物理学家创造了一种可以“忘记”记忆的装置，就像人脑一样

物理学家创造了一种可以“忘记”记忆的装置，就像人脑一样

The brain is the ultimate computing machine, so it's no wonder researchers are keen to try and emulate it. Now, new research has taken an intriguing step in that direction - a device that's able to 'forget' memories, just like our brains do.

大脑是终极的计算机器，所以研究人员热衷于模仿它也就不足为奇了。现在，一项新的研究朝着这个方向迈出了有趣的一步——一种能够“忘记”记忆的设备，就像我们的大脑一样。

It's called a second-order memristor (a mix of "memory" and "resistor"). The clever design mimics a human brain synapse in the way it remembers information, then gradually loses that information if it's not accessed for an extended period of time.

它被称为二阶记忆电阻器(内存和电阻的混合)。这个聪明的设计模仿了人脑突触记忆信息的方式，如果长时间不被访问，它会逐渐失去这些信息。

While the memristor doesn't have much practical use just now, it could eventually help scientists develop a new kind of neurocomputer – the foundation of artificial intelligence systems – that fulfils some of the same functions a brain does.

虽然忆阻器目前还没有多少实际用途，但它最终可以帮助科学家开发出一种新的神经计算机——人工智能系统的基础——它能完成一些与大脑相同的功能。

In a so-called analogue neurocomputer, on-chip electronic components (like the memristor) could take on the role of individual neurons and synapses. That could both reduce the computer's energy requirements and speed up computations at the same time.

在所谓的模拟神经计算机中，芯片上的电子元件(如记忆电阻器)可以起到单个神经元和突触的作用。这样既可以减少计算机的能量需求，又可以加快计算速度。

Right now analogue neurocomputers are hypothetical, because we need to work out how electronics can mimic synaptic plasticity – the way that active brain synapses strengthen over time and inactive ones get weaker. It's why we can hang on to some memories while others fade away, scientists think.

现在，模拟神经计算机只是假设的，因为我们需要弄清楚电子设备是如何模拟突触可塑性的——大脑中活跃的突触随着时间的推移而增强，而不活跃的突触则会变弱。科学家们认为，这就是为什么我们可以保留一些记忆，而其他的则逐渐消失的原因。

Previous attempts to produce memristors used nanosized conductive bridges which would then decay over time, in the same way that memories might decay in our minds.

以前制造记忆电阻器的尝试使用纳米导电桥，然后随着时间的推移而衰减，就像记忆在我们的大脑中衰减一样。

"The problem with this [first-order memristor] solution is that the device tends to change its behaviour over time and breaks down after prolonged operation," says physicist Anastasia Chouprik, from the Moscow Institute of Physics and Technology (MIPT) in Russia.

俄罗斯莫斯科物理技术研究所(MIPT)的物理学家阿纳斯塔西娅·乔普里克说：“这种(一阶记忆电阻器)解决方案的问题在于，随着时间的推移，这种装置往往会改变其行为，并在长时间运行后发生故障。”

"The mechanism we used to implement synaptic plasticity is more robust. In fact, after switching the state of the system 100 billion times, it was still operating normally, so my colleagues stopped the endurance test."

“我们用来实现突触可塑性的机制更加强大。实际上，在切换了1000亿次系统状态后，它仍然正常工作，所以我的同事停止了耐力测试。”

In this case, the team used a ferroelectric material called hafnium oxide in place of nanobridges, with an electric polarisation that changes in response to an external electric field. It means low and high resistance states can be set by electric pulses.

在这种情况下，研究小组使用一种叫做氧化铪的铁电材料来代替纳米电桥，这种铁电材料的极化作用随着外部电场的变化而变化。这意味着可以通过电脉冲设置低电阻和高电阻状态。

突触(左)与记忆电阻器(右)。(Elena Khavina/MIPT Press Office)

What makes hafnium oxide ideal for this, and puts it ahead of other ferroelectric materials, is that it's already being used to build microchips by companies such as Intel. That should mean it's easier and cheaper to introduce memristors if and when the time comes for an analogue neurocomputer.

氧化铪之所以是制造这种材料的理想材料，并将其置于其他铁电材料之前，是因为它已经被英特尔(Intel)等公司用于制造微芯片。这应该意味着，当模拟神经计算机出现时，引入记忆电阻器会更容易、更便宜。

"The main challenge that we faced was figuring out the right ferroelectric layer thickness," says Chouprik. "Four nanometres proved to be ideal. Make it just one nanometre thinner, and the ferroelectric properties are gone, while a thicker film is too wide a barrier for the electrons to tunnel through."

“我们面临的主要挑战是找出合适的铁电层厚度，”Chouprik说。实验证明理想厚度是的四纳米。把它再薄一纳米，铁电性能就消失了，而更厚的薄膜太宽了，电子就不能通过这个屏障。”

The actual 'forgetfulness' is implemented via an imperfection that makes hafnium-based microprocessors difficult to develop – defects at the interface between the silicon and hafnium oxide. These same defects allow memristor conductivity to die down over time.

实际的“健忘”是通过一种缺陷来实现的，这种缺陷使得基于铪的微处理器难以开发——硅和氧化铪界面的缺陷。这些相同的缺陷使得忆阻电导率随着时间的推移而降低。

It's a promising start, but there's a long way still to go: these memory cells still need to be made more reliable, for example. The team also wants to investigate how their new device could be incorporated into flexible electronics.

这是一个有希望的开始，但还有很长的路要走：例如，这些存储单元仍然需要更加可靠。该小组还想研究他们的新设备如何被集成到柔性电子设备中。

"We are going to look into the interplay between the various mechanisms switching the resistance in our memristor," says physicist Vitalii Mikheev, from MIPT.

“我们将研究改变记忆电阻器电阻的各种机制之间的相互作用。”来自MIPT的物理学家VitaliMikheev说。

"It turns out that the ferroelectric effect may not be the only one involved. To further improve the devices, we will need to distinguish between the mechanisms and learn to combine them."

“事实证明，铁电效应可能不是唯一的影响因素。为了进一步改进这些设备，我们需要区分这些机制，并学会将它们结合起来。”

The research has been published in ACS Applied Materials & Interfaces.

该研究发表在《ACS应用材料与界面杂志》上。