研究人员开发出一种新的计算机内存设计，可以大大提高性能并减少互联网和通信技术的能源需求，预计在未来十年内，互联网和通信技术将消耗全球近三分之一的电力。

Researchers have developed a new design for computer memory that could both greatly improve performance and reduce the energy demands of internet and communications technologies, which are predicted to consume nearly a third of global electricity within the next ten years.

由剑桥大学领导的研究人员开发了一种处理数据的设备，其方式与人脑中的突触类似。这种装置的基础是氧化铪，一种已经在半导体工业中使用的材料，以及微小的自组装屏障，可以升高或降低以允许电子通过。

The researchers, led by the University of Cambridge, developed a device that processes data in a similar way as the synapses in the human brain. The devices are based on hafnium oxide, a material already used in the semiconductor industry, and tiny self-assembled barriers, which can be raised or lowered to allow electrons to pass.

我们这个对数据需求旺盛的世界导致了能源需求的激增，这使得减少碳排放变得更加困难。在未来几年内，人工智能、互联网使用、算法和其他数据驱动技术预计将消耗全球30%以上的电力。

Our data-hungry world has led to a ballooning of energy demands, making it ever more difficult to reduce carbon emissions. Within the next few years, artificial intelligence, internet usage, algorithms and other data-driven technologies are expected to consume more than 30% of global electricity.  

剑桥大学治金系的第一作者Markus Hellenbrand博士说，“在很大程度上，能源需求的爆炸式增长是由于当前计算机存储技术的缺陷。在传统计算中，内存在一边，处理在另一边，数据在两者之间来回切换，这既耗费精力也耗费时间。”

 “To a large extent, this explosion in energy demands is due to shortcomings of current computer memory technologies,” said first author Dr Markus Hellenbrand, from Cambridge’s Department of Materials Science and Metallurgy. “In conventional computing, there’s memory on one side and processing on the other, and data is shuffled back between the two, which takes both energy and time.”

一种潜在的解决低效计算机存储器问题的方法是一种被称为电阻开关存储器的新技术。传统的存储设备有两种状态:一或零。然而，功能性的电阻开关存储器能够实现连续的状态范围——基于这一原理的计算机存储器将具有更大的密度和速度。

One potential solution to the problem of inefficient computer memory is a new type of technology known as resistive switching memory. Conventional memory devices are capable of two states: one or zero. A functioning resistive switching memory device however, would be capable of a continuous range of states – computer memory devices based on this principle would be capable of far greater density and speed.

Hellenbrand说:“例如，一个典型的基于连续范围的u盘可以存储10到100倍的信息。”

 “A typical USB stick based on continuous range would be able to hold between ten and 100 times more information, for example,” said Hellenbrand.

Helenbrand和同事开发了一种基于氧化铪的原型装置，氧化铪是一种已经在半导体工业中使用的绝缘材料。将这种材料用于电阻开关存储器应用的问题被称为均匀性问题。在原子水平上，氧化铪没有结构，铪和氧原子随机混合，使其难以用于存储应用。

Hellenbrand and his colleagues developed a prototype device based on hafnium oxide, an insulating material that is already used in the semiconductor industry. The issue with using this material for resistive switching memory applications is known as the uniformity problem. At the atomic level, hafnium oxide has no structure, with the hafnium and oxygen atoms randomly mixed, making it challenging to use for memory applications.

然而，研究人员发现，通过将钡添加到氧化铪薄膜中，在复合材料中开始形成一些不同寻常的结构，垂直于氧化铪平面。

However, the researchers found that by adding barium to thin films of hafnium oxide, some unusual structures started to form, perpendicular to the hafnium oxide plane, in the composite material.

Hellenbrand说:“这允许材料中存在多种状态，不像传统的存储器只有两种状态。”

“This allows multiple states to exist in the material, unlike conventional memory which has only two states,” said Hellenbrand.

与其他需要昂贵的高温制造方法的复合材料不同，这些氧化铪复合材料在低温下自组装。该复合材料表现出高水平的性能和均匀性，使其在下一代存储器应用中具有很高的前景。

Unlike other composite materials, which require expensive high-temperature manufacturing methods, these hafnium oxide composites self-assemble at low temperatures. The composite material showed high levels of performance and uniformity, making them highly promising for next-generation memory applications.

剑桥大学的商业化部门剑桥企业已经为这项技术申请了专利。

A patent on the technology has been filed by Cambridge Enterprise, the University’s commercialisation arm.

Hellenbrand说:“这些材料真正令人兴奋的是，它们可以像大脑中的突触一样工作:它们可以在同一个地方存储和处理信息，就像我们的大脑一样，这使得它们在快速发展的人工智能和机器学习领域颇有应用前景。”

“What’s really exciting about these materials is they can work like a synapse in the brain: they can store and process information in the same place, like our brains can, making them highly promising for the rapidly growing AI and machine learning fields,” said Hellenbrand.

研究人员现在正与工业界合作，对这种材料进行更大规模的可行性研究，以便更清楚地了解高性能结构是如何形成的。由于氧化铪是一种已经在半导体工业中使用的材料，研究人员说，将其集成到现有的制造工艺中并不困难。

The researchers are now working with industry to carry out larger feasibility studies on the materials, in order to understand more clearly how the high-performance structures form. Since hafnium oxide is a material already used in the semiconductor industry, the researchers say it would not be difficult to integrate into existing manufacturing processes.

这项研究得到了美国国家科学基金会和工程与物理科学研究委员会(EPSRC)的部分支持，EPSRC是英国研究与创新署(UKRI)的一部分。

The research was supported in part by the U.S. National Science Foundation and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).