PP
Paolo PAVAN
University of Modena and Reggio Emilia
Research areas: Engineering · Electrical and Electronic Engineering · Semiconductor materials and devices · Ferroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing
0000-0001-5420-1797
Employment
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University of Modena and Reggio Emilia Professor
Education
Education history is unavailable.
Projects & Funding
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Publications (297)
- Calibration and Enhancement of Analytical IR-Drop Model for Resistive Crossbar Arrays Save
- Physics-based RRAM compact model for multilevel programming across multiple timescales Save
- Design for Reliability of Multi-Bit Operations in RRAM-Based SIMPLY Logic-in-Memory Circuits Save
- Physical Modelling of Charge Trapping Effects Save
- Simulation and benchmarking of crossbar parasitic resistance models: Accuracy and performance comparison Save
- Unobtrusive Multimodal Monitoring of Physiological Signals for Driver State Analysis Save
- A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules Save
- Correlating Interface and Border Traps With Distinctive Features of C–V Curves in Vertical Al$_{\text{2}}$O$_{\text{3}}$/GaN MOS Capacitors Save
- Driving Towards Safety: Online PPG-based Drowsiness Detection with TCNs Save
- Experimental and Numerical Analysis of OFFState Bias Induced Instabilities in Vertical GaNon-Si Trench MOSFETs Save
- From Accelerated to Operating Conditions: How Trapped Charge Impacts on TDDB in SiO2 and HfO2 Stacks Save
- Physical insights into trapping effects on vertical GaN-on-Si trench MOSFETs from TCAD Save
- Vertical GaN Devices: Reliability Challenges and Lessons Learned from Si and SiC Save
- Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance Save
- A Unified Framework to Explain Random Telegraph Noise Complexity in MOSFETs and RRAMs Save
- ANGELS - Smart Steering Wheel for Driver Safety Save
- Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection Save
- Local electric field perturbations due to trapping mechanisms at defects: What random telegraph noise reveals Save
- Reliability of HfO2-Based Ferroelectric FETs: A Critical Review of Current and Future Challenges Save
- Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model Save