1. IHP, Frankfurt (Oder), Germany
2. Department of Electrical Engineering, UFMG - Belo Horizonte-MG, Brazil
3. University of Applied Sciences Wildau, Germany
Design methodologies. Digital and analog synthesis. Hardwaresoftware codesign. Reconfigurable hardware. Hardware description languages. Intellectual property-based design. Design reuse.2. Thermal Issues in Microelectronics
Thermal and electro-thermal modelling, simulation methods and tools. Thermal mapping. Thermal protection circuits.3. Analysis and Modelling of ICs and Microsystems
Simulation methods and algorithms. Behavioural modelling with VHDL-AMS and other advanced modelling languages. Microsystems modelling. Model reduction. Parameter identification.4. Microelectronics Technology and Packaging
New microelectronic technologies. Packaging. Sensors and actuators.5. Testing and Reliability
Design for testability and manufacturability. Measurement instruments and techniques.
Design, manufacturing and simulation of power semiconductor devices. Hybrid and monolithic Smart Power circuits. Power integration.
Digital and analogue filters, telecommunication circuits. Neural networks. Fuzzy logic. Low voltage and low power solutions.
Design, verification and applications.
Medical and biotechnology applications. Biometrics. Thermography in medicine.10. Artificial Intelligence in Electronic SystemsAI-driven design. AI-driven signal and data processing. Edge AI.
Lodz University of Technology
Department of Microelectronics and Computer Science (K-22)
ul. Wólczańska 221 (building B18)93-005 Łódź, Poland
Abstract: Neural compact models are proposed to simplify device-modeling processes without requiring domain expertise. However, the existing models have certain limitations. Specifically, some models are not parameterized, while others compromise accuracy and speed, which limits their usefulness in multi-device applications and reduces the quality of circuit simulations. To address these drawbacks, a neural compact modeling framework with a flexible selection of technology-based model parameters using a two-stage neural network (NN) architecture is proposed. The proposed neural compact model comprises two NN components: one utilizes model parameters to program the other, which can then describe the current–voltage (IV) characteristics of the device. Unlike previous neural compact models, this two-stage network structure enables high accuracy and fast simulation program with integrated circuit emphasis (SPICE) simulation without any trade-off. The IV characteristics of 1000 amorphous indium–gallium–zinc-oxide thin-film transistor devices with different properties obtained through fully calibrated technology computer-aided design simulations are utilized to train and test the model and a highly precise neural compact model with an average IDS error of 0.27% and R2 DC characteristic values above 0.995 is acquired. Moreover, the proposed framework outperforms the previous neural compact modeling methods in terms of SPICE simulation speed, training speed, and accuracy.
DevIC 2021: Call for Papers