物理學(xué)家創(chuàng)造了一種可以“忘記”記憶的裝置，就像人腦一樣

物理學(xué)家創(chuàng)造了一種可以“忘記”記憶的裝置，就像人腦一樣

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.

大腦是終極的計(jì)算機(jī)器，所以研究人員熱衷于模仿它也就不足為奇了。現(xiàn)在，一項(xiàng)新的研究朝著這個(gè)方向邁出了有趣的一步——一種能夠“忘記”記憶的設(shè)備，就像我們的大腦一樣。

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.

它被稱為二階記憶電阻器(內(nèi)存和電阻的混合)。這個(gè)聰明的設(shè)計(jì)模仿了人腦突觸記憶信息的方式，如果長時(shí)間不被訪問，它會逐漸失去這些信息。

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.

雖然憶阻器目前還沒有多少實(shí)際用途，但它最終可以幫助科學(xué)家開發(fā)出一種新的神經(jīng)計(jì)算機(jī)——人工智能系統(tǒng)的基礎(chǔ)——它能完成一些與大腦相同的功能。

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.

在所謂的模擬神經(jīng)計(jì)算機(jī)中，芯片上的電子元件(如記憶電阻器)可以起到單個(gè)神經(jīng)元和突觸的作用。這樣既可以減少計(jì)算機(jī)的能量需求，又可以加快計(jì)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.

現(xiàn)在，模擬神經(jīng)計(jì)算機(jī)只是假設(shè)的，因?yàn)槲覀冃枰宄娮釉O(shè)備是如何模擬突觸可塑性的——大腦中活躍的突觸隨著時(shí)間的推移而增強(qiáng)，而不活躍的突觸則會變?nèi)?。科學(xué)家們認(rèn)為，這就是為什么我們可以保留一些記憶，而其他的則逐漸消失的原因。

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.

以前制造記憶電阻器的嘗試使用納米導(dǎo)電橋，然后隨著時(shí)間的推移而衰減，就像記憶在我們的大腦中衰減一樣。

"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.

俄羅斯莫斯科物理技術(shù)研究所(MIPT)的物理學(xué)家阿納斯塔西婭·喬普里克說：“這種(一階記憶電阻器)解決方案的問題在于，隨著時(shí)間的推移，這種裝置往往會改變其行為，并在長時(shí)間運(yùn)行后發(fā)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."

“我們用來實(shí)現(xiàn)突觸可塑性的機(jī)制更加強(qiáng)大。實(shí)際上，在切換了1000億次系統(tǒng)狀態(tài)后，它仍然正常工作，所以我的同事停止了耐力測試。”

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.

在這種情況下，研究小組使用一種叫做氧化鉿的鐵電材料來代替納米電橋，這種鐵電材料的極化作用隨著外部電場的變化而變化。這意味著可以通過電脈沖設(shè)置低電阻和高電阻狀態(tài)。

突觸(左)與記憶電阻器(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.

氧化鉿之所以是制造這種材料的理想材料，并將其置于其他鐵電材料之前，是因?yàn)樗呀?jīng)被英特爾(Intel)等公司用于制造微芯片。這應(yīng)該意味著，當(dāng)模擬神經(jīng)計(jì)算機(jī)出現(xiàn)時(shí)，引入記憶電阻器會更容易、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."

“我們面臨的主要挑戰(zhàn)是找出合適的鐵電層厚度，”Chouprik說。實(shí)驗(yàn)證明理想厚度是的四納米。把它再薄一納米，鐵電性能就消失了，而更厚的薄膜太寬了，電子就不能通過這個(gè)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.

實(shí)際的“健忘”是通過一種缺陷來實(shí)現(xiàn)的，這種缺陷使得基于鉿的微處理器難以開發(fā)——硅和氧化鉿界面的缺陷。這些相同的缺陷使得憶阻電導(dǎo)率隨著時(shí)間的推移而降低。

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.

這是一個(gè)有希望的開始，但還有很長的路要走：例如，這些存儲單元仍然需要更加可靠。該小組還想研究他們的新設(shè)備如何被集成到柔性電子設(shè)備中。

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

“我們將研究改變記憶電阻器電阻的各種機(jī)制之間的相互作用。”來自MIPT的物理學(xué)家VitaliMikheev說。

"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."

“事實(shí)證明，鐵電效應(yīng)可能不是唯一的影響因素。為了進(jìn)一步改進(jìn)這些設(shè)備，我們需要區(qū)分這些機(jī)制，并學(xué)會將它們結(jié)合起來。”

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

該研究發(fā)表在《ACS應(yīng)用材料與界面雜志》上。