Energy-Efficient Memory Materials
In the current era of Big Data, with over 150 zettabytes of data being created and replicated globally, the energy consumption of information and communication technologies (ICT) is rising exponentially. A significant portion of this energy is wasted as heat due to the Joule effect (i.e., from electric currents required to operate memory devices). Additionally, traditional computers have separate memory and data processing units that must continuously communicate, leading to substantial time and energy expenditure.
Given the limitations of current computing devices, advancing ICT has become increasingly challenging. A paradigm shift in computing is essential. Presently, several strategies aim to develop memory devices that emulate the human brain, where data storage and processing occur in the same unit (in-memory neuromorphic computing). Various materials are being explored for this purpose, including memristive, spintronic, ferroelectric, multiferroic, magneto-ionic, 2D or phase-change materials. In addition to new materials, advanced computing concepts are also being developed, such as deep, spiking, recurrent, or Hopfield neural networks. Other approaches include reservoir, photonic, thermodynamic,or analog computing, amongst others.
This Special Topic brings together scholars from diverse scientific disciplines—physics, chemistry, materials science, engineering—to explore all aspects related to advanced materials for energy-efficient memories, from fundamentals to applications.
Guest Edited by Karin Everschor-Sitte and Daniele Ielmini, with Jordi Sort (Associate Editor, APL Materials) and Monica Lira-Cantu (Editor-in-Chief, APL Energy).
