AI for 2D Materials and 2D Materials for AI
Saptarshi Das a
a The Pennsylvania State University, United States
Proceedings of Neuronics Conference 2025 (Neuronics25)
Tsukuba, Japan, 2025 June 17th - 20th
Organizers: Takashi Tsuchiya, Chu-Chen Chueh, Sabina Spiga and Jung-Yao Chen
Invited Speaker, Saptarshi Das, presentation 023
Publication date: 15th April 2025

In this talk, I will explore the intersection of artificial intelligence (AI), advanced materials, and novel computing architectures, focusing on the development of bio-inspired circuits for neuromorphic and edge sensing applications. By leveraging the unique properties of two-dimensional (2D) materials, such as MoS2, WSe2, and graphene, I will demonstrate their potential in enabling energy-efficient, scalable, and highly adaptable circuits that emulate biological neural systems. These circuits are particularly well-suited for edge computing and sensing tasks, where compactness and power efficiency are critical.

One example of this approach is leveraging AI and machine learning (ML) to enhance the performance of graphene-based electronic tongue for food safety, environmental monitoring, and healthcare applications (Nature, 634, 572–578, 2024). Furthermore, we have demonstrated bio-inspired computing paradigms that replicate complex biological processes, including auditory processing in barn owls, collision avoidance in locusts, probabilistic computing in dragonflies, and multisensory integration in octopuses. By combining 2D materials with bio-inspired principles, we are paving the way for compact, functionally diverse integrated circuits that harness the power of neuromorphic processing.

Finally, beyond bio-inspired circuit design, I will delve into the transformative potential of monolithic 3D integration using emerging 2D field-effect transistors (FETs). Our recent breakthroughs in wafer-scale 2-tier and 3-tier 3D integration with MoS2 and WSe2 FETs have enabled the realization of multifunctional circuits, paving the way for the next generation of logic and memory devices (Nature, 625, 276-281, 2024). These advancements are critical for overcoming the limitations of conventional scaling and unlocking new possibilities in high-density, low-power electronics, particularly for mimicking the three-dimensional structure of the brain.

© FUNDACIO DE LA COMUNITAT VALENCIANA SCITO
We use our own and third party cookies for analysing and measuring usage of our website to improve our services. If you continue browsing, we consider accepting its use. You can check our Cookies Policy in which you will also find how to configure your web browser for the use of cookies. More info