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  4. SMART EIS Sensor Interface – Cadence Virtuoso Design Files & MATLAB/Simulink Analysis Data
 
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SMART EIS Sensor Interface – Cadence Virtuoso Design Files & MATLAB/Simulink Analysis Data

Citation Link: https://doi.org/10.15480/882.16892
Type
Simulation Data
Version
Version_1
Date Issued
2026-03-19
Author(s)
Kumar, Anupam Hari  orcid-logo
Albert-Ludwigs-Universität Freiburg  
Researcher
Becker, Maximilian  
Mechatronik im Maschinenbau M-4  
Orth, Maike  orcid-logo
Feststoffverfahrenstechnik und Partikeltechnologie V-3  
Contact
Kuhl, Matthias  orcid-logo
Integrierte Schaltungen E-9  
Language
English
DOI
https://doi.org/10.15480/882.16892
TORE-URI
https://hdl.handle.net/11420/62226
References
10.15480/882.16823
Abstract
This dataset supports the publication "Towards Smart Sensor Particles for Fluidized Bed Granulators Using Electrical Impedance Spectroscopy: A Conceptual Design" (submitted to ACS Sensors). It contains two categories of data: (1) Cadence Virtuoso design libraries for the SMARTe_V2 custom ASIC, implemented in X-FAB 180 nm CMOS technology (xh018), comprising a transimpedance amplifier, fully differential difference amplifier, passive switching mixer, and top-level pad frame integration; (2) MATLAB scripts and Simulink models for system-level analysis of the lock-in amplifier-based EIS front-end, including optimal component sizing, loop gain analysis, Bode characterization, and post-processing of experimental impedance measurements from the dataset cited hereby. The data is intended to support reproducibility and reuse of the presented EIS sensor interface design.
Subjects
Electrochemical Impedance Spectroscopy
Custom Integrated Circuit
Lock-in Amplifier
Fluidized Bed Granulation
MATLAB
Cadence Virtuoso
DDC Class
621.38: Electronics, Communications Engineering
Funding(s)
SFB 1615 - Teilprojekt A07: Hochintegrierte Sensoren für die Inline-Erfassung des Granulationsfortschrittes in Wirbelschichtreaktoren  
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
More Funding Information
This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1615 – 503850735.
License
https://mit-license.org/
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data.zip

Size

3.07 MB

Format

ZIP

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