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Journal Articles
- 2024
[j2]Alecsandra Rusu
, Emilian David, Vasile Grosu, Marina Dana Topa, Andi Buzo, Bianca Carbunescu, Georg Pelz:
On Multivariate Electrical Performance Machine Learning Driven Pre-Silicon IC Adaptive Verification. IEEE Access 12: 136436-136450 (2024)- 2022
- [j1]Elena-Diana Sandru, Emilian David, Ingrid Kovacs, Andi Buzo, Corneliu Burileanu, Georg Pelz:
Modeling the Dependency of Analog Circuit Performance Parameters on Manufacturing Process Variations With Applications in Sensitivity Analysis and Yield Prediction. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(1): 129-142 (2022)
Conference and Workshop Papers
- 2024
- [c8]Alecsandra Rusu, Emilian David, Marina Dana Topa, Vasile Grosu, Andi Buzo, Georg Pelz:
On Approaching Multivariate IC Pre-silicon Verification Using ML-based Adaptive Algorithms. IOLTS 2024: 1-3 - [c7]Vasile Grosu, Emilian David, Liviu Goras, Georg Pelz:
Multiple Output Modelling of Integrated Circuits Behavior Using an Active Learning Approach. SMACD 2024: 1-4 - [c6]Ingrid Kovacs, Bianca Carbunescu-Stoenescu, Marina Dana Topa, Andi Buzo, Emilian David, Georg Pelz:
Frontend-Backend Integrated Circuit Device Linking Using Machine Learning Algorithms. SMACD 2024: 1-4 - 2020
- [c5]Vasilica-Daniela Andries, Liviu Goras, Emilian David, Andi Buzo, Georg Pelz:
On the Pole-Placement Technique for the Design of a DC-DC Buck Converter Discrete PID Control. DDECS 2020: 1-4 - [c4]Andrei Gaita, Georgian Nicolae, Emilian C. David, Andi Buzo, Corneliu Burileanu, Georg Pelz:
A SIFT-based Waveform Clustering Method for aiding analog/mixed-signal IC Verification. ETS 2020: 1-2 - [c3]Elena-Diana Sandru, Emilian David, Georg Pelz:
Machine Learning-Based Local Sensitivity Analysis of Integrated Circuits to Process Variations. ICECS 2020: 1-2 - 2019
- [c2]Elena-Diana Sandru, Emilian David, Georg Pelz:
Pre-Silicon Yield Estimation using Machine Learning Regression. ICECS 2019: 103-104 - [c1]Elena-Diana Sandru, Corneliu Burileanu, Emilian David, Andi Buzo, Georg Pelz:
Modeling the Dependencies between Circuit and Technology Parameters for Sensitivity Analysis using Machine Learning Techniques. SMACD 2019: 237-240
Coauthor Index
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