Neuroevolution-Based Efficient Field Effect Transistor Compact Device Models

Ho, Ya-Wen; Rawat, Tejender Singh; Yang, Zheng-Kai; Pratik, Sparsh; Lai, Guan-Wen; Tu, Yen-Liang; Lin, Albert

doi:10.1109/access.2021.3130254

Cited by 5 publications

(5 citation statements)

References 40 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, we used a NE-based model with genetic algorithms (GA) in transistor compact device modeling and achieved satisfactory results. 25 In this work, the NE method is applied to searching the neural network architecture shown in Figure 1 , which can find the optimized model to extract domain knowledge in semiconductor manufacturing. Initially, the input parameters are denoted as dopants, Cdose1, Cdose2, Endose1, Endose2, and Edose for the abovementioned parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, we take advantage of the automatic search ability of NAS to find out a particular network architecture as a type of domain knowledge constraint to help convergence. Previously, we have received some achievements in optimizing ML compact device models via NE algorithms . In our work, the NE algorithm capable of dynamically tuning model architectures is utilized as a model optimization method to extract the physical characteristics of semiconductor devices manufactured under different process parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, we have received some achievements in optimizing ML compact device models via NE algorithms. 25 In our work, the NE algorithm capable of dynamically tuning model architectures is utilized as a model optimization method to extract the physical characteristics of semiconductor devices manufactured under different process parameters. There have been very few studies in the field of semiconductors using NE with evolved topologies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing

2023

Self Cite

View full text Add to dashboard Cite

Promoted model architectures or algorithms are crucial for intelligent manufacturing since developing them takes a lot of trial and error to embed the domain knowledge into the models correctly. Especially in semiconductor manufacturing, the whole processes depend on complicated physical equations and sophisticated fine-tuning. Therefore, we use a neuroevolution-based model to search the optimized architecture automatically. The collector current value at a particular bias of the silicon–germanium (SiGe) heterojunction bipolar transistor, generated by technology computer-aided design (TCAD), is used as the target dataset with six process parameters as the inputs. The processes include oxidation, dry and wet etching, implantation, annealing, diffusion, and chemical–mechanical polishing. Our work can build a suitable model network with a fast turnaround time, and practical physical constraints are fused in it without domain knowledge extraction. Take the case with 3840 data and one output as an instance. The mean square errors of the train set and validation set, as well as the mean absolute percentage error of the test set, are 1.317 × 10–6, 7.215 × 10–7, and 0.216 while using multilayer perceptron (MLP) and they are 3.285 × 10–7, 1.661 × 10–7, and 0.097 while using NE. The consequences show that the work in this vein is promising. According to the trend plot and results, the ability to extract physic is much better than the traditional (MLP) model.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [23], a genetic algorithm was used with the ADAM optimizer to optimize the architecture and parameters of small neural networks. The problem addressed was the compact modeling of MOSFET devices using neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering

et al. 2022

View full text Add to dashboard Cite

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.

show abstract

“…Ho et al [15] proposed an algorithm that utilizes genetic algorithms and multi-layer perceptrons to predict current (I d ) using an evolved neural network. Their work demonstrated that the prediction accuracy of the evolved neural network was superior to that of a conventional multi-layer perceptron.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks-Based Direct-Current Operation Prediction and Circuit Migration Design

Liu

Jian

et al. 2023

Electronics

View full text Add to dashboard Cite

Recently, design methods based on gm/Id parameters have attracted attention in analog integrated circuit design and have been automated with computer assistance. However, the look-up tables (LUTs) in the gm/Id method have the problem of high hardware resource overhead. To address this issue, this paper proposes a multi-output deep neural network (DNN) structure for modeling the direct-current parameters of transistors and replacing LUTs for circuit design. The proposed DNN models’ performance is verified using mainstream design technologies such as TSMC 40 nm (T40), TSMC 65 nm (T65), TSMC 180 nm (T180), and SMIC 180 nm (S180). Compared with LUTs, the proposed DNN models are able to reduce at least 99.9% storage space occupation and 95.62% prediction time overhead with a mean absolute percentage error of less than 0.2%. In addition, we propose an automated circuit migration design method using DNN models in different technologies, combined with gm/Id parameters. The method generates circuit design databases in different technologies and obtains device design results according to performance requirements. The experimental results show that using DNN models can reduce the time overhead by more than 40% compared to using LUTs. The simulation results of circuit transplantation design show that the circuit performance of T40, T65, S180, and T180 meets the requirements, which verifies the proposed DNN-based automated circuit design method.

show abstract

Neuroevolution-Based Efficient Field Effect Transistor Compact Device Models

Cited by 5 publications

References 40 publications

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing

A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering

Deep Neural Networks-Based Direct-Current Operation Prediction and Circuit Migration Design

Contact Info

Product

Resources

About