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Silicon retains conductivity at ultra-low charge levels

01.03.2020

Researchers from the American National Institute of Standards and Technology (NIST) have come up with a new method for measuring the mobility of charged particles in silicon, which, if not overturned, then significantly expanded the understanding of charge transfer processes in semiconductors.

The method proposed by the scientists made it possible to carry out the most sensitive measurements of the speed of movement of an electric charge in silicon, and this is an indicator of its effectiveness as a semiconductor. As a result, the new method will make it possible to more accurately assess the effect of certain dopants on the silicon conductivity and will form the basis for improving the characteristics of semiconductor devices. This is a chance to improve the performance of the chips almost for nothing only through a better understanding of the processes. Carry out tuning, so to speak.

Traditionally, the mobility of electrons and holes in silicon was measured by the Hall method. This method assumes that contacts are soldered on a sample of silicon (semiconductor) to pass an electric current. The disadvantage of this method is that defects or impurities appear at the soldering points, which introduce distortions into the measurement results.

For the purity of the experiment, scientists from NIST used a non-contact method. The silicon sample was first exposed to light of low intensity in the form of ultrashort pulses of visible light, and then the sample was irradiated with radiation pulses in the far infrared or microwave range. Weak visible light produced a photodoping effect on silicon: charged particles appeared in the silicon layer in the form of electrons and holes.

Visible light, for obvious reasons, could not penetrate into the thickness of silicon. For this purpose, the photodoped sample was irradiated with terahertz radiation (in the far infrared range), for which silicon is transparent. And the more charged particles in the sample, the more light penetrates or is absorbed by the sample. It is important to note here that for a more accurate measurement of the electron mobility in the sample, its thickness should have been quite large, up to 1 mm. This ruled out the influence of defects on the sample surface on measurements.

However, the number of electrons and holes "introduced" by visible light in the sample had to be as small as possible in order to lower the sensitivity threshold during measurements. Usually, for this, the sample was irradiated with one photon, but in the case of a thick sample, one photon knocked out insufficiently charged particles in silicon. A way out was found in irradiating the sample with two photons of visible light. After that, terahertz radiation freely passed through the sample with a minimum number of charged particles in the bulk of the material. According to scientists, the threshold of sensitivity was reduced by a factor of 10 from 100 trillion charge carriers per cm2 to 10 trillion.

As soon as the threshold of sensitivity was lowered, the surprising became clear. The mobility of electrons in silicon turned out to be able to grow even to a very rarefied state of carriers in the material, which no one suspected before. Actually, the mobility itself turned out to be 50% higher than previously thought. For a control check, a similar experiment was carried out with gallium arsenide (GaAs), also a photosensitive semiconductor. It was found that the mobility of charge carriers in this material continues to grow as their density decreases. The carrier density limit measured by the new method turned out to be about 100 times lower than previously thought.

In the far or not so far future, semiconductors will be able to operate at very low charge levels. At least the theoretical limit has been pushed far enough. These are highly sensitive solar panels, and single-photon detectors (hello to quantum computers!), ultra-efficient electronics and much more.

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Nanowire networks learn and remember like the human brain 25.04.2023

An international team of scientists led by the University of Sydney tested the ability of a nanowire network to perform complex cognitive tasks that are characteristic of a complex nervous system. The analysis showed that networks that mimic the physical structure of the brain can learn and remember data.

To test their network, the scientists used a modified analog of the n-back problem. This is a popular experiment used in neurophysiology and psychology to evaluate memory. During the test, the subject, who is presented with a series of images (visual test) or names objects (hearing test), must indicate when this or that object was encountered n-steps ago.

For example, such a task may involve demonstrating a sequence of letters, and the person should be told when the same letter was encountered 1, 2, or more steps ago. The average score for most people on this task is 7. This means that most people can recognize the same image that appeared seven steps ago.

The researchers found that the nanowire network was also able to "remember" the desired endpoint in an electrical circuit seven steps back. "We manipulated the end electrode voltages to force the paths to change instead of letting the network just do its thing," explains Alon Loeffler, co-author of the study.

A series of experiments showed that under external influence, the nanonet "learned" and its memory "strengthened": the network memorized the paths proposed by the scientists, and over time, the existing connection did not decrease. This is reminiscent of the work of a synaptic network, scientists note, some connections are strengthened, others are weakened, and as a result, stable connections are formed in the learning process.

Nanowire networks are a type of nanotechnology, usually made from tiny, highly conductive silver wires. Invisible to the naked eye, particles coated with a plastic material are scattered in disorder to form a grid. Such wires, due to their properties, mimic the physical properties of the brain: a network of neurons connected via synapses.

The use of such networks opens up many real-world applications: for example, the creation of robotic devices and sensors that need to make quick decisions in unpredictable conditions.

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