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The super τ-charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It isdesigned to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5 × 1035 cm-2·s-1 or higher. The STCF will produce a data sample about a factor of 100 larger than that of the present τ-charm factory ‒ the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-dep [Detail] ...
Download coverThe super τ-charm facility (STCF) is an electron−positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5 × 1035 cm−2·s−1 or higher. The STCF will produce a data sample about a factor of 100 larger than that of the present τ-charm factory — the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R&D and physics case studies.
In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.
In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.
Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.
The intrinsic variability of memristor switching behavior can be used as a natural source of randomness, this variability is valuable for safe applications in hardware, such as the true random number generator (TRNG). However, the speed of TRNG is still be further improved. Here, we propose a reliable Ag/SiNx/n-Si volatile memristor, which exhibits a typical threshold switching device with stable repeat ability and fast switching speed. This volatile-memristor-based TRNG is combined with nonlinear feedback shift register (NFSR) to form a new type of high-speed dual output TRNG. Interestingly, the bit generation rate reaches a high speed of 112 kb/s. In addition, this new TRNG passed all 15 National Institute of Standards and Technology (NIST) randomness tests without post-processing steps, proving its performance as a hardware security application. This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.
Two-dimensional (2D) heterostructures have shown great potential in advanced photovoltaics due to their restrained carrier recombination, prolonged exciton lifetime and improved light absorption. Herein, a 2D polarized heterostructure is constructed between Janus MoSSe and MoTe2 monolayers and is systematically investigated via first-principles calculations. Electronically, the valence band and conduction band of the MoSSe−MoTe2 (MoSeS−MoTe2) are contributed by MoTe2 and MoSSe layers, respectively, and its bandgap is 0.71 (0.03) eV. A built-in electric field pointing from MoTe2 to MoSSe layers appears at the interface of heterostructures due to the interlayer carrier redistribution. Notably, the band alignment and built-in electric field make it a direct z-scheme heterostructure, benefiting the separation of photogenerated electron-hole pairs. Besides, the electronic structure and interlayer carrier reconstruction can be readily controlled by reversing the electric polarization of the MoSSe layer. Furthermore, the light absorption of the MoSSe/MoTe2 heterostructure is also improved in comparison with the separated monolayers. Consequently, in this work, a new z-scheme polarized heterostructure with polarization-controllable optoelectronic properties is designed for highly efficient optoelectronics.
