We studied lateral silicon p-i-n junctions, doped with phosphorus and boron, regarding charge sensing feasibility. In order to examine the detection capabilities and underlying mechanism, we used in a complementary way two measurement techniques. First, we employed a semiconductor parameter analyzer to measure I−V characteristics at a low temperature, for reverse and forward bias conditions. In both regimes, we systematically detected Random Telegraph Signal. Secondly, using a Low Temperature Kelvin Probe Force Microscope, we measured surface electronic potentials. Both p-i-n junction interfaces, p-i and i-n, were observed as regions of a dynamic behaviour, with characteristic time-dependent electronic potential fluctuations. Those fluctuations are due to single charge capture/emission events. We found analytically that the obtained data could be explained by a model of two-dimensional p-n junction and phosphorus-boron interaction at the edge of depletion region. The results of complementary measurements and analysis presented in this research, supported also by the previous reports, provide fundamental insight into the charge sensing mechanism utilizing emergence of individual dopants.

Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.

Fano resonance is an optical effect that emerges from the coherent coupling and interference (constructive and destructive) between the continuous state (background process) and the Lorentzian state (resonant process) in the plasmonic waveguide-resonator system. This effect has been used in the applications like optical sensors. These sensors are extensively used in sensing biochemicals and gases by the measurement of refractive index changes as they offer high sensitivity and ultra-high figure of merit. Herein, we surveyed several plasmonic Fano sensors with different geometries composed of metal-insulator-metal waveguide(s). First, the resonators are categorized based on different architectures. The materials and methods adopted for these designs are precisely surveyed and presented. The performances are compared depending upon the characterization parameters like sensitivity and figure of merit. Finally, based on the survey of very recent models, the advances and challenges of refractive index sensing deployed on Fano resonances are discussed.