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Defect and Material Characterization for Radiation Hardening

This project aims to detect and characterize electrically active defects in silicon, study defect annealing behavior, analyze microstructural investigations on extended and clustered defects, and develop theoretical models for defect kinetics. The project also involves device characterization and simulation using different silicon materials for radiation hardening.

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Defect and Material Characterization for Radiation Hardening

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  1. AIDA Michael Moll Michael Moll CERN EP-DT, Geneva, Switzerland

  2. Defect and Material Characterization Michael Moll • Main activities • Detection and microscopic characterization of standard and material engineered silicon via dedicated techniques (DLTS, TSC, TDRC, SIMS, ICP-MS, PITS, FTIR, TCT, EPR, HRTEM) • Identification of electrically active defects induced by irradiation responsible for trapping, leakage current, change of Neff , change of E-Field • Studying possible application for radiation hardening • Deliver input for device simulations (e.g. TCAD) to predict detector performance under various conditions • Milestones [2018-2022] • WP 1.1 p-type silicon [7 MS] • WP 1.2 Cluster defects [4 MS] • WP 1.3 Theory of defects and defect kinetics modeling [5 MS]

  3. Defect and Material Characterization Upcoming milestones Michael Moll • WP 1.1. Electrically active defects in p-type silicon Analysis of electrically active defects and of the radiation induced changes in the electrical characteristics of devices built on p-type silicon. • M1: Detection and characterization of all radiation induced defects in STFZ and engineered silicon (Q3/2019) • M2: Determine defect annealing behaviour in STFZ and engineered silicon. Correlation with device performances (Q4/2019) • M3: Determine defect transformations and kinetics in STFZ and engineered silicon after treatments at high temperatures (between 150 °C and 350 °C). Correlation with the device performance (Q3/2020) • M4: Identify the role of impurities in defect formation (Q1/2021) • M5: Detection/characterization of radiation induced defects in LGAD and HV-CMOS sensors made with STFZ and engineered p-type silicon, establishing annealing behaviour and correlation with electrical performance (Q3/2021) • M6: Validity tests on optimized material engineered sensors (pads, LGADs and HV-CMOSs). Comparison between prediction and experiments (Q1/2022) • M7: Validity tests on finally optimized material engineered sensors (pads, LGADs and HV-CMOSs) (Q3/2023)

  4. Defect and Material Characterization Upcoming milestones Michael Moll • WP 1.2. Microstructural Investigations on extended and clustered defectsThis work targets microstructural investigations of extended and clustered defects by electron microscopy: • M1: Microstructural characterization of the radiation induced clustered defects (fluences between 1015 and 1017neq cm-2) and monitoring of the evolution of clusters at 80 °C (Q3/2019) • M2: In situ- annealing studies at 5 temperatures (between 150 °C and 350 °C) in order to determine the structural transformations of the extended and clustered defects (Q3/2020) • M3: Microstructural characterization of the oxide-semiconductor interface in irradiated LGADs and HV-CMOS devices, time evolution at 80 °C (Q3/2021) • M4: Microstructural characterization of the oxide-semiconductor interface in irradiated optimized LGADs and HV-CMOS devices (Q3/2022) • WP 1.3 Theory of defects and defect kinetics modelling • M1: Modelling of the detected defect generation/kinetics and of the impact on the device performance corresponding to annealing treatments at 80 °C (Q3/2020) • M2: Modelling of the detected defect generation/kinetics and of the impact on the device performance corresponding to annealing at temperatures between 150 °C and 350 °C and final assessment of the role of the intentional added impurities (Q1/2021) • M3: Identification of the optimal impurity concentrations for pads, LGADs and HV-CMOSs as input for production. (Q3/2021) • M4: Improvements of the developed models according to validity test foreseen as 1.1-M6 and provide new optimization solutions for 5.1.1-M7. (Q3/2022) • M5: Validity test for the developed theoretical models based on the results obtained on 1.1-M7 optimized sensors (Q3/2023)

  5. Device Characterizationand Device Simulation Michael Moll • Milestones [2018-2022] • WP 2.1. Silicon materials [5 MS] • WP 2.2. Extreme fluences [5 MS] • WP 2.3. Experimental techniques [3 MS] • WP 2.4. Surface damage [1 MS] • WP 2.5. TCAD simulations [7 MS]

  6. Device Characterizationand Device Simulation Upcoming milestones Michael Moll • WP 2.1. Silicon Materials FZ p-type silicon will be used for ATLAS and CMS upgrades. In running pixel systems oxygen enriched (DOFZ) n-type with n-side readout is used. For radiation hardening different impurity engineered material like MCz, oxygen enriched epitaxial (DOEPI) or nitrogen enriched FZ or EPI will be tested. Also the optimal thickness of the sensor material has to be studied in more detail. • M1: Development of impurity engineered p-type silicon with possible vendors (Topsil for N enriched FZ, …) (Q3/2019) • M2: Search for possible production of nitrogen enriched epitaxial silicon (e.g. ITME) (Q3/2019) • M3: Production of diodes, strip and pixel sensors with engineered materials by different vendors (CiS, CNM, FBK, ….) and field tests (Q3/2019) • M4: Several irradiation campaigns with different particles up to fluences exceeding 1017neq/cm2 on standard and engineered materials (Q4/2020) • M5: Macroscopic studies on all irradiated devices including Edge-TCT, TPA-TCT and investigations with radioactive sources/test-beams, including annealing studies (Q3/2023).

  7. Device Characterizationand Device Simulation Upcoming milestones Michael Moll • WP 2.2. Extreme Fluences Device properties (I-V, C-V-f, CCE) on different p-type silicon materials to fluences ranging from 1016 to 5×1017neq cm-2 with neutrons and protons of different energies. • M1: Precise mobility parametrization for electrons and holes (Q4/2019) • M2: Development of method for extraction of trapping times/distances from the measured data (Q3/2020) • M3: Establishing leakage current behavior at extreme fluences (Q3/2020) • M4: Measurement of recombination lifetimes in silicon bulk (Q3/2021) • M5: Modeling/parameterization of CCE(fluence, voltage, annealing time) (Q3/2023)

  8. Device Characterizationand Device Simulation Upcoming milestones Michael Moll • WP 2.3. Experimental techniques Beside standard device characterization tools (I-V, C-V, TCT), more complex systems like “Edge-TCT” and “Two Photon Absorption (TPA)” technique became available. • M1: Full specification of a TPA-TCT method for the characterization of irradiated devices (Q4/2018) • M2: Commissioning of the top-bench fiber-based femto second laser TPA-TCT setup at the SSD/CERN (Q4/2019). • M3: Phenomenological parametrization of the radiation effects on diodes using the TPA-TCT method (Q3/2020)

  9. Device Characterizationand Device Simulation Upcoming milestones Michael Moll • WP 2.5. TCAD simulations and custom device simulators • M1: Comparison of commercial TCAD tools; preparation of a recommendation for parameters and physics models (Q4/2019) • M2: Development of a reliable radiation damage model (I-V, C-V, CCE and the E-field) covering the HL-LHC fluences for protons and neutrons for a given operation temperature (Q4/2020) • M3: Model M1 extended to cover temperature dependence of the bulk-damage related effects from room temperature down to -30 °C. (Q3/2021): • M4: Model from M2 extended to cover annealing effects (Q3/2022): • M5: Model of the donor and acceptor removal (SiPMs, LGAD, CMOS,..) (Q3/2020): • M6: Surface damage model with implementation of surface damage in p-type segmented sensors (Q1/2021) • M7: Evaluation of possible implementation of cluster defects in commercial TCAD device simulators using a charge carrier occupation dependent energy level distribution (Q2/2021)

  10. CiS – RD50 06/2019 Michael Moll

  11. CiS – RD50 06/2019 Michael Moll

  12. CiS – RD50 06/2019 Michael Moll

  13. Budget – for discussion Michael Moll

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