SOI pixel detector based on CMOS time-compression charge-injection
Concept and experimental results obtained from a pixel detector based on CMOS time-compression charge- injection-devices (TC-CID) with a huge internal photocurrent amplification (-104), fabricated in CMOS silicon-on-insulator (SOI) technology are presented. Here, the readout circuitry is fabricated on highly-doped, 200 nm thick SOI film, while the photogate (PG) detector is fabricated on higher-resistivity handle wafer. The latter, together with the 30 V biasing possibilities enhances the quantum efficiency, especially for irradiations with wavelengths in the near-infra-red (NIR) part of the spectra.
Charge-injection photogate pixel fabricated in CMOS silicon-on-insulator technology
Concept, theoretical analysis, and experimental results obtained from a charge-injection photogate (CI-PG) pixel detector fabricated in CMOS silicon-on-insulator (SOI) technology are presented. The charge collected in the photodetector during a certain charge collection (integration) time is injected into the substrate for readout. This readout principle presents a huge internal photocurrent amplification (∼104) taking place in the photodetector, obtained through the ‘time-compression’ approach. Here, the readout circuitry is fabricated on highly doped, 200 nm thick, SOI film, while the photogate detector is fabricated on higher resistivity handle-wafer. The latter, together with the 30 V biasing possibilities, enhances the quantum efficiency of the pixel, especially for irradiations with wavelengths in the near-infra-red part of the spectra. Copyright © 2008 John Wiley & Sons, Ltd.
Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
Abstract The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) interval of Sawan gas field, Middle Indus Basin, Pakistan. The hydrocarbon bearing zone is well identified through the seismic attribute analysis along a sand channel. The sparse-spike inversion analysis has efficiently captured the variations in reservoir parameter (P-impedance) for gas prospect. Inversion results indicated that the relatively lower P-impedance values are encountered along the predicted sand channel. To further characterize the reservoir, geostatistical techniques comprising multiattribute regression and probabilistic neural network (PNN) analysis are applied to predict the effective porosity of reservoir. Comparatively, the PNN analysis predicted the targeted property more efficiently and applied its estimations on entire seismic volume. Furthermore, the geostatistical estimations of PNN analysis significantly predicted the gas-bearing zones and confirmed the sand channel as a major contributor of gas accumulation in the area. These estimates are in appropriate agreement with each other, and the workflow adopted here can be applied to various South Asian regions and in other parts of the world for improved characterization of gas reservoirs.