A new thermal transistor can control heat as precisely as an electrical transistor can control electricity.
By Rachel Nuwer
Category: computing – Page 205
Combining smart sensors with an older technology — analog computing — could dramatically reduce their power consumption.
Researchers at the Georgia Institute of Technology have created the world’s first functional semiconductor made from graphene, a single sheet of carbon atoms held together by the strongest bonds known. Semiconductors, which are materials that conduct electricity under specific conditions, are foundational components of electronic devices. The team’s breakthrough throws open the door to a new way of doing electronics.
Their discovery comes at a time when silicon, the material from which nearly all modern electronics are made, is reaching its limit in the face of increasingly faster computing and smaller electronic devices.
Walter de Heer, Regents’ Professor of physics at Georgia Tech, led a team of researchers based in Atlanta, Georgia, and Tianjin, China, to produce a graphene semiconductor that is compatible with conventional microelectronics processing methods—a necessity for any viable alternative to silicon.
Researchers are actively engaged in the dynamic manipulation of quantum systems and materials to realize significant energy management and conservation breakthroughs.
This endeavor has catalyzed the development of a cutting-edge platform dedicated to creating quantum thermal machines, thereby unlocking the full potential of quantum technologies in advanced energy solutions.
By October 2025, more than a billion PCs will be running a dead operating system, leaving many computers vulnerable to malware or headed for the trash. What’s Microsoft going to do about it?
A new technique by Apple researchers enables edge devices to run LLMs that are too large to load on DRAM by dynamically loading them from flash memory.
In a recent leap forward for quantum computing and optical technologies, researchers have uncovered an important aspect of photon detection. Superconducting nanowire single-photon detectors (SNSPDs), pivotal in quantum communication and advanced optical systems, have long been hindered by a phenomenon known as intrinsic dark counts (iDCs). These spurious signals, occurring without any real photon trigger, significantly impact the accuracy and reliability of these detectors.
Understanding and mitigating iDCs are crucial for enhancing the performance of SNSPDs, which are integral to a wide range of applications, from secure communication to sensitive astronomical observations.
A team headed by Prof. Lixing You and Prof. Hao Li from Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences (CAS) employed a novel differential readout method to investigate the spatial distribution of iDCs in SNSPDs with and without artificial geometric constrictions. This approach allowed for a precise characterization of the spatial origins of iDCs, revealing the significant influence of minute geometric constrictions within the detectors.
Embark on a journey into the microscopic world of nanosheets and nanowires, where cutting-edge technology and materials science converge.
A team of Japanese researchers has discovered significant properties of non-Fock states (iNFS) in quantum technology, revealing their stability through multiple linear optics and paving the way for advancements in optical quantum computing and sensing.
Quantum objects, such as electrons and photons, behave differently from other objects in ways that enable quantum technology. Therein lies the key to unlocking the mystery of quantum entanglement, in which multiple photons exist in multiple modes or frequencies.
In pursuing photonic quantum technologies, previous studies have established the usefulness of Fock states. These are multiphoton, multimode states made possible by cleverly combining a number of one-photon inputs using so-called linear optics. However, some essential and valuable quantum states require more than this photon-by-photon approach.
RENGE is a computational method that infers gene regulatory networks using time-series single-cell CRISPR data as input.