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Junction engineering of microstrip silicon sensors to operate in controlled charge multiplication mode for enhanced radiation tolerance

Funder: UK Research and InnovationProject code: ST/H003924/1
Funded under: STFC Funder Contribution: 75,256 GBP

Junction engineering of microstrip silicon sensors to operate in controlled charge multiplication mode for enhanced radiation tolerance

Description

This project involves the high energy Physics (HEP) group of the University of Liverpool and Micron Semiconductor (UK) Ltd as the industrial partner. The Liverpool group has a world recognised track record in R&D, assembly and commissioning of silicon detectors for HEP experiments. Micron Semiconductor LTd is a leading supplier of silicon detectors to particle physics experiments, having a long track record dating from fixed target programmes, to LEP, Tevatron, PEP-II, HERA and the LHC. However, Micron have diversified into work for space (with contracts with NASA,in USA and JAXA in Japan), defence, nuclear and medical applications with particle physics representing only about 20% of current orders. However, the development of new technology and techniques for these other areas has historically always taken place in the context of meeting the challenges of particle physics and the current programme represents an excellent opportunity for both the company and the student. The over 15 years long collaboration between Micron and the University of Liverpool has lead to the production of extremely successful silicon sensors for HEP experiments like CDF at Tevatron-FNAL, DELPHI at CERN-LEP, ATLAS and LHCb at the CERN-LHC. It is worth noting that the first p-type sensors, leading to the development for the n-in-p technology now the default for sLHC, grew out of a CASE studentship between Liverpool and Micron, for which the student, Moshe Hanlon, won the Rutherglen Prize for his PhD. thesis research. The work will focus on the optimisation of the design and processing parameters of silicon detectors to operate in a controlled charge multiplication regime by mean of the shaping of the electric field at the junction side. In the first phase (a) of the project the student will perform2-d and 3-d device simulations of the, using TCAD and/or custom made charge transport models. A model of the radiation damaged silicon will be implemented by the student using the existing literature and the experimental data accumulated by the group. The performance of irradiated silicon is a subject of high interest to both industry and academy, and the ability to access a substantial amount of experimental data to compare to the simulations, with the deep understanding of the tested devices coming from the collaboration with the specialised industrial partner makes a unique working environment likely to lead to publishable results and to enhanced manufacturing methods. The student will design the photolithography mask set using a state of the art package that will be adopted by the industrial partner for processing the novel devices. Pad diodes, microstrip detectors, pixels and various test structures for process control and optimisation will be designed (phase (b)) and produced. Both task (a) and (b) will be performed at the University of Liverpool during the first semester of the thesis. The following phase (c) will start on the 2nd semester and will involve close collaboration with the company to learn the processing parameters that can be tuned for a fine shaping of the junction. The production of a first set of wafers (10-15) will be based on intuitive modifications (in the opposite directions of a steeper and smoother profile) of the current processing parameters to explore more extreme junction geometries (step or diffused junction) and their performance after irradiation. The first processed detectors can be expected within the 1st semester of the 2nd year, with measurements (performed by the student under the initial guidance of the supervisor) taking place within the end of the same year. This allows for feedback from the measurement to correct the processing parameters, verify and improve the simulation with information from measured data. The finally optimised geometries will be processed and characterised in the last year, with fine tuning of the parameters.

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