Diamonds and holograms in microchip production.

"A NIMS research team has successfully developed the world’s first n-channel diamond MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor), a breakthrough that enables the creation of diamond-based CMOS (Complementary Metal-Oxide-Semiconductor) integrated circuits for high-performance use in extreme environments. This innovation leverages diamond’s exceptional properties, such as high thermal conductivity and radiation resistance, paving the way for energy-efficient electronics capable of operating under intense heat and radiation. (Artist’s concept.) Credit: SciTechDaily.com"(ScitechDaily, Diamond Devices Break Limits: Scientists Unveil New High-Performance Nuclear-Grade Transistor)

Diamond devices can revolutionize electronics. They can act as transistors. But those diamonds can also control things like resistance in materials. The diamond can act as a phonon that can send acoustic waves into the semiconductor. That thing allows the system to control electricity in the semiconductor better. The diamond-phonons can create extremely strong pressure in an extremely small area. That thing can turn the point into superconducting at lower temperatures than normal pressure and temperature allow. 

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"(Left) Atomic force microscope image of diamond epilayer surface morphology. (Middle) Optical microscope image of the diamond MOSFET. (Right) Performance of the MOSFET measured at 300°C. The drain current increased when the gate voltage (Vg) was increased from -20 V (indicated by a black line) to 10 V (indicated by a yellow line). Credit: Satoshi Koizumi, Meiyong Liao National Institute for Materials Science" (ScitechDaily, Diamond Devices Break Limits: Scientists Unveil New High-Performance Nuclear-Grade Transistor)

"Researchers at UMass Amherst developed a novel method for aligning 3D semiconductor chips using laser light and concentric metalenses, projecting holograms that reveal even atomic-scale misalignments. This breakthrough could reduce chip manufacturing costs, enable advanced 3D electronics, and lead to compact, affordable sensors." (ScitechDaily, Laser Holograms Could Revolutionize 3D Chip Manufacturing)

"Simulated and measured results of different sizes of lateral misalignment, from 150 nm to 1 micrometer (or 1,000 nm). Credit: Amir Arbabi"  (ScitechDaily, Laser Holograms Could Revolutionize 3D Chip Manufacturing)

"[Left] Semiconductor layers are stacked using concentric metalenses as alignment marks. [Right] Light shines through these marks to project a hologram. The alignment or misalignment of the lenses dictates the hologram’s appearance. Credit: Amir Arbabi"  (ScitechDaily, Laser Holograms Could Revolutionize 3D Chip Manufacturing)

Simulated and measured results of different sizes of misalignment in the gap between two layers, from 1 micrometer (or 1,000 nm) to 3 µm. Credit: Amir Arbabi  (ScitechDaily, Laser Holograms Could Revolutionize 3D Chip Manufacturing)

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That makes it possible to create mass memory where the computer can put the data. And when those memories are not needed the system can remove pressure and that removes the superconductor. This makes electronics safe. The diamond can also be used as SONAR. Which can search for non-wanted potholes and scratches from microchips' surfaces.  

The diamond can also clean the layers by sending acoustic waves to it. That kind of system can be used to create ultra-clean layers. 

Another interesting thing that can revolutionize microchip production is holograms. Holograms can test does the microchip fits its position on the microcircuit. Holograms can also test the non-wanted anomalies from the layers. The reflection of the holograms uncovers if there is some kind of error in the structure. That thing makes it possible to create more advanced and more effective microchips. 

Those holograms can also be used to illuminate the film where the system makes a route for electricity. That kind of microchip manufacturing systems are more advanced and effective than traditional systems. 

https://scitechdaily.com/diamond-devices-break-limits-scientists-unveil-new-high-performance-nuclear-grade-transistor/ 

https://scitechdaily.com/laser-holograms-could-revolutionize-3d-chip-manufacturing/

Stanford researchers created the digital twin of human brains.

"Stanford researchers used AI to create a “digital twin” of a mouse’s visual cortex that can predict brain activity in response to new visual stimuli. These digital models could revolutionize neuroscience by allowing unlimited, efficient virtual experiments and revealing how the brain organizes information." (Stanford Scientists Create “Digital Twin” of the Brain Using AI)

AI is a tool that can do almost anything. The AI can be used to make a digital twin of things like neutron stars and human brains. The digital twins are also used to find out weaknesses of things like F-35 jet fighters. In those cases, digital twins can also make holograms that the researchers can use to simulate things like wave movement behavior on the aircraft's body. The system can use two different digital models the radar and aircraft. 

The system can transport virtual radar transmitters over the virtual layers. That makes it possible for the system to calculate the ideal angles of the shell. 

The system can also calculate the ideal approaching angles to the aircraft. The hologram can demonstrate things like paintings and positions where that aircraft can be when it's not in operation. 

But digital twins of human brains are things that can make revolution for research that handles intelligence. The size of those databases is enormous. The system can also have multiple independent operating levels. That system can include simulations of the neurons. Neuron's digital twins can simulate human brains with a very high accuracy. And if someday the computers are effective enough, that system can make the hybrid, multicore digital twin of the human brain. 

The system requires 100 billion cores that simulate individual neurons in the human brain. And their ability to create virtual neurons or internal, independently operating neuron groups that can form more neuron entireties. For making digital twins the AI requires as much information as possible. Advanced X-ray systems and scanners might make it possible to create the digital twin of the entire human body. 

There is the possibility of making digital twins of planets, or some chemical environment. 

Or if we have enough computer capacity we can make digital twins of the entire universe. The requirement for computers is so high. 

This kind of thing requires that computers advance. But the accuracy determines the need for computer power. We can make inaccurate models of the gas flows into the static objects. 

The ability to make the digital twin of the chemical laboratory environment saves money. The system can simulate chemical reactions with a very high accuracy. This eliminates the need for physical stuff. 

https://scitechdaily.com/stanford-scientists-create-digital-twin-of-the-brain-using-ai/

The bias in AI is a big problem.

The AI's problem is that it doesn't think. That is the thing that we usually repeat and repeat. That ability to get data without analyzing it causes bias. The thing that makes that model possible is that the AI doesn't know what the homepage involves. 

It sees words and their connections to something, but then the system doesn't realize what those words really mean. The AI, or large language model, LLM is programmed to give answers in a certain time. That means the LLM doesn't compare and search connections to every single word in the document. That saves time, but it decreases the data's trustworthiness. 

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The study put ChatGPT through 18 different bias tests. The results?

AI falls into human decision traps – ChatGPT showed biases like overconfidence or ambiguity aversion, and conjunction fallacy (aka as the “Linda problem”), in nearly half the tests.

AI is great at math, but struggles with judgment calls – It excels at logical and probability-based problems but stumbles when decisions require subjective reasoning.

Bias isn’t going away – Although the newer GPT-4 model is more analytically accurate than its predecessor, it sometimes displayed stronger biases in judgment-based tasks.

The study found that ChatGPT tends to:

Play it safe – AI avoids risk, even when riskier choices might yield better results.

Overestimate itself – ChatGPT assumes it’s more accurate than it really is.

Seek confirmation – AI favors information that supports existing assumptions, rather than challenging them.

Avoid ambiguity – AI prefers alternatives with more certain information and less ambiguity.

(ScitechDaily, More Like Us Than We Realize: ChatGPT Gets Caught Thinking Like a Human)

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The fact is that at least part of the results that the AI gets from the net are based on the search engine's page ranks. When we think like this: we teach AI we should give feedback on its results. 

If we tell that the AI gives bad answers that helps it to adjust the results that it uses. The problem is always that for giving trustworthy feedback to the AI we should sometimes be experts. Of the thing that we asked. 

The big problem with AI and other copilots is that those things are made for customers. The product must also please the user in some way. And that can cause the bias. When somebody writes about AI, that person should compare it with other LLMs. The most advanced AI "thinks" that it's the best of all. The algorithms tell it how impressive it is. The more advanced AI means that it's better than other AI's. 

When we think about AI and its operators and owners in the world we must realize that all those companies that develop AI are independent companies. That means those AIs don't have access to the competitor's statistics. So, those AIs are not under one dome. And if they should compare themselves with other AIs like "What is the best AI in the world"? 

Those systems cannot get trusted data. Like how many percent of their answers satisfy the users. That kind of data is not collected. The AI will not ask would the user like its answers or if are they good. Another thing is that if the regular user is not the same as some doctoral user. The AI requires specific commands that involve the right terminology. Without the right terminology, the AI is helpless. 

And then AIs cannot compare those percents with other AI's statistics. This makes the AI think. That it's better than it is. The AI can use Wikipedia. When it should use specific sources like CERN homepages. The AI requires that the person who uses it knows the terminology, and then the misuse of some term in the wrong place in a query can turn the AI's text into a grab. AI will not make a difference in the words "hall" and "hall effect". That thing can cause a situation in which the AI starts to talk about echoes or some aircraft hangars when it should start to talk about resistance in electric wires. 

 https://scitechdaily.com/more-like-us-than-we-realize-chatgpt-gets-caught-thinking-like-a-human/

AI computers, nanoantennas, and holograms are tools that can revolutionize manufacturing.

That would raise the sales of the microchips. And that's why that is the goal for the microchip producers. Those new PCs require lots of new microchips. The new AI PCs can run more powerful and effective programs. That makes them powerful tools in the hands of hackers. 

The prediction is that AI-based computers will soon be standard. New and effective microchips and memory controls allow the system to run more complicated code than before. The new systems can also enhance data security. But the same systems can also find vulnerabilities in the old-time systems. The new and effective computers are tools, that can revolutionize things, like graphics, system handling, interfaces, and many other things. 

These kinds of computers are things. That will revolutionize computing and nanotechnology sooner than we even expect. The nanomachines that can transport sensors to the desired positions in the body can make a revolution in neurology. And maybe in the future things like neuro implants will inject into people's bodies. Then nanotechnology transports them into the right positions. 

"The principle of this work is demonstrated by using a laser to levitate nanoparticles and make them carry an electric charge to become an antenna that receives an external electric field containing a communication signal. Credit: Adapted from Zhenhai Fu / Zhejiang Lab, China, edited." (ScitechDaily, 10,000x Smaller: Tiny Nano-Antenna Could Revolutionize Wireless Tech)

The tiny nano-antenna can revolutionize wireless technology. It can make new, powerful multi-band transmitters possible. Those transmitter-receivers can handle thousands of frequencies. And that is a great advance for portable technology. Those tiny nano-size antennas can also operate in the nano-size robots to transmit. 

And receive information. The nano-size antennas can also operate with nanotechnical systems that can put those antennas between axons. And those new types of systems can hear signals that travel in the nervous system. The new antennas that nanotechnology can put their position can make. 

A new type of singularity with living nervous systems and robots is possible. Nanotechnology can slip those antennas into the bodies of natural animals. And maybe quite soon we can see and hear what those animals hear and see. The knowledge of the nervous signals increases all the time. That means the systems can decode nervous signals in new ways. The singularity is the ultimate version of the user interface. In those systems robots and humans are combinations. 

"The method allows the fabrication of millimeter-scale objects within seconds. Credit: © LAPD EPFL" (ScitechDaily, Holograms Take 3D Printing to the Next Level – With Amazing Precision and Speed)

But antennas are not the only things that advancing all the time. The nano-antennas can give revolutionary information. But new AI computers. And new algorithms and AI-based systems can make new types of intelligent, network-based data analysis possible. The idea in those systems is that if they cannot solve problems in a certain time, they call more systems into the neural network to solve problems. 

The highly advanced AI is the tool that can make the new computer-aided design CAD, and computer-aided manufacturing CAM tools possible. The AI can remove errors from the product automatically. And the holograms are making it more effective. The infrared hologram can be used to remove things like scratches from the plastic layers. Or maybe if the IR's temperature is high enough, that lightsaber can remove roughness even from the metal surfaces. The hologram can also used to test what some products like furniture can look like in some rooms. 

Highly advanced computing is a tool for material research. It is a tool for holographic-support 3D printing. In those systems, the developer makes the 3D CAD image for the system. The system makes holograms of the object. Then the developer can check the measures and dimensions of the object. The hologram tells does the object fits into its place. 

The system uses holograms to model things. And then the 3D printing system follows the hologram and fits the model into the hologram. The hologram's shape helps the system make a sharp 3D model by using the printer. The 3D printers can use holograms to test how the part that the system makes fits into the place. Then the system can use AI to fix the diameters and other things. 

 https://www.freethink.com/consumer-tech/ai-pcs

https://scitechdaily.com/10000x-smaller-tiny-nano-antenna-could-revolutionize-wireless-tech/

https://scitechdaily.com/holograms-take-3d-printing-to-the-next-level-with-amazing-precision-and-speed/

The computer requires hardware and software.

The new photonic microchips are extremely fast. They can make many things faster than traditional microchips. And the quantum computers require that thing. Quantum computers are tools. That can make calculations faster than any binary computer on Earth. But as we know. The traditional computer can make any calculation as quantum computers. The binary computers just make those calculations far slower. And that means that quantum computers make things faster. 

But both computer types must follow the orders of mathematics. Even the fastest calculation tool is helpless without formulas. They need to make solutions that we can accept. So, we can say that even the best and fastest computers are helpless without mathematicians. However, the computer requires programs that turn mathematical formulas into understandable forms for the computer. 

Hardware and software are both important. Without software, the computer doesn't do anything. The software has three layers. The programs, operating systems, and kernel. The last one interacts with the hardware like microchips and other tools like mice, display adapters, and sound circuit cooperation. 

Or then we must realize that the computer requires programs that it can do anything. The programs and operating systems are things. That is vital for computers. And of course, computers require input and output tools like keyboards, microphones, and screens. And then there must be an internet that computer programs like AI use to for search information. Here we see what kind of problems we have with computing. 

We develop processors but we should also develop programs that support those new processors. Things like biological processors that involve living neurons are not solutions for all problems. The microchip must transmit data to neurons and receive it. But that means the computer must program the neuron. 

We can think about things like "iron-based AI". That term means the computer chip or microchip that has the large language model, LLM in its kernel. We have the "iron-based (A)I) in our nervous system. We can say that when we learn something we program our nervous system. 

That kind of microchip is a tool that involves miniature computers. So that means that the layer where that tool called LLM exists doesn't matter. It can be an independent program or it can be a layer in the operating system. If the LLM is the layer on the operating system all commands to the computer travel through the LLM. The thing is that the AI is the computer program that is in some layer in those computers. The thing is that the network-based solutions offer laptop users the ability to use complicated AI-based systems over the internet. 

Those complicated and hard systems exist only in the remote computers far away from the users. The code is driven by supercomputers. And the home, or office users can give orders to the LLM through the internet. That means the user can have lightweight systems, and the rough force that is needed is in some cave, maybe far away from the place where that operator sits. 

Intensive interaction over the net requires extremely fast and safe communication. The system requires highly secured data highways there information is out of the hands of outsiders. The system must pack the data into the form so that the attackers cannot see or touch those data packages. This is the difference between quantum and binary networks. In binary networks, the data is just mixed in the encryption process into the form that its reorder is difficult. In quantum networks, the system denies the attacker to see the message. 

The new solutions in the computer systems emulate human brains.

"A joint university project funded by the NSF aims to create atomically tuned memristors for neuromorphic computing. The focus is on simulating brain functions for AI with high speed and efficiency. Credit: SciTechDaily.com" (ScitechDaily, Revolutionizing AI: The Tiny Tech Powering Brain-Like Computers)

There are many ways in which the computer can emulate human brains. The neuromorphic networks can begin their operations. Same time at different points. That means the neuromorphic networks must not stop before they take on another mission. And then another thing is that neuromorphic computers can use their calculation power to solve multiple problems at the same time. Or the systems can focus their energy on one single solution. 

That makes that kind of network faster than traditional computers that use only one processor that must stop before the next mission. Another way is to use the living neurons that can cooperate with new microchips. The ability to make neurons interact with microchips is one of the most effective ways to create computers that support large language models, LLM better than modern computers. The thing that neuromorphic networks must not stop before the next mission makes them suitable to control things like robots. 

"Researchers at the University of Minnesota have created a transparent conducting material that significantly boosts electron speed and efficiency in high-power electronics, potentially transforming fields like AI, computing, and quantum tech. (Artist’s concept.) Credit: SciTechDaily.com" (ScitechDaily, Transparent New Material Paves the Way for Advanced Electronics and Quantum Devices)

Temperature is the biggest problem with highly advanced microchips.

Before we can create room-temperature quantum computers we must use binary computers. And even if we would have room-temperature quantum computers, we would need binary computer to transmit data to the quantum system. Compact quantum computers require room-temperature superconductors. And researchers work hard with those things. Researchers search for new materials to replace silicon. And the promising thing is gallium nitride. 

The silicone microchips are at the end of their road. New materials like gallium-nitride metal alloys pave the road to the new processor technology. Those metal alloys cause less disturbance to the system. And that allows the system to keep a higher clock frequency than in silicon microchips. Those metal alloys also take less temperature. And that helps the system to keep the temperature low. The temperature causes problems with computers because oscillation disturbs signals.

"If you ever put your hands on a mobile phone charger, they’re warm and that’s the energy being wasted in the silicon,” Rachel Oliver, director of the Cambridge Centre for Gallium Nitride, told Freethink. “A gallium nitride charger will feel a lot cooler to the touch — it’s wasting a lot less energy.” (FreeThink, Silicon chips are no longer sustainable. Here’s what’s next.)

"Gallium and gallium compounds have been used in tech for decades — they’re in LEDs, lasers, military radars, satellites, solar cells, and more — but gallium nitride is currently a major focus of researchers looking for ways to make tech more powerful and energy efficient. " (FreeThink, Silicon chips are no longer sustainable. Here’s what’s next.)

"So why is the microchip industry built around silicon, if gallium nitride is superior in various ways and has been around for a while? The answer, as usual, is cost: gallium nitride chips are more expensive and complicated to manufacture. Getting costs down while scaling up production is going to take time, but the US government is trying to help jumpstart the nascent industry. " (FreeThink, Silicon chips are no longer sustainable. Here’s what’s next.)

The problem in superconductors is simple, how to control the electric flow? And another thing is that until researchers can create room-temperature superconductors those systems require powerful coolers. But maybe someday in the future, we can have superconducting computers on our tables. 

Superconductors are things. That can give answers to the new requirement for new microchips. The problem is that superconducting materials don't have resistance. So when the system tries to load information to the normal temperature systems. The wave that impacts electricity forms between those systems will travel backward in the superconductors. 

Another thing is superconducting microchips. The superconductor technology is quite a new thing. The superconducting materials can act like normal traditional semiconductors. But there is one big difference between those superconductors and traditional semiconductors. There is no resistance in the superconductor. That means things like resistors cannot work like they work in normal semiconductors. 

It's possible to adjust the superconducting ability of the material by using lasers. Lasers can increase the temperature at some points of the superconducting materials. When the temperature rises the material loses its superconducting ability. And that kind of system can adjust the superconducting by switching it on and off. 

https://www.freethink.com/the-material-world/gallium-semiconductors

https://scitechdaily.com/revolutionizing-ai-the-tiny-tech-powering-brain-like-computers/

https://scitechdaily.com/transparent-new-material-paves-the-way-for-advanced-electronics-and-quantum-devices/

The photonic chips are the new tools for computing.

 

Things like quantum computers require ultra-fast data handling systems to control them. The quantum computer is the most powerful calculation system in the world. The problem is that the quantum computer requires binary computers to input and output information to the system.  Researchers cannot connect things like screens and keyboards straight to the quantum computer. That's why there is needed a binary computer between the quantum state and input-output devices. 

When the controlling system notices some anomaly it must react immediately. Another thing that the system requires is that. The system that controls the quantum entanglement should not disturb the quantum entanglement and sensors. 

That downloads and uploads data in and out from superpositioned and entangled photons. Or some other particles. All electromagnetic systems cause electromagnetic fields that can affect data that travels in the qubit. 

So the answer is the photonic microchip. The photonic microchip can load data to photons and then deliver it to the quantum computers. There is one little problem with photonic computers. The system needs regular quantum computers to drive information to photonic computers. And, the new nanomaterials can make it possible to change photons to electricity and backward. 

In this model, the quantum computer has three stages. 

The regular binary computer. 

The photonic binary computer

Quantum computer. 

Image 2

The input will happen through the regular binary computer, which decodes it to the photonic binary system. And then the photonic binary system transfers data to the quantum state. When the quantum state makes its duty, the system will return the data to the regular binary computer through a photonic binary computer. 

This model means that the system is scalable and it saves energy. The binary system calls those other layers or states to work with a mission that takes too long time for the first level. If photonic computers cannot solve the problem in a certain time. The system transfers the problem to the quantum state. 

Image 3 

Those photonic processor rings look like token ring architecture. (Image 2)The processing system can involve many processors. That allows it to drive multiple databases at the same time. Or they are hybrid systems. That uses mesh-protocol-based architecture (Image 3). 

In that system, the central processor shares the missions with the other processors. The neural networks use mesh protocol. The mesh- or distributed networks have one benefit to centralized networks. If one processor has problems or damages, the data can pass that processor. 

When we think about the primary computers the photonic microchips can make the ring where they drive information. The system can involve two photonic microchip rings. It can compile the intermission after each processor drive. And if there are anomalies like different results there is something wrong. 

After a certain time. The system can transfer data to the next processor. And when the processor transfers the mission to the new processor. It can make the backup. 

Then there is the control system between those two rings that can compile the data. And that can be the new tool for systems that drive complex data structures. Things like the large language model. The LLM-type systems require. The new physical tools to handle information. New systems must support quantum calculation more effectively. 

The system must start to drive multiple databases at the same time. The photonic systems allow researchers to make new systems. That supports machine learning more effectively than traditional systems. 

https://scitechdaily.com/harnessing-light-quantum-materials-supercharge-data-transmission/

https://scitechdaily.com/integrating-photonics-with-silicon-nanoelectronics-into-chip-designs/

https://scitechdaily.com/microscopic-marvel-a-photonic-device-that-could-change-physics-and-lasers-forever/

The new nanomaterials can make it possible to change photons to electricity and backward. 

Biocomputers will be man-made aliens.

If we want to make the biocomputers. We will make the modern Frankenstein. Computers that use cloned brains for thinking are man-made aliens. The most capable and dangerous systems that we can even imagine are in the sector of computing. 

Biological computers are a group of mini-brains. That is connected to computers. Human brains use quantum calculation methodology.  The thing that makes them so powerful is their ability to begin computing at many points at the same time. 

The image series shows how the system scales the mission for a larger number of data handlers. If one data handler does not get the correct answer, the system calls more units to work on that too-difficult mission. 

This makes the brain act as the qubit. In the brain, multiple neurons form the structure and layers that act like qubit states. Which makes brains so powerful. Then we can make a model, where the brain cells or neural centers in the human brain. Are replaced using mini-brains. Each mini-brain would be one neural center in the biological computer. The system can connect many mini-brains into one entirety. 

But then we can think about the thing called mini-brains. Minibrains are lab-grown miniature brains. They are created for testing neurological medicines. However, researchers teach those mini-brains to play things like ping-pong. The mini-brains communicate with traditional computers using microchips and sensors that can exchange information between those mini-brains and computers. 

Theoretically, there is no limit to how many mini-brains are connected to the computer. The system must give nutrients to those miniature brains that communicate with computers. The biological processors, or, mini-brains will get tired. And they need lots of nutrients. The system must have life support that removes waste from the mini-brains. 

When the system gives a mission to those mini-brains it can give the same time to the multiple mini-brains or biological data-handling units. The system can have two or three layers where those mini-brains work with the problem. When neurotransmitters go low in those systems, the system can change the operating layer. That allows the biological computer can operate non-stop. The system records the solution and drives it to another mini-brain group. 

Mini-brains are tools that can operate as vital parts of computers that are more intelligent than humans. Nothing denies researchers to connect adult-size human brains into one entirety. Computer-based EEG coders and decoders can make human brains communicate with computers that operate as neural networks. This kind of system requires the ability to code and decode the EEG signals. 

But if that thing is possible to make. The biological computer would be the most powerful machine in the data-handling sector. That we can create. This kind of system involves many risks and possibilities. But if we someday create this system we are making another intelligent organism, a man-made alien. 

Images: AI