Frontiers of Materials Award Symposium: Low-Dimensional Materials and Interfaces for Next Generation Computing: Session I
Program Organizers: Deep Jariwala, University of Pennsylvania

Tuesday 8:30 AM
March 16, 2021
Room: RM 19
Location: TMS2021 Virtual


8:30 AM  
Introductory Comments: Frontiers of Materials Award Symposium: Low-dimensional Materials and Interfaces for Next Generation Computing: Deep Jariwala1; 1University of Pennsylvania
    Introductory Comments

8:35 AM  Invited
Gate-tunable Neuromorphic Devices Enabled by Low-dimensional Materials: Mark Hersam1; 1Northwestern University
    The exponentially improving performance of conventional digital computers has slowed in recent years due to the speed and power consumption issues that are largely attributable to the von Neumann bottleneck (i.e., the need to transfer data between spatially separate processor and memory blocks). In contrast, neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. In addition to reducing power consumption in conventional computing, neuromorphic devices also provide efficient architectures for emerging applications such as image recognition, machine learning, and artificial intelligence. This talk will explore how the reduced dielectric screening in low-dimensional nanoelectronic materials enables opportunities for novel gate-tunable neuromorphic devices. Overall, this work introduces new foundational circuit elements for neuromorphic computing in addition to providing alternative pathways for utilizing the unique quantum characteristics of low-dimensional nanoelectronic materials.

9:15 AM  Keynote
2D/3D Heterostructures for Low-power Logic and Memory Devices: Deep Jariwala1; 1University of Pennsylvania
    The power and efficiency of conventional computers continues to rise despite the slowdown or “death” of Moore’s law. This progress has been largely enabled by clever engineering at the architecture level of hardware as well as software. However, the growing need to process vast amounts of data for internet of things and other “big data” applications means conventional computers have to become more energy efficient. There is where low-dimensional materials such as 2D semiconductors present a unique opportunity, particularly in conjunction with conventional, commercialized 3D materials such as silicon and III-nitrides. Here, we will present our recent work on gate-tunable diode and tunnel junction devices based on integration of 2D chalcogenides with Si and GaN. Following this I will present our recent work on non-volatile memories based on Ferroelectric Field Effect Transistors (FE-FETs) made using a heterostructure of MoS2/AlScN as well as AlScN-based Ferroelectric Tunnel Junction (FTJ) devices.

9:55 AM  Invited
Ferroelectrics: From Memory to Computing: Suman Datta1; 1University of Notre Dame
    The discovery of ferroelectricity in doped hafnium dioxide thin films has ignited activity in exploration of CMOS compatible ferroelectric FETs for a range of applications from low-power logic to embedded non-volatile memory to in-memory compute kernels. Here, we will present key milestones in the development of Ferroelectric Field Effect Transistor technology (FeFETs) and the emergence of a versatile ferroelectronic platform. FeFETs exhibit superior energy efficiency and high performance as embedded nonvolatile memory. When embedded into an SRAM or D-flip-flop, they enable fast data backup and recovery for IoT applications powered intermittently by energy harvesters. The polarization switching dynamics in multi-domain ferroelectric can be harnessed to develop analog synaptic weight cell for deep learning accelerators. We have demonstrated ultra-compact FeFET-based in-memory hardware prototype, such as ternary content addressable memory, for memory augmented neural networks suitable for few-shot learning. Therefore, ferroelectronics has emerged as a promising platform for improving embedded memory performance.