Related papers: A back-end, CMOS compatible ferroelectric Field Ef…
Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through…
Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications, including logistical planning, resource allocation, chip design, drug explorations, and more. Due to their critical significance and the…
Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. We report on a class of neuromorphic metamaterials embodying bioinspired mechanosensing, memory, and…
Ferroelectric field-effect-transistor (FEFET) has emerged as a scalable solution for 3D NAND and embedded flash (eFlash), with recent progress in achieving large memory window (MW) using metal-insulator-ferroelectric-insulator-semiconductor…
Memristive circuit elements constitute a cornerstone for novel electronic applications, such as neuromorphic computing, called to revolutionize information technologies. By definition, memristors are sensitive to the history of electrical…
FeFETs hold strong potential for advancing memory and logic technologies, but their inherent randomness arising from both operational cycling and fabrication variability poses significant challenges for accurate and reliable modeling.…
Non-volatile memories (NVMs) have the potential to reshape next-generation memory systems because of their promising properties of near-zero leakage power consumption, high density and non-volatility. However, NVMs also face critical…
In the last decade, a 2-terminal passive circuit element called a memristor has been developed for non-volatile resistive random access memory and has more recently shown promise for neuromorphic computing. Compared to flash memory,…
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking…
We report the fabrication and testing, at 4.2 K, of an SISFS device, where S, F, and I denote a superconductor (Nb), a ferromagnetic material (permalloy), and an insulator (AlOx), respectively. The F layer covers about one half of the top…
Spin field-effect transistors (SFETs) are promising candidates for low-power spin-based electronics, yet existing realizations that rely on spin-orbit coupling are constrained by limited material choices and short spin-coherence lengths.…
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates…
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate. Even though several algorithmic…
Brain-inspired computing has the potential to revolutionise the current von Neumann architecture, advancing machine learning applications. Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical…
Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines…
Two-dimensional ferroelectric materials are beneficial for power-efficient memory devices and transistor applications. Here, we predict out-of-plane ferroelectricity in a new family of buckled metal oxide (MO; M: Ge, Sn, Pb) monolayers with…
We analyze the distributions of electric potential and field, polarization and charge, and the differential capacitance of a silicon metal-oxide-ferroelectric field effect transistor (MOSFET), in which a gate insulator consists of thin…
Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint. However, digital hardware implementation of bionic learning…
Analog switching in ferroelectric devices promises neuromorphic computing with highest energy efficiency, if limited device scalability can be overcome. To contribute to a solution, we report on the ferroelectric switching characteristics…
The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at device level to enable novel…