Researchers with Stanford University have singled out a compound of palladium that seems to serve all the functions required from a memristor-based design. It's an approach to working memory that promises to improve performance while reducing energy consumption.
Well, it's good to know they are suing Palladium fro Memristors, but I doubt the availability and the widespread usage pf this compound. The amounts of palladium like gold isn't that commonly used in electronics.
Current alternative SOT-MRAM contenders (excluding optane). Most are phased out now, but I was hoping Nantero will do more breakthroughs in Nano-RAM tech, but they haven't:
Ferroeletric RAM (FeRAM)
Magnetoresistive RAM (MRAM)
Phase-change memory (PCM)
Resistive RAM (ReRAM)
Nano-RAM (NRAM)
But assuming if SOT-MRAM becomes the next mainstream MEM technology, they can try and find a better material as switchover to it, but nonetheless, the cost of palladium would not overall be a major roadblock for this technology.
But since we're talking micrograms of palladium per chip vs milligrams of gold, the amount is minimal, and unless I'm mistaken, thickness of the MnPd layer is just 10nm, so based on this, for a 100mm2 chip a single layer would have ~8μg of it, costing ~0.04 cents at current price. In future who knows the availability/cost of palladium though. 🙄
But there are other problems first need to be taken care of apart form the cost of palladium.
We all know that there are 4 fundamental 2-terminal circuit elements:
Resistor, Capacitor, Inductor and Memristor.
In circuits class, some of us were sleeping the day memristors were introduced.
A 6-minute memristor guide is shown below for those who missed this material.
But since we're talking micrograms of palladium per chip vs milligrams of gold, the amount is minimal, and unless I'm mistaken, thickness of the MnPd layer is just 10nm, so based on this, for a 100mm2 chip a single layer would have ~8μg of it, costing ~0.04 cents at current price. In future who knows the availability/cost of palladium though. 🙄
But there are other problems first need to be taken care of apart form the cost of palladium.
Sounds like the palladium isn't really a problem at all and the semiconductor industry demand would pale in comparison to the amount of palladium used in automotive catalytic converters. If trillions of SoCs are being produced with palladium memristors 20 years from now, they can mine more from asteroids.
I think you guys are correct. Let's see what the future holds for palladium. Btw, that's a pretty old video on memristors which I think I have watched before. Thanks for the brush up.
Also yes, Palladium is used in a wide range of applications, from dentistry to fertilizer manufacturing, but by far the most important application is as an active component in catalytic converters in the automotive field like you mentioned.
Most of palladium is used for exhaust treatment of gasoline vehicles. I heard they are trying to replace palladium with platinum/Pt in gasoline vehicle catalysts in near future.
This one is actually a pretty good and informative lecture/video if anyone wants to learn more about Memristors, or just brush up the basics. Worth watching:
BTW, a very informative article I just stumbled upon.
In the future, memristor appears to be a promising candidate for high-density, high-energy efficiency, ultra-fast, low-latency, low power, large-capacity non-volatile memory. Therefore, many companies (Samsung, Panasonic, HP, Micron, Sony, Yangtze Memory Technologies Co., Ltd. (YMTC), Crossbar etc.) are engaged in research and development of memristors.
These memristor arrays can build more integrated neural network structures, including artificial neural networks (ANN) convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), and spike neural networks (SNNs).
The second article which is actually referencing an old thesis, however focuses on design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs).
So to extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components.
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can ...
Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current ...