We at the Emerging Nanomaterials and Technology Research Laboratory are searching for ways to explore emerging device technologies and systems to craft heterogeneous circuits with the potential to pioneer unprecedented applications. On one hand, we use traditional (eg. amorphous-Si) and emerging nanomaterials (eg. 2D  Materials, phase-change materials, …) to build new emerging electron devices and computing architectures.  On the other hand, we use computational tools to develop new device and circuits concepts.

Large-scale Integration of Emerging Devices

Emerging nanomaterials have currently been used to prototype new and exciting devices. However, these prototypes are limited to one or a few devices, for developing systems and doing a transition lab to fab. Our goal is to allow the large-scale integration of these emerging devices.  

Selected Publications:

  • Migliato Marega, G., Ji, H.G., Wang, Z. et al. A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories. Nature Electronics 6, 991–998 (2023). https://doi.org/10.1038/s41928-023-01064-1
  • G. M. Marega et al., “How to Achieve Large-Area Ultra-Fast Operation of MoS2 Monolayer Flash Memories?,” in IEEE Nanotechnology Magazine, vol. 17, no. 5, pp. 39-43, Oct. 2023, doi: 10.1109/MNANO.2023.3297118

 

Emerging Computing Technologies

New emerging materials allow the development of new architectures and technologies. Our goal is to explore solution beyond the traditional technologies. We are currently looking into new emerging system based on emerging materials for developing new neuromophic, in-memory and monolithic 3D integration architecures.

Selected Publications:

  • Migliato Marega, G., Zhao, Y., Avsar, A. et al. Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020). https://doi.org/10.1038/s41586-020-2861-0
  • Guilherme Migliato Marega, Zhenyu Wang, Maksym Paliy, Gino Giusi, Sebastiano Strangio, Francesco Castiglione, Christian Callegari, Mukesh Tripathi, Aleksandra Radenovic, Giuseppe Iannaccone, and Andras Kis. Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2 . ACS Nano 2022 16 (3), 3684-3694 .DOI: 10.1021/acsnano.1c07065
  • Samizadeh Nikoo, M., Soleimanzadeh, R., Krammer, A. et al. Electrical control of glass-like dynamics in vanadium dioxide for data storage and processing. Nature Electronics 5, 596–603 (2022). https://doi.org/10.1038/s41928-022-00812-z

Computation Methods for Material-Device-System Design

Due to the modern constraints of device scaling, simply exploring new materials with traditional device structures may not lead to optimal performance on a system level. Our approach explores how to combine new materials with their optimal device/system counterparts. For this, we are using new computational methods for achieving co-design from the material level to the system level.