Suppressed Transmission of Long-Range Surface Plasmon Polariton by TE-Induced Edge Plasmon

Work on controlling the propagation of surface plasmon polaritons (SPPs) through the use of external stimuli has attracted much attention due to the potential use of SPPs in nanoplasmonic integrated circuits. We report that the excitation of edge plasmon by TE-polarized light passing across gapped-SPP waveguides (G-SPPWs) leads to the suppressed transmission of long-range SPPs (LRSPPs) propagating along G-SPPWs. The induced current density by highly confined edge plasmon is numerically investigated to characterize the extended radiation length of decoupled LRSPPs by the TE-induced edge plasmon. The suppressed transmission of LRSPPs is confirmed using the measured extinction ratio of the plasmonic signals which are generated from the modulated optical signals, when compared to the extended radiation length calculated for a wide range of the input power. It is also shown that LRSPP transmission is sensitive to the excited power of edge plasmon in the gap through the permittivity change near the gap. Such a control of SPPs through the use of light could be boosted by the hybridized edge plasmon mode and a huge field enhancement using nanogap, gratings or metasurfaces, and could provide opportunities for ultrafast nano-plasmonic signal generation that is compatible with pervasive optical communication systems.

Using machine learning to speed up new and upgrade detector studies: a calorimeter case

In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may improve the precision of the reconstruction methods being considered during detector R&D. Moreover, such reconstruction methods can be reproduced automatically while changing the main optimisation parameters of the detector like geometrical size, position, configuration, radiation length, Molière radius of the sensitive elements. This allows us to speed up the verification of the possible detector configurations and eventually the entire detector R&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector[1]. The reconstruction methods such as spatial reconstruction, timing reconstruction, and distinguishing of overlapped signals are covered in this paper.

The Silicon Tracking System of the CBM Experiment at FAIR

The Compressed Baryonic Matter (CBM) experiment at FAIR (Darmstadt, Germany) is designed to study the dense nuclear matter in a fixed target configuration with heavy ion beams up to kinetic energies of 11 AGeV for Au+Au collision. The charged particle tracking with below 2% momentum resolution will be performed by the Silicon Tracking System (STS) located in the aperture of a dipole magnet. The detector will be able to reconstruct secondary decay vertices of rare probes, e.g., multistrange hyperons, with 50 мm spatial resolution in the heavy-ion collision environment with up to 1000 charged particle per inelastic interaction at the 10 MHz collision rate. This task requires a highly granular fast detector with radiation tolerance enough to withstand a particle fluence of up to 1014 neq/cm2 1-MeV equivalent accumulated over several years of operation. The system comprises 8 tracking stations based on double-sided silicon microstrip sensors with 58 мm pitch and strips oriented at 7.5∘ stereo angle. The analog signals are read out via stacked microcables (up to 50 cm long) by the front-end electronics based on the STS-XYTER ASIC with self-triggering architecture. Detector modules with this structure will have a material budget between 0.3% and 1.5% radiation length increasing towards the periphery. First detector modules and ladders built from pre-final components have been operated in the demonstrator experiment mCBM at GSI-SIS18 (FAIR Phase-0) providing a test stand for the performance evaluation and system integration. The results of mSTS detector commissioning and the performance in the beam will be presented.

Evolution of heavy ions (He2+, H+) radiolytic yield of molecular hydrogen vs. “Track-Segment” LET values

Ionizing radiation’s effects onto water molecules lead to the ionization and/or the excitation of them. Then, these phenomena are followed by the formation of radicals and molecular products. The linear energy transfer (LET), which defines the energy deposition density along the radiation length, is different according to the nature of ionizing particles. Thus, the values of radiolytic yields, defined as the number of radical and molecular products formed or consumed by unit of deposited energy, evolve according to this parameter. This work consists in following the evolution of radiolytic yield of molecular hydrogen and ferric ions according to the “Track-Segment” LET of ionizing particles (protons, helions). Concerning G(Fe

Development of a Stress Compensation Layer for 3-D Assembly of Thin Pixel Modules

Pixel modules are the fundamental building blocks of the ATLAS pixel detector system used in the CERN LHC facility. In their basic form, they consist of a silicon sensor that is flip-chip bonded to a CMOS read-out integrated chip (ROIC). One of the main objectives for the ATLAS experiment is to develop an approach toward low-mass modules, thus reducing radiation length. From the module perspective, this can be achieved by using advanced 3-D technology processes that include the formation of copper and solder microbumps on top of the ROIC front side, the thinning of both the sensor and the CMOS ROIC, and, finally, the flip-chip assembly of the two chips. The thinning of the silicon chips leads to low bump yield at the solder reflow stage, due to bad coplanarity of the two chips creating dead zones within the pixel array. In the case of the ROIC, which is thinned to 100 μm, the chip bow varies from −100 μm at room temperature to +175 μm at reflow temperature, resulting in CTE mismatch between materials in the CMOS stack and the silicon substrate. Our objective was to compensate dynamically for the stress of the front-side stack by adding a compensating layer to the back side of the wafer. Using our material thermomechanical database coupled with a proprietary analytical simulator, and measuring the bow of the ROIC at die level, we were able to reduce the bow magnitude by approximately a factor of 3 by introducing the compensating layer. We show that it is possible to change the sign of the bow at room temperature after deposition of a SiN/Al:Si stack. The amplitude of the correction can be manipulated by the deposition conditions of the SiN/Al:Si stack. Further development of the back-side deposition conditions are ongoing, where the target is to control the room temperature bow close to zero and reduce the bow magnitude throughout the full solder reflow temperature range, hence conserving bump yield. In keeping with a 3-D process, the materials used are compatible with through-silicon via (TSV) technology with a TSV-last approach in mind, should we integrate this technology in the future.