A high accuracy and robust machine learning network for pattern recognition based on binary RRAM devices

Utilizing the binary RRAM devices, a hardware implemented network based on the modified k-nearest neighbor (KNN) algorithm is proposed for pattern recognition. Regarding to the recognition of the handwritten digits, the hardware network shows brilliant performance with simple training scheme, high tolerance of input noise (up to 40%) and variation (up to 60%), and high recognition accuracy rate (more than 90%). The simulations show that the stable binary rather than multilevel resistance characteristics of RRAM enable the better coupling of device and algorithm for the proposed learning network.NSFC [61334007, 61421005]; Beijing Municipal Science and Technology Plan ProjectsCPCI-S(ISTP

Design and application of resistive switching devices for novel computing/memory architectures

Oxide-based resistive random access memory(RRAM) has been widely studied as the promising candidate for the applications of next generations of data storage and computing technologies. In this paper, we will discuss the physical mechanism and optimization design of oxide-based RRAM devices, the novel RRAM-based computing/memory unifying architectures and the applications for data storage and computing technologies.Oxide-based resistive random access memory(RRAM) has been widely studied as the promising candidate for the applications of next generations of data storage and computing technologies. In this paper, we will discuss the physical mechanism and optimization design of oxide-based RRAM devices, the novel RRAM-based computing/memory unifying architectures and the applications for data storage and computing technologies.IEEE Beijing Section

Mass testing and characterization of 20-inch PMTs for JUNO

AbstractMain goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3% at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).</jats:p

Sub-percent precision measurement of neutrino oscillation parameters with JUNO*

JUNO is a multi-purpose neutrino observatory under construction in the south of China. This publication presents new sensitivity estimates for the measurement of the $\Delta m^2_{31}$, $\Delta m^2_{21}$, $\sin^2 \theta_{12}$, and $\sin^2 \theta_{13}$ oscillation parameters using reactor antineutrinos, which is one of the primary physics goals of the experiment. The sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site, the nuclear reactors in the surrounding area and beyond, the detector response uncertainties, and the reactor antineutrino spectral shape constraints expected from the TAO satellite detector. It is found that the $\Delta m^2_{21}$ and $\sin^2 \theta_{12}$ oscillation parameters will be determined to 0.5% precision or better in six years of data collection. In the same period, the $\Delta m^2_{31}$ parameter will be determined to about $0.2$% precision for each mass ordering hypothesis. The new precision represents approximately an order of magnitude improvement over existing constraints for these three parameters