Quantum computers are NOT a replacement for the classical computers, holy hell

Very good content. I just find the desync between your video and sound a bit annoying.

I love these dual language channels, I don't know Russian and would have never have been able to understand your phenomenal video otherwise. Thank you, truly.

So glad UKposts recommended this video. Nicely done, great balance of information and presentation without coming off overly optimistic or pessimistic.

You mentioned photonics use in communications but didn't mention photonics switching. They've been trying to make purely photonic chips for decades, and it's always just around the corner much like fusion. It's that research that led to photonics being integrated into silicon chips. It seems like that research was very close to yielding results but focus and funding got taken over by quantum computing. From all I've read purely photonic chips would run 100-1000 times faster than silicon at far lower power and heat. Hopefully as silicon reaches it's limitations there will be renewed funding and research for it.

A very interesting compilation, the UKposts algorithm recommended this hidden gem.

also worth considering in the mean time, their are computers that can run on trits (-1, 0, 1) which requires some fundamental changes, but could theoretically be more effective. This could even be extended further though it gets less practical the more you add.

Silicon photonics is what I have the most hope in for in the next couple of decades. Imo, transitioning from electricity to light is just the most logical step forward. It will set moores law back by quite a bit, but the insane clock rate of the processors will make up for it. Most modern keyboards already use light to transmit signals.

Silicon carbide is used in power transitors, handles high temperatures and very high frequencies, well above silicon. It already has a supply chain, and looks like a candidate.

oh boy, quantum computers are even further out than tfets, optical compute, or memristors and you still need a von-neuman silicon computer to work with it. thanks so much for the great video, fantastic questions and background material. much appreciated

Well-Done, summary: SILICON: Above vs below a 5nm gate width silicon is discrete on eor off vs statisticial on&off (aka tunnel field effect) GERMANIUM: Germanium Has much better lab performance but silicon is extraordinarily more pragmatic/practical (availability/cost, heat dissipation/tolerance, oxidation, freq band, etc). However germanium might be modified to improve its characteristics (ex: Molybdenite in development, lab germanane, etc). CARBON nano-tubes (in development): Graphene is a one-atom-thick layer of carbon atoms arranged in a hexagonal lattice. A carbon nano-tube is a tube of graphene GALLIUM NITRIDE: has some better performance than silicon & can be manufactured with silicon based equipment/industry Better CMOS: design based on statisticial on&off (aka tunnel field effect) instead of discrete on eor off to lower power/heat. Only work for graphene & at super low temps. MEMRISTOR: A memristor is an electrical component that limits or regulates the flow of electrical current in a circuit and remembers the amount of charge that has previously flowed through it. Memristors are important because they are non-volatile, meaning that they retain memory without power. OPTICAL COMPUTING: QUANTUM COMPUTING: In theory quantum computing can find least-worst solutions to problems no-matter the number of potential candidates (ex: NP problems like the traveling salement, decryption/password breaking, etc)

Quantum computing can't replace existing systems, but it can be an add on for some special types of problems. What was really missed here, in my opinion, is (1) true 3D chips, like a solid 1" cube, and (2) wetware which uses living cells, DNA, RNA, and proteins.

I just saw this is a dub channel of another. I didn’t know I was looking for content like this and I love it. I’ve only seen dub channels go to other languages from English so it’s really cool to see that it actually does work!

The next frontier will likely be 3D chips. We already do build them with a few layers, especially memory chips which are dozens of cell layers thick now. We will need to do the same with processors, and also switch to more thermally efficient materials to help avoid the increased heat per sqr unit as a processor gets thicker (building heat exchange tubes into the design itself can also drastically help combat this). A 3D design also opens up pathways for much more optimized computation methods that a chiplet design does not really lend itself well to as it scales. Chiplets primarily communicate to eachother through side channels, whereas a 3D chip would simply be one single complex chip of interconnected logic in all directions. The potential performance gains as we add layers is insane to think about, when you consider how thin a given layer of a processor is. You could have hundreds of thousands of layers eventually. A single processor rivaling billion dollar super computer warehouses of today.

Really good run down of the limits of silicon. Great research and presentation!

This video is more informativ than I thought. I love the web for channels like this.

4:56 Not quite true. I have some Germanium transistors here which were used at 10.7 MHz, and some other low power ones which were used at over 100 MHz. They were expensive, but very capable at low currents. Bipolar Silicon transistors have been made which would amplify at frequencies close to 20GHz (I made some of them, back in the 1980's.) For higher frequencies, JFET, then MOSFET, IGFET and other types are needed. But yes; Germanium transistors are definitely very limited at higher frequencies, in comparison to Silicon devices. Leakage currents are very problematic, and they lead to high noise levels. It would still be interesting to know how a Germanium MOSFET would perform at somewhat higher frequencies, however.

Thanks. I enjoyed your fascinating journey into the chip realm, exploring the potential future.

Great video I found today I don't know if it's just me but the video and audio was out and not synced

We still got some ways to go with classical architecture, you can also gain some performance by improving how quickly we can access memory and how much of that fast memory we got, there are also architectural adjustments with concepts like RISC, bigLITTLE, vcash, new schedulers in OSs etc. so both on hardware and software level. With many new technologies being developed like Quantum, DNA-computing and Optical-computing, I don't think these will replace classic computer but with interconnects between them then they can work in tandem in the tasks they are best at.

What I like about quantum computing is it could eventually be used to work the problems we face much faster, like disease cures, and silicon limitations.

Thank you. That was very informative with a thought processing progression that was spot on without someone knocking on your door. I've been imagining crystal tech ever since I learned about piezoelectric when I was 5. Magnetic cooling, heating and generators are my insanity. It's all about switches and ball bearings. Space and Storage. I still don't know why they just didn't cut the 0 into an 8??? Cheers

Very informative and explained in very interesting way. Thank you.

Great information and you are very carismatic you can give tones of information without feeling overwelming.

Nice job researching and pulling all these alternatives together.

hey i realy liked the video nicely balanced and no stale moments, i got the random recomendation video in auto play and didn't regret a BIT XD

"that is, if it continues to exist for us, in this line of events" that hit hard

Thanks sir. I never knew this information... Amazing developments..

The biggest problem with Quantum computer is the lack of software. Writing code for them is so complicated it practically eliminates 99% if not more people who currently make their living by writing code. That means less developers and thus less development. Unless coding will become heavily AI assisted.

Years ago, I did a calculation to determine when the speed and power benefits of process scaling would no longer offset the process variation. I got 5nm for that number as well.

I don't see quantum processors replacing processors but if they become cheap enough I can definitely see them becoming a new component of the computer. If they do replace anything it will probably be graphics cards. All in all, we probably won't see quantum computers hit mass market until probably decades. What is more likely to happen than most theories is the integration of supercomputers, powered by more rare metals, that use the internet to give you your computer as a service rather than owning a computer. The system we have will be a lot more stupid and only decode the sent information.

Dear Ivan. At 9:05, there's one frame where the cmos construction steps are shrinking, and the diagram there is not translated from Russian. Please enjoy it!

Very good video, but I have two questions: -I wonder if it took around a half a century to reach this performance on silicon/CMOS doesn't it mean that it would take around the same time for others technologies to catch up or do improvements already discovered accelerate the rest? -After the huge rise of chip price in the last years can we expect before others technologies are catching up, let's say in the next decades that chip prices will decrease a lot because of the silicon limit that meanwhile we will see a comeback of dual CPU and SLI/crossfire for PC to keep improving performances?

only ~800 subs oO such high production, nice video man cant wait to see more!

When it comes to silicon being a pillar of computing, that will never really go away. Why ? Mass computing. Basically, when wanting to do computing en masse, given that power usage is not a problem with enough silicon-based photovoltaics in mind, computing can still use mostly silicon-based computing for when computing something that requires so much computing power that using the more scarce resource based computing options become prohibitively expensive. It's like creating a very large computer that can be like a workhorse, while using the more scarce resource based computing option for small applications. The workhorse can be used as an off-site computing 'monster' to offload work on. This may seem rather inefficient, but given that electricity can easily be farmed with solar voltaics, the power requirements are a lesser drawback, while the mass computing becomes cheaper over time, and can yet benefit from increased performance. Once people go into space and build space stations, remote computing this way can be much cheaper than supplying the more scarce resource based computing options for all/everyone. The problem is not one about creating a few million small computing options that require scarce resources (and thus becoming expensive), but once the newer options need to be created by the billions, yearly. The amount of mining required to get enough of the scarcer resource would be bad for the environment in such a fashion that humanity would 'compute' itself to death. Since, computing to death does not compute, en masse computing with the cheaper silicon option makes more sense. Also notice that en masse computing is much more efficient. When a resource isn't used by someone, someone else may be allocated more computing resource aiding his or her task to complete more rapidly. When it comes to remote computing for things like gaming, this may also work well. Silicon will always stay the best and easiest resource for computing, since it's most abundant. Also, the new computing options will have their own size limitations, albeit somewhat smaller. This would be a fixed factor. 4:28 Yes, the cost of fabrication of non silicon processors is much higher due to the scarcity of the required materials. This will never change. Also, due to the limitation of availability the newer processors would be fabricated in much smaller quantity, while being much more expensive. Due to rising cost of scarce materials the cost over time would rise, not become lower. For regular silicon-based processors, these would become cheaper over time. Combined with photovoltaics the increased power usage would not be the limiting factor. 13:56 Photonics are best used for transfer of data, not computing. Extreme short relays, let's say between chip layers and external to chip pathways would be an option, since these can transfer data really fast, and yet use very little power, which results in less heat release. Photons travel light so to speak while electron travel is heavy. This means less heat release. There's one problem though, the creation of very small light emitters/receivers and very small fiber optics or such is much harder than a regular circuit. It's at this point in time at least not very useful. A similar thing is with the wattage per computing. If the wattage becomes low enough, further reduction of such becomes less useful, and with that only extreme high end computing purposes would be served by further reduction. Let's examine an example, where you can have a fully capable processor using a 10 cm by 10 cm solar panel for continuous usage. What would the difference make if it's then operable by an 8 x 8 cm solar panel instead, by increasing the cost tenfold ? It would mean less power used, yes, by as much as 33%, but then the tenfold cost ? So, at some point in time the resource cost will define the computing solution, rather than its capability, once again referring to the scarcity of materials. 14:46 Photonics will not remove the limitations of distance. Any signal will still be limited to the speed of light, whether electronic or photonic based. Latency stays the same no matter which of these mediums are used. Basically, the best way to make processors smaller and faster, currently, is to provide on-chip memory, with comparatively slow memory being used for the externals. On chip memory would increase processing by removing much of the latency of memory instructions, like cache does, but then used on a larger scale, with only one cache and larger memory area. Then you'd have 16 GB base memory on the chip, with like 4-8 megs in cache. When a large-scale external reference would occur (like writing to SSD) the memory controller would copy the on-chip memory to the external one, subsequently transferring it to SSD, while optimizing the transfer speed, between on-chip and external memory. On chip the memory could have the same latency as regular cache, but transfer between the on-chip memory and external (DDR) memory would still be many times greater than the transfer speed between regular memory and SSD. Also, when using stuff like virtual memory on an SSD, this could then be replaced by the external (DDR) memory, increasing computational output per unit by that much as well. Knowing that most of the CPU's and GPU's time is wasted on waiting for memory return, you know that this transfer of operations from external memory to on-chip memory can make a big difference, even when the computational output of the processor is yet smaller. Solving each bottleneck in turn is also a way to speed up computing, in some cases making a bigger difference than making it smaller.

Great video. Thanks for posting.

Look at it this way. Human DNA is 2nm. To achieve human DNA size would be not only astounding but also open the potential for micro robots. At some point all manufacturers will have no choice but to develop more cores to handle more tasks. In addition to this, it might be possible to develop cybernetics and simulate human nerves.

First time I've ever seen a channel translated like this, cool

According to some scientists, quantic computer may never become real fully capable computers and instead became at best some kind of accelerator or limited to very specific tasks.

Overheating becomes less of an issue with the reduction of transistor size. The only reason modern transistors are emanating more heat than older models, is because manufacturers make use of that reduction in heat output, by increasing cores, core frequency, and core complexity (more transistors per core).

UKposts recommendations at its finest. Keep up.

good content. i really enjoyed it up to the end

If I were to guess which one would become the first step in a post-silicon CMOS world, it would probably be a room temperature tunnel field effect transistor.

This thinking is what we humans do 🤔 and how we achieve it! Just by solving problems one by one. Then we wait for another or more problems and we solve them again, and again. That's the beauty in science and what our world represents.

Thank you, I was lacking any news about memristors for about 8 years, so I stopped looking for updates. It is great to hear there is finally some progress

Even if Moore's law may not apply to Si applications, they are still very useful in solving many of our computational applications at 16 and even 32 nm technologies.

16:58 Classic mistake, the qubits are not in every state at once, each can only have one state at any given time. They fluctuate really fast, though. The boundaries between reading 1 and 0's digitally limit these state's readings, while the qubit itself is factually an analog computing bit, with the state varying between 0 and x, where x means the highest possible potential contained in said qubit. 18:07 Well, I'd like to see quantum graphics cards, with a nice new Unreal Engine 6 or so, personally. Nanites + quantum => huge open worlds with extreme distant view capability.

Superb man👍💐👌♥️ #Mycomputer

i think just coming up with better architechture and firmware is the easier route for now than finding a replacement for silicon

0:20 Credit for obtaining the first pure silicon goes to Swedish chemist Jöns Jacob Berzelius. He succeeded in this in the year 1823. 4:20 Germanium semiconductors have a maximum operating temperature of up to 70°C, which is much lower than silicon semiconductors.

Ayyy I am your 1000th subscriber! Great video

There's good news coming out of georgia tech this week about silicon epigraphene transistors, compatible with current manufacturing methods 👍

Goood video man, best wishes from Switzerland

Silicon is also relatively easy to work with and process.

finally yt algorithm doing his job well

Re: "silicon being near the end of its life cycle," this is just sensationalistic journalism (or an uneducated remark). It's not going anywhere in our lifetimes due to the deeply entrenced and highly refined manufacturing process. More advanced materials will have a much higher and impractical cost of scale. The most certain outcome is slowly more advanced materials will be used in conjunction with silicon, but it isn't anywhere near ending its life cycle, not for a hundred years or more. We can even do photonic and quantum compute on silicon.

Good new channel subbed. Well done.

I think the "next thing" will be photonic cpu or/and ai neuromorphic chiplet, which both are extryemly fast. The issue is still a silicon as a substrate. I heard that specific "glass" can be used even more better than silicon

GE is used in military industry that's why. + Gallium arsenide is used in military because chips made from that materials has lowest fail rate and extended temperature limits.

very good video, this truly is a hidden gem

Another direction we could take to fundamentally change how computers work in order to increase efficiency is reversible computing. The laws of thermodynamics gives us a minimal possible energy cost per bit of computation, which we are slowly approaching. However, there is a workaround, since the actual cost is in erasing information. So long as the computation does not erase information, there is no theoretical lower limit in energy use. With technology based on this, it might be possible to build 3d-chips having to worry about the heat problem at all. That said, it's at least as fundamentally different from how computers function today as quantum computers are, so it will be very difficult to transition and we will still need components that actually erase information in many cases.

Thank you! We support you.

Oh I think you've missed quite a bit; Photonic computers are not just transmitting data with light, they are CPU's, and very powerful ones at that. Memristors do not just simulate neurons closely they are physically closer to logic gates then transistors

Can't wait to get my own silicone chip manufacturing system for cheap in a few decades B)

Check into computing with Ternary. It is mathematically more efficient than binary and requires just one extra voltage level. This could lead to more efficient and faster CPUs than we have today and some are already being built. All good wishes.

Bro amazing content!

I was watching how atom's are being controlled and manipulated on 2d materials. That would suggest we have a ways to go, regarding Moore's Laws future.

as an old viewer of yours, i'd like to wish u luck with ur new channel :D

great video, keep it up!

Photonics can also do prossessing via constructive/distructive wave interference

Awesome video, thanks.from India.

How does one remember which position all those switches are in ?!? Either On or Off. Well that is what computer glitches are, the chip is forgetting where, what, who and when the switches are doing at any given time. Software errors can multiply these error glitches.😮😊

I wholeheartedly disagree with the concluding section of this video. It insinuates that quantum-cloud-compute backend reliant hardware, which are about as functional as a storage device with some added connectivity, but mostly hollow toys which will serve as GUIs is the future of computers. I reject such a plane of existence, even if there is simplicity, or even necessity, in it; such a paradigm shift would basically mark the end of personal computers and ownership of anything related to computers. Quantum computers may as well exist in their own space and continue to get more sophisticated; they will still require close-to-0-Kelvin temperatures to even function, so an average person cannot carry one such device, let alone drive one in a home setting in today's society. I cannot visualise a future in which these devices become so compact, efficient, easy-to-manage and affordable as to be the de facto standard for computers because somehow, for some godforsaken reason, they are the only way going forward. How dreadful.

amazing video. made me subscribe. ill watch more

Thks again & request you update your most excellent video once a ~quarter.

What version of knock knock on heavens door it is bro? Thnks

Amazing content 🥰

Very nice reporting. I subscribed immediately. Thank you!

Very GOOD !!!!

Silicon still has a way to go before its phased out. The key is better packaging (things like 3D stacking).

Interesting, keep an eye out for Biphenylene I believe this will have a wider application field than graphene.

Thanks for the info

I worked on photonic semiconductors at Berkeley thats the next wave for chips for sure if yall are interested look it up

Dam man you need to give credit to Eugene Khutoryanski for using his graphics

thanks for the vid. what's the outro song thanks

Great video bro

You forgot multi valued transistors. Non binary transistors which can switch between more than 2 values. A 4 state transistor can emulate 2 binary transistor, without miniaturization. So it can preserve Moore's law, without requiring smaller atoms, or getting into tunneling troubles.

I really think that graphene is the right way to move forward, we don't know how or when, but ultimately they will be the solution

actually a silicon graphene polymer has already shown upto 60% improvements in to the polarities & movement of electrons so maybe silicon will move to that

This is what I like best about UKposts, you got me thinking... 1) The way we are doing things now is coming to an end. 1a) we may get smaller features, but we are close to the limit 1b) due to size, we may find different circuits that get better results, but again an incremental change 1c) chiplets, and other die layouts, can give up more performance, but nothing major. 2) We need to think about pipeline, Risc/Cisc/Vliw, process. 2a) my personal opinion is we need to rethink compilers and CPU so they make optimal code, not optimizing compilers for a CPU 3) The software process is broken 3a) with ~8m lines of code for Linux and ~50m for Windows 10, something is wrong 4) I am a product of 16-bit words and 64K of memory. 4a) My OS was 4k 4b) I could run a word processor or a spreadsheet 4c) my machine was 10 MHz 5) when we buy a new machine, we generally get all new things 5a) New OS do NOT need to be backward compatible I really think we need to look at how we move the software to a CPU and fix that issue.

Very good and interesting content. Thank you

Hi there, it seems that the audio and video aren't synched.

Umm, the death of silicon as a base for ICs is GREATLY overstated. There is this thought about some END coming that just fails to think clearly about what will happen. So let's evaluate some timelines. Sure, silicon is going to hit a limit, but so would any other material. If you want an IC based on on/off transistors, there is only so small you can make the transistors, but once you hit that theoretical size, is that the death of silicon. The answer is no. YOu can measure CPU performance in multiple ways. One is raw performance. Raw performance is based on work done in a certain amount of time, but to calculate this you use multiple types of applications to calculate this performance. If I want a render machine, do I need a CPU that's great at gaming? The answer is no. I need something that's great at stream processing, and that could be a combination of CPUs and GPUs and accelerators, or a computer that integrates all three in a single circuit to run any type of stream processing programs. Maybe with the accelerators I can program it first before running the job, so the first part of the render application checks for an FPGA and if it exists it programs the accelerators before running the render tasks. Next, at the beginning, which is as far as I got because these kind of videos are silly, the talk is on chiplets and IPC (Instructions per clock cycle). What was skipped is making a process node much more efficient at running higher speeds. There's no law that says a CPU can't be clocked over 6GHz. In fact I think over the next 5 years TSMC and Intel will both have process nodes that run 6 - 6.5GHz and it won't be very high power consumption. Also what isn't mentioned is compute power in 3 dimensional space. We're getting to the point to where ICs can have multiple layers, and this is for different reasons, one being the shrinking of transistor size and every shrink usually comes with power reduction. I think it's safe to say that CPU design is going to involve 3D layouts and this will involve layering in such a way to where one layer isn't producing so much heat and the other layer can, so planning out the layout of circuits will become more complex. But here is the REAL meat and potatoes of this. Home users already have access to so much compute power it's crazy. A home user can now do with large servers had to do 10 years ago, and in another 5 years the amount of power that can exist in a laptop is competing with servers from a decade ago. The move to 2nm or Intel 20A which are the same thing is SO monumental that for home users there's no need to get past that. The issue won't be not having enough compute power, but having too much with rare exception. One area of growth is PC gaming. Graphics compute still has to improve to get high quality 4K gaming. But, we're there NOW. The GPUs that AMD and Nvidia are releasing right now are good enough for high quality 4K gaming, and this is using a 5nm node or modified 5nm node (TSMC N4). When semi-conductor companies get to 2nm the issue won't be not having enough transistors anymore. It will be hard to keep adding more compute units (graphics processors) and in fact for Nvidia GPUs they're using multiple types of compute in a single GPU, and that's basically the raw compute for making the image on the screen, a lighting and shading technique used for games called ray tracing for there are RT cores, and then the cores for AI, tensor cores. 2nm is SO small with SO MUCH transistor density that home computing just never needs to move to anything else, but there will be other nodes that come out with better transistor density, so like 18, 16A, etc.... and this can probably go all the way down to 10A. By this time we're about 2 decades from now. The REAL issue isn't going to be transistor density. It WILL be about making machines smarter, to where they can do more with the same number of transistors. IPC isn't a gimic. It's how you get more performance in a fixed time period from a CPU over an older CPU at the same clock speed. So, there are many tools for improving compute power, and they are NOT gimmicks to hide some flaw. It's PART of better engineering for computing. One is more processor cores. For home computing though you hit a limit because most programs home users use don't have so many processing threads and adding ever more cores is a total waste. For servers on the other hand, there are many types of server compute loads that can benefit for a LOT more cores. Another is clock speed. But this has to be solved while at the same time keeping power consumption down. THIS is a real issue and it's hard to solve, but it IS getting solved slowly, but every process node is different so you have to solve that problem for every process node. Another is instructions per clock (IPC). This also improves over time and one of the ways to solve this is simply to have more cache so the cores have more data and instructions next to the cores instead of having to access system memory to get data/instructions. But there are other ways to improve IPC too. Another is making better processors that can accelerate workloads. AMD, Intel and Nvidia are all working on this, and even Apple has done excellent work with acceleration and that's part of what makes the Apple M1 and M2 processors so fast and NOT the fact that it's based on ARM instead of X86-64. Another is being able to stack transistors which shrinks the area needed for the same circuit. Another IS chiplets. This helps with heat issues. The point is, yes silicon will hit a limit for transistor density for a 2 dimensional area, but that's a single problem, not THE problem. Solving the other problems I listed is more important. And lastly we can talk about how much silicon is needed. Well, as process nodes shrink transistor size over time, a single wafer can make a lot more ICs, so we are always improving the number of circuits that some from a fixed amount of silicon. It's not an issue at least not for another 50 years or so.

The Japanese have made it possible, and in mass. Smart move abandoning silicon 20 years ago to focus on developing GaN. Hands down!

Good video but the video and sound is not synced and it's annoying but other than that it's 10/10

Very good ... subscribed !

Quantum computer will *NEVER* happen. Forget that *FANTASY* !

Derp, you completely ignored spintronics as a possible future path. Which is funny as it easily has the most diverse amount of research going in with it.

amazing

Legendary content 😮