Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN

This study offers a precise, expandable, and effective ANN (Artificial Neural Network) model to evaluate and calculate the important device parameters like breakdown voltage, efficiency, negative conductance, negative resistance, susceptance, and RF power of a heterostructure Si/3C-SiC-based IMPATT...

Full description

Saved in:
Bibliographic Details
Main Authors: Mamata Rani Swain, Pravash Ranjan Tripathy
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:e-Prime: Advances in Electrical Engineering, Electronics and Energy
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S277267112500124X
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849690411019272192
author Mamata Rani Swain
Pravash Ranjan Tripathy
author_facet Mamata Rani Swain
Pravash Ranjan Tripathy
author_sort Mamata Rani Swain
collection DOAJ
description This study offers a precise, expandable, and effective ANN (Artificial Neural Network) model to evaluate and calculate the important device parameters like breakdown voltage, efficiency, negative conductance, negative resistance, susceptance, and RF power of a heterostructure Si/3C-SiC-based IMPATT diode at an operating frequency of 94 GHz. The authors have compared the simulation and optimization of a Si/SiC-based heterostructure IMPATT diode with the neural network techniques for CW operation. The experimental data are almost 85 % to 90 % the same as computer simulation outcomes and provide numerically agreed results regarding breakdown voltage, efficiency, negative conductance, and power. Owing to several factors such as temperature, parasitic impacts, and appropriate hit sink arrangements, there is a 10 %–15 % discrepancy between the theoretical simulation result and the experimental output. This newly developed ANN technique, developed by the authors for the first time, was found to be in close agreement with the experimental findings available at 94.0 GHz. The simulation result gives the breakdown voltage of the IMPATT device as 188 V as compared with the experimental results of 185 V. Similarly, the neural network model shows approximately 183 V. The RF power of the simulated device is 2.5 W as compared to the experimental result of 2.0 W at 94 GHz, whereas the neural network model gives 2.2 W, which shows the validity of the model. The assessed outcomes clearly demonstrate the effectiveness of the device parameter estimations and optimizing IMPATT device design efficiently, and the findings will benefit missile and radar technology.
format Article
id doaj-art-2e71a4f4e2f844809d48a82873adacc5
institution DOAJ
issn 2772-6711
language English
publishDate 2025-06-01
publisher Elsevier
record_format Article
series e-Prime: Advances in Electrical Engineering, Electronics and Energy
spelling doaj-art-2e71a4f4e2f844809d48a82873adacc52025-08-20T03:21:19ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-06-011210101710.1016/j.prime.2025.101017Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANNMamata Rani Swain0Pravash Ranjan Tripathy1Synergy Institute of Technology, BPUT, Rourkela, Odisha, India; Corresponding author.Gandhi Engineering College, Bhubaneswar, IndiaThis study offers a precise, expandable, and effective ANN (Artificial Neural Network) model to evaluate and calculate the important device parameters like breakdown voltage, efficiency, negative conductance, negative resistance, susceptance, and RF power of a heterostructure Si/3C-SiC-based IMPATT diode at an operating frequency of 94 GHz. The authors have compared the simulation and optimization of a Si/SiC-based heterostructure IMPATT diode with the neural network techniques for CW operation. The experimental data are almost 85 % to 90 % the same as computer simulation outcomes and provide numerically agreed results regarding breakdown voltage, efficiency, negative conductance, and power. Owing to several factors such as temperature, parasitic impacts, and appropriate hit sink arrangements, there is a 10 %–15 % discrepancy between the theoretical simulation result and the experimental output. This newly developed ANN technique, developed by the authors for the first time, was found to be in close agreement with the experimental findings available at 94.0 GHz. The simulation result gives the breakdown voltage of the IMPATT device as 188 V as compared with the experimental results of 185 V. Similarly, the neural network model shows approximately 183 V. The RF power of the simulated device is 2.5 W as compared to the experimental result of 2.0 W at 94 GHz, whereas the neural network model gives 2.2 W, which shows the validity of the model. The assessed outcomes clearly demonstrate the effectiveness of the device parameter estimations and optimizing IMPATT device design efficiently, and the findings will benefit missile and radar technology.http://www.sciencedirect.com/science/article/pii/S277267112500124XHetero-structureIMPATTSCDDRF powerMillimeter-waveMLP
spellingShingle Mamata Rani Swain
Pravash Ranjan Tripathy
Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
e-Prime: Advances in Electrical Engineering, Electronics and Energy
Hetero-structure
IMPATT
SCDD
RF power
Millimeter-wave
MLP
title Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
title_full Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
title_fullStr Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
title_full_unstemmed Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
title_short Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN
title_sort modeling and optimization of hs impatt diode execution enriched with si sic using ann
topic Hetero-structure
IMPATT
SCDD
RF power
Millimeter-wave
MLP
url http://www.sciencedirect.com/science/article/pii/S277267112500124X
work_keys_str_mv AT mamataraniswain modelingandoptimizationofhsimpattdiodeexecutionenrichedwithsisicusingann
AT pravashranjantripathy modelingandoptimizationofhsimpattdiodeexecutionenrichedwithsisicusingann