A review on machine learning approaches for predicting the effect of device parameters on performance of nanoscale MOSFETs
R Ghoshhajra, K Biswas… - 2021 Devices for Integrated …, 2021 - ieeexplore.ieee.org
This review investigates the possibility of using Machine Learning as a replacement for
numerical TCAD device simulation. As the chip design is getting complex to incorporate …
numerical TCAD device simulation. As the chip design is getting complex to incorporate …
Neural approach for modeling and optimizing Si-MOSFET manufacturing
An optimal design of semiconductor device and its process uniformity are critical factors
affecting desired figure-of-merits as well as reducing fabrication cost of fixing possible …
affecting desired figure-of-merits as well as reducing fabrication cost of fixing possible …
Sensitivity analysis based on neural network for optimizing device characteristics
This letter presents a novel method for the sensitivity analysis between a process parameter
and an electrical characteristic using the gradient of a neural network (NN). As devices …
and an electrical characteristic using the gradient of a neural network (NN). As devices …
Cell library characterization using machine learning for design technology co-optimization
To explore the full potential of any circuit and ensure its functionality at run-time, cell libraries
beyond the typical PVT corners are needed. This holds even more for emerging …
beyond the typical PVT corners are needed. This holds even more for emerging …
Device performance prediction of nanoscale junctionless FinFET using MISO artificial neural network
This paper investigates the way to use Multi-layer neural network as a possible replacement
of numerical TCAD device simulation to study device characteristics using limited …
of numerical TCAD device simulation to study device characteristics using limited …
Neural network based design optimization of 14-nm node fully-depleted SOI FET for SoC and 3DIC applications
In this article, by using neural network, we proposed a method to optimize Fully-Depleted
(FD) Silicon-on-Insulator (SOI) Field-Effect-Transistor (FET) structures to maximize the on/off …
(FD) Silicon-on-Insulator (SOI) Field-Effect-Transistor (FET) structures to maximize the on/off …
Design and system technology co-optimization sensitivity prediction for VLSI technology development using machine learning
As technology nodes evolve, geometric pitch scaling starts to slow down. In order to retain
the trend of the Moore's Law, Design Technology Co-Optimization (DTCO) and System …
the trend of the Moore's Law, Design Technology Co-Optimization (DTCO) and System …
Ensemble Learning strategy in modeling of future generation nanoscale devices using Machine Learning
In semiconductor industry, conventional method of device characteristics analysis using
TCAD based simulation is sometimes difficult. TCAD based device simulation gives the …
TCAD based simulation is sometimes difficult. TCAD based device simulation gives the …
Machine learning prediction for design and system technology co-optimization sensitivity analysis
As technology nodes continue to advance relentlessly, geometric pitch scaling starts to slow
down. In order to retain the trend of Moore's law, design technology co-optimization (DTCO) …
down. In order to retain the trend of Moore's law, design technology co-optimization (DTCO) …
Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices
Statistical modeling and optimization are critical for product quality management and
technology development in the manufacturing industry. As the emerging device appears and …
technology development in the manufacturing industry. As the emerging device appears and …