As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.
Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. Their study is published in the journal Nature Communications.
As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (memory) from data processing (processors), resulting in significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of in-memory computing.
In-memory computing enables calculations directly within memory, eliminating data movement and achieving faster, more efficient operations. ECRAM is a critical technology for implementing this concept. ECRAM devices store and process information using ionic movements, allowing for continuous analog-type data storage. However, understanding their complex structure and high-resistive oxide materials has remained challenging, significantly hindering commercialization.
To address this, the research team developed a multi-terminal structured ECRAM device using tungsten oxide and applied the parallel dipole line Hall system, enabling observation of internal electron dynamics from ultra-low temperatures (-223°C, 50K) to room temperature (300K). They observed, for the first time, that oxygen vacancies inside the ECRAM create shallow donor states (~0.1 eV), effectively forming shortcuts through which electrons move freely.
Rather than simply increasing electron quantity, the ECRAM inherently creates an environment facilitating easier electron transport. Crucially, this mechanism remained stable even at extremely low temperatures, demonstrating the robustness and durability of the ECRAM device.
Prof. Seyoung Kim from POSTECH emphasized, “This research is significant as it experimentally clarified the switching mechanism of ECRAM across various temperatures. Commercializing this technology could lead to faster AI performance and extended battery life in devices such as smartphones, tablets, and laptops.”
More information:
Hyunjeong Kwak et al, Unveiling ECRAM switching mechanisms using variable temperature Hall measurements for accelerated AI computation, Nature Communications (2025). DOI: 10.1038/s41467-025-58004-0
Pohang University of Science and Technology
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A shortcut to AI computation: In-memory computing overcomes data transfer bottlenecks (2025, April 25)
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