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Is "Valleytronics" the Next Big Thing in Quantum Computing?

New valleytronics approach encodes data using quantum information without any inteference from magnetic fields

2 min read
Is "Valleytronics" the Next Big Thing in Quantum Computing?
Image: Lawrence Berkeley National Laboratory

Researchers at the Lawrence Berkeley National Laboratory (LBL) have developed a new pathway to achieving “valleytronics” using two-dimensional (2D) semiconductors.  The LBL researchers believe that this new approach could make valleytronics a more stable alternative to “spintronics” as a replacement for traditional electronics.

The term valleytronics is starting to filter into in the lexicon of cutting-edge electronics research. What it actually means is complicated, but it represents a movement away from exploiting the electrical charge of electrons as a means for storing information and instead using the wave quantum number of an electron in a crystalline material to encode data.

The “valley” in valleytronics comes from the shape of the graph you get when you plot the energy of electrons relative to their momentum: the resulting curve features two valleys. Electrons move through the lattice of a 2D semiconductor as a wave populating these two valleys, with each valley being characterized by a distinct momentum and quantum valley number. The trick is to manipulate these two valleys so that one is deeper than the other, which leads the electrons to populate one valley more than the other. When the electrons are in a minimum energy valley, the quantum valley number associated with it can be used to encode information.

This is analogous to how spintronics operates, but instead of using the quantum spin of an electron to encode information, valleytronics uses a quantum wave number instead.

Research started to move in this field last year when a team at the Massachusetts Institute of Technology (MIT) LBL demonstrated that the 2D material rhenium disulfide could be used in place of diamonds to achieve a valleytronic effect.

In this latest research out of Berkeley Lab, which was published in the journal Science, the team used the 2D material called tungsten diselenide in conjunction with a phenomenon known as the “optical Stark effect” to selectively control photoexcited electrons/hole pairs—excitons—in different energy valleys. The Stark effect involves the shifting and splitting of spectral lines of atoms and molecules when exposed to an external electric field

“This is the first demonstration of the important role the optical Stark effect can play in valleytronics,” said Feng Wang, a condensed matter physicist with Berkeley Lab’s Materials Sciences Division, in a press release. “Our technique, which is based on the use of circularly polarized femtosecond light pulses to selectively control the valley degree of freedom, opens up the possibility of ultrafast manipulation of valley excitons for quantum information applications.”

Spintronics too has been held out as a way to greatly increase data processing speeds by exploiting the quantum spin of electrons. However, quantum spin can be impacted magnetic fields, which leads to stability problems for spintronics.

The LBL researchers believe that since valleytronics is based on quantum waves rather than quantum spin this instability can be eliminated.

Wang believes that this valley-dependent optical Stark effect offers a convenient and ultrafast way of switching valley polarizations while eliminating any inteference.

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3 Ways 3D Chip Tech Is Upending Computing

AMD, Graphcore, and Intel show why the industry’s leading edge is going vertical

8 min read
A stack of 3 images.  One of a chip, another is a group of chips and a single grey chip.
Intel; Graphcore; AMD

A crop of high-performance processors is showing that the new direction for continuing Moore’s Law is all about up. Each generation of processor needs to perform better than the last, and, at its most basic, that means integrating more logic onto the silicon. But there are two problems: One is that our ability to shrink transistors and the logic and memory blocks they make up is slowing down. The other is that chips have reached their size limits. Photolithography tools can pattern only an area of about 850 square millimeters, which is about the size of a top-of-the-line Nvidia GPU.

For a few years now, developers of systems-on-chips have begun to break up their ever-larger designs into smaller chiplets and link them together inside the same package to effectively increase the silicon area, among other advantages. In CPUs, these links have mostly been so-called 2.5D, where the chiplets are set beside each other and connected using short, dense interconnects. Momentum for this type of integration will likely only grow now that most of the major manufacturers have agreed on a 2.5D chiplet-to-chiplet communications standard.

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