages and energies for switching states, the higher level of interconnectivity among devices, and the larger number of available device states. These desirable properties could be further leveraged for addressing the aforementioned challenge related to hardware-based neural networks.

In spite of such anticipated advantages, the ultimate realization of the neural network systems based on 2D semiconductor memristors demands the research efforts to address several important device-oriented challenges. Specifically, the synaptic weight update characteristics of 2D semiconductor memristors need to be improved to be linear and symmetric in response to pulse-like encoding signals, and new device doping/integration techniques are needed to form different synaptic regions for emulating bio-realistic functions.

In addition, more experimental attempts for constructing small-scale networks consisting of 2D semiconductor memristors need to be performed, seeking to exploring the neuromorphic computing algorithms that can fully harvest the aforementioned advantages of 2D semiconductor based memristive devices in processing dynamic spatiotemporal signals. To address these challenges, the PI will perform a series of research tasks to produce reliable 2D semiconductor memristors suitable for practical network implementation and also preliminarily demonstrate small-scale networks for neuromorphic control applications.

The specific sub-aims include: (1) Obtain an in-depth understanding of the memristive switching schemes of 2D semiconductor memristors at the microscopic level and produce memristors with improved synaptic weight update characteristics; (2) Realize scalable integration of 2D memristors with deterministic and uniform synaptic properties; (3) Preliminarily demonstrate a small-scale network consisting of 2D semiconductor memristors for temporal data analysis.

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.