Shahrir Rizal Kasjoo
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Preferred name
Shahrir Rizal Kasjoo
Official Name
Shahrir Rizal, Kasjoo
Alternative Name
Kasjoo, S.
Kasjoo, Shahrir R.
Kasjoo, S. R.
Shah K.
Main Affiliation
Scopus Author ID
36809748400
Researcher ID
ABI-6061-2020

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Hybrid statistical and numerical analysis in structural optimization of silicon-based RF Detector in 5G Network

2022-02-01 , Tan Yi Liang , Nor Farhani Zakaria , Shahrir Rizal Kasjoo , Safizan Shaari , Muammar Mohamad Isa , Mohd Khairuddin Md Arshad , Arun Kumar Singh , Sharizal Ahmad Sobri

In this study, a hybrid statistical analysis (Taguchi method supported by analysis of variance (ANOVA) and regression analysis) and numerical analysis (utilizing a Silvaco device simulator) was implemented to optimize the structural parameters of silicon-on-insulator (SOI)-based self-switching diodes (SSDs) to achieve a high responsivity value as a radio frequency (RF) detector. Statistical calculation was applied to study the relationship between the control factors and the output performance of an RF detector in terms of the peak curvature coefficient value and its corresponding bias voltage. Subsequently, a series of numerical simulations were performed based on Taguchi’s experimental design. The optimization results indicated an optimized curvature coefficient and voltage peak of 26.4260 V−1 and 0.05 V, respectively. The alternating current transient analysis from 3 to 10 GHz showed the highest mean current at 5 GHz and a cut-off frequency of approximately 6.50 GHz, indicating a prominent ability to function as an RF detector at 5G related frequencies.

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Publication

Hybrid statistical and numerical analysis in structural optimization of silicon-based RF detector in 5G network

2022-01-21 , Tan Yi Liang , Nor Farhani Zakaria , Shahrir Rizal Kasjoo , Safizan Shaari , Muammar Mohamad Isa , Mohd Khairuddin Md Arshad , Arun Kumar Singh , Sharizal Ahmad Sobri

In this study, a hybrid statistical analysis (Taguchi method supported by analysis of variance (ANOVA) and regression analysis) and numerical analysis (utilizing a Silvaco device simulator) was implemented to optimize the structural parameters of silicon-on-insulator (SOI)-based self-switching diodes (SSDs) to achieve a high responsivity value as a radio frequency (RF) detector. Statistical calculation was applied to study the relationship between the control factors and the output performance of an RF detector in terms of the peak curvature coefficient value and its corresponding bias voltage. Subsequently, a series of numerical simulations were performed based on Taguchi’s experimental design. The optimization results indicated an optimized curvature coefficient and voltage peak of 26.4260 V−1 and 0.05 V, respectively. The alternating current transient analysis from 3 to 10 GHz showed the highest mean current at 5 GHz and a cut-off frequency of approximately 6.50 GHz, indicating a prominent ability to function as an RF detector at 5G related frequencies.

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