Development Of A Cmos Pixel Sensor With On Chip Artificial Neural Networks
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Development of a CMOS Pixel Sensor with On-chip Artificial Neural Networks
In the vertex detector of the ILC (International Linear Collider), a large number of extra hits will be generated by electrons coming from the beam background. Momenta of these background electrons typically are lower than particles coming from physics events. Our group in IPHC has proposed the concept of a CMOS pixel sensor with on-chip ANNs to tag and remove hits generated by background particles.During my PhD thesis, I focused on the study of a CMOS pixel sensor with on-chip ANNs from the following aspects :1. The implementation of preprocessing modules and an ANN in an FPGA device for the feasibility study ;2. An on-chip algorithm for cluster search which is a part of preprocessing modules has been proposed to integrate into the ASIC design.
Artificial Neural Networks – ICANN 2009
This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14–17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.
Graphene for Post-Moore Silicon Optoelectronics
Graphene for Post-Moore Silicon Optoelectronics Provides timely coverage of an important research area that is highly relevant to advanced detection and control technology Projecting device performance beyond the scaling limits of Moore’s law requires technologies based on novel materials and device architecture. Due to its excellent electronic, thermal, and optical properties, graphene has emerged as a scalable, low-cost material with enormous integration possibilities for numerous optoelectronic applications. Graphene for Post-Moore Silicon Optoelectronics presents an up-to-date overview of the fundamentals, applications, challenges, and opportunities of integrating graphene and other 2D materials with silicon (Si) technologies. With an emphasis on graphene-silicon (Gr/Si) integrated devices in optoelectronics, this valuable resource also addresses emerging applications such as optoelectronic synaptic devices, optical modulators, and infrared image sensors. The book opens with an introduction to graphene for silicon optoelectronics, followed by chapters describing the growth, transfer, and physics of graphene/silicon junctions. Subsequent chapters each focus on a particular Gr/Si application, including high-performance photodetectors, solar energy harvesting devices, and hybrid waveguide devices. The book concludes by offering perspectives on the future challenges and prospects of Gr/Si optoelectronics, including the emergence of wafer-scale systems and neuromorphic optoelectronics. Illustrates the benefits of graphene-based electronics and hybrid device architectures that incorporate existing Si technology Covers all essential aspects of Gr/Si devices, including material synthesis, device fabrication, system integration, and related physics Summarizes current progress and future challenges of wafer-scale 2D-Si integrated optoelectronic devices Explores a wide range of Gr/Si devices, such as synaptic phototransistors, hybrid waveguide modulators, and graphene thermopile image sensors Graphene for Post-Moore Silicon Optoelectronics is essential reading for materials scientists, electronics engineers, and chemists in both academia and industry working with the next generation of Gr/Si devices.