Search for: All records

Big Data has an insatiable appetite for larger and better-performing memory. While current memory technologies continue to advance, the performance gaps in current memory and storage technology have motivated the exploration of emerging memory technologies capable of providing new functionalities. Ferroelectric memory is one such promising candidate which has recently experienced a revival after the discovery of ferroelectricity in hafnium dioxide (HfO₂) – the dielectric of choice in advanced CMOS manufacturing. While the commercial viability of ferroelectric memory technology has made significant progress over the past decade, several challenges related to variation and reliability still stand as a barrier to large-scale commercial implementation. Here, we review some of the outstanding challenges of ferroelectric memory technology along with the recent materials and device innovations that are being considered to overcome them. Moreover, we aim to highlight these challenges as materials and device co-design problems that must be addressed through collaborative efforts that straddle the two disciplines. We identify and provide our perspective on some of the key challenges and opportunities for ferroelectric-based microelectronic technology. Ferroelectrics non-volatile memory, in-memory computation 

This study explores the Faraday instability as a mechanism to enhance heat transfer in two-phase systems by exciting interfacial waves through resonance. The approach is particularly applicable to reduced-gravity environments where buoyancy-driven convection is ineffective. A reduced-order model, based on a weighted residual integral boundary layer method, is used to predict interfacial dynamics and heat flux under vertical oscillations with a stabilising thermal gradient. The model employs long-wave and one-way coupling approximations to simplify the governing equations. Linear stability theory informs the oscillation parameters for subsequent nonlinear simulations, which are then qualitatively compared against experiments conducted under Earth’s gravity. Experimental results show up to a 4.5-fold enhancement in heat transfer over pure conduction. Key findings include: (i) reduced gravity lowers interfacial stability, promoting mixing and heat transfer; and (ii) oscillation-induced instability significantly improves heat transport under Earth’s gravity. Theoretical predictions qualitatively validate experimental trends in wavelength-dependent enhancement of heat transfer. Quantitative discrepancies between model and experiment are rationalised by model assumptions, such as neglecting higher-order inertial terms, idealised boundary conditions, and simplified interface dynamics. These limitations lead to underprediction of interface deflection and heat flux. Nevertheless, the study underscores the value of Faraday instability as a means to boost heat transfer in reduced gravity, with implications for thermal management in space applications. 

A significant body of research is dedicated to developing language models that can detect various types of online abuse, for example, hate speech, cyberbullying. However, there is a disconnect between platform policies, which often consider the author's intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent. This paper examines the role of intent in the moderation of abusive content. Specifically, we review state-of-the-art detection models and benchmark training datasets to assess their ability to capture intent. We propose changes to the design and development of automated detection and moderation systems to improve alignment with ethical and policy conceptualizations of these abuses.