Safizan Shaari
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Preferred name
Safizan Shaari
Official Name
Safizan, Shaari
Alternative Name
Shaari, Safizan
Shaari, S.
Main Affiliation
Scopus Author ID
55807708700
Researcher ID
AAR-5562-2021

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The modelling of SiC Gate Oxide thickness based on thermal oxidation temperatures and durations for high-voltage applications

2023 , Nuralia Syahida Hashim , Banu Poobalan , Nor Farhani Zakaria , Manikandan Natarajan , Safizan Shaari

This research has shown that the oxide thickness for silicon carbide (SiC) based wide materials can be predicted using regression techniques in wet/dry nitrided or wet/dry non-nitrided thermal oxidation process conditions for high voltage applications by employing 2 different regression techniques: Polynomial and linear regression. The R-squared (R2) and Mean Absolute Percentage Error (MAPE) techniques are used to evaluate the regression models. Furthermore, this work investigates and presents a calculation of gate oxide thickness that is correlated to gate voltage ranges for high voltage applications. In this work, the thermal oxidation process environment is classified into 3 different processing conditions: conventional (dry and wet), dry nitrided (NO,N2O), and wet nitrided (HNO3 vapour). The findings from this study showed that wet oxidation combined with nitrided elements can produce thicker and better-quality gate oxide as compared to conventional dry and wet oxidation techniques. The outcome of this work clearly shows that gate oxide thickness may be derived from silicon carbide-based wide-bandgap materials utilizing linear and polynomial approaches using thermal oxidation durations at different temperatures for high-power applications. The regression models and formulations produced in this work are expected to aid the researchers in determining appropriate oxide thickness under practicable process conditions, with the exception of real thermal oxidation process conditions. Hence, the outcome of this work is expected to save the processing time, material, and cost of the power semiconductor device fabrication technology, mainly for high voltage applications.

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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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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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