Explore challenges and solutions in AI chip development
Discover and learn how modeling with Synopsys atomistic QuantumATK and TCAD Sentaurus Device tools is pivotal to designing novel sensor devices.
Dr. Sayan Kanungo from Birla Institute of Technology and Science presents an investigation of a multifunctional breath and pressure sensor using combined atomistic modeling with Synopsys QuantumATK, sensor device fabrication and ex-vivo testing.
Atomistic calculations with DFT-D3 in QuantumATK are used to identify the transduction mechanism for pressure and breath sensing in Perylene Diimide (PDI): study the influence of reduced PDI-PDI inter-molecule distance (i.e., pressure effect) on DOS and investigate the favorable H2O adsorption sites on PDI, adsorption energies and charge transfer.
Dr. Gabriele Boschetto and Prof. Aida Todri-Sanial from CNRS and Eindhoven University of Technology showcase the design of a strain sensor based on CNTs and MoS2 using a multi-disciplinary approach: atomistic modeling with Synopsys QuantumATK, sensor fabrication and ex-vivo testing for monitoring respiratory rate.
In this study, CNT percolating network and single-layer MoS2 are modeled using DFT-D2 method in QuantumATK to compute bandgaps and electron effective masses (to estimate electron mobility and conductivity) as a function of strain and thus structural deformations along chosen directions.
Dr. Gabriele Boschetto, Dr. Stefania Carapezzi and Prof. Aida Todri-Sanial from CNRS and Eindhoven University of Technology share how multiscale Synopsys atomistic (QuantumATK) to TCAD (Sentaurus Device) modeling workflow is used to predict the chemresistive response of the designed MoS2 FET-based sensor for the detection of cortisol.
DFT-D2 method in QuantumATK is used to calculate band gaps, effective masses, relative permittivities, adsorption energies and charge transfer for different Pt cluster sizes on MoS2 surface with and without adsorbed cortisol. Then DFT calculated parameters are used as an input to Sentaurus Device simulations of MoS2-FET to predict device response, i.e., IDS-VGS, with respect to the Pt cluster size, cluster density and cluster occupancy with cortisol.
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