{"id":891678,"date":"2026-03-24T06:25:21","date_gmt":"2026-03-24T06:25:21","guid":{"rendered":"https:\/\/www.electronicsweekly.com\/?p=891678"},"modified":"2026-03-24T09:59:47","modified_gmt":"2026-03-24T09:59:47","slug":"cambridge-researchers-propose-hfsrtio2-thin-films-for-memristor-based-neuromorphic-devices","status":"publish","type":"post","link":"https:\/\/www.electronicsweekly.com\/news\/business\/cambridge-researchers-propose-hfsrtio2-thin-films-for-memristor-based-neuromorphic-devices-2026-03\/","title":{"rendered":"Cambridge researchers propose Hf(Sr,Ti)O2 memristors for neuromorphic devices"},"content":{"rendered":"<p>Researchers at the University of Cambridge have proposed an answer to escalating energy consumption of \u00a0AI hardware &#8211; multicomponent p-type Hf(Sr,Ti)O<sub>2<\/sub> thin films for memristor-based neuromorphic devices.<\/p><div class=\"elect-content\" style=\"margin-left: auto; margin-right: auto; text-align: center; \" id=\"elect-1911128802\"><div id=\"x02\">\r\n<script type=\"text\/javascript\"> if ($(window).width() <= 768) { googletag.display('x02'); } <\/script>\r\n<\/div>\r\n<\/div>\n<p>\u2018Neuromorphic computing offers promises to drastically reduce this footprint,\u2019 say the researchers, \u2018here, we introduce multicomponent p-type Hf(Sr,Ti)O<sub>2<\/sub> thin films for energy-efficient, resistive switching\u2013based neuromorphic devices. We demonstrate interfacial memristors with ultralow switching currents (\u2264~10<sup>\u22128<\/sup> A), exceptional cycle-to-cycle and device-to-device uniformities, and retention &gt;10<sup>5<\/sup> s.\u2019<\/p><div class=\"elect-test\" style=\"margin-left: auto; margin-right: auto; text-align: center; \" id=\"elect-2000779304\"><div id=\"DFP-EW-InRead1\">\r\n<script type=\"text\/javascript\"> if ($(window).width() > 768) { googletag.display('DFP-EW-InRead1'); } <\/script>\r\n<\/div>\r\n<BR>\r\n<\/div>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-891682 size-full\" src=\"https:\/\/static.electronicsweekly.com\/wp-content\/uploads\/2026\/03\/23082617\/IMG_1912.webp\" alt=\"Cambridge researchers propose Hf(Sr,Ti)O2 memristors for neuromorphic devices\" width=\"588\" height=\"426\" srcset=\"https:\/\/static.electronicsweekly.com\/wp-content\/uploads\/2026\/03\/23082617\/IMG_1912.webp 588w, https:\/\/static.electronicsweekly.com\/wp-content\/uploads\/2026\/03\/23082617\/IMG_1912-300x217.webp 300w\" sizes=\"auto, (max-width: 588px) 100vw, 588px\" \/><\/p>\n<p>They reveal hundreds of ultralow conductance levels with a modulation range of &gt;50 (without reaching any saturation) and reproducibly satisfy unsupervised learning rules.<\/p><div class=\"elect-post-content-2\" style=\"margin-left: auto; margin-right: auto; text-align: center; \" id=\"elect-1729413550\"><div id=\"DFP-EW-InRead1-Mobile\">\r\n<script type=\"text\/javascript\"> if ($(window).width() <= 768) { googletag.display('DFP-EW-InRead1-Mobile'); } <\/script>\r\n<\/div>\r\n<\/div>\n<p>This performance originates from incorporating a self-assembled p-n heterointerface between p-type Hf(Sr,Ti)O<sub>2<\/sub>and n-type TiO<sub>x<\/sub>N<sub>y<\/sub>, resulting in a fully depleted space-charge layer asymmetrically extended into Hf(Sr,Ti)O<sub>2<\/sub>, a large built-in potential, and extremely low saturation current density under reverse bias.<\/p>\n<p>\u201cEnergy consumption is one of the key challenges in current AI hardware,\u201d says researcher Dr. Babak Bakhit, \u201cto address that, you need devices with extremely low currents, excellent stability, outstanding uniformity across switching cycles and devices, and the ability to switch between many distinct states.\u201d<\/p>\n<p>Ultralow conductance modulation is controlled by tuning p-n heterointerface\u2019s energy-barrier height through electro-ionic charge migration. This materials-engineering strategy addresses energy consumption and variability in existing memristors, opening a pathway toward energy-efficient neuromorphic computing systems.<\/p>\n<p>&nbsp;<\/p>\n<p><a rel=\"image\" href=\"https:\/\/www.electronicsweekly.com\/news\/research-news\/\" target=\"_blank\" rel=\"noopener\">See all our Research content<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at the University of Cambridge have proposed an answer to escalating energy consumption of \u00a0AI hardware &#8211; multicomponent p-type Hf(Sr,Ti)O2 thin films for memristor-based neuromorphic devices. \u2018Neuromorphic computing offers &#8230;<\/p>\n","protected":false},"author":3,"featured_media":891682,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16039,12658],"tags":[13197],"class_list":["post-891678","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business","category-research-news","tag-university-of-cambridge"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.7 (Yoast SEO v27.2) - 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