Accurate, Efficient and Reliable Small-Signal Modeling Approaches for GaN HEMTs
At a Glance
Section titled “At a Glance”| Metadata | Details |
|---|---|
| Publication Date | 2023-01-01 |
| Journal | IEEE Access |
| Authors | Saddam Husain, Anwar Jarndal, Mohammad Hashmi, Fadhel M. Ghannouchi |
| Institutions | University of Calgary, Nazarbayev University |
| Citations | 12 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled “Executive Summary”This analysis details the development and validation of accurate Small-Signal Equivalent Circuit Models (SSECMs) for Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) grown on diamond substrates.
- Core Achievement: Successfully implemented and compared a Scanning-Based Systematic (SBS) extraction method against three novel Optimization Algorithm (OA)-based hybrid methods: Marine Predators Algorithm (MPA), Pelican Optimization Algorithm (POA), and Tunicate Swarm Algorithm (TSA).
- Performance Validation: All four modeling approaches achieved excellent agreement between measured and simulated S-parameters, with minimum errors consistently around 0.06% at the Cold-FET pinch-off condition.
- Frequency Range: The models are validated and reliable across an extensive frequency range, from 0.1 GHz up to 40 GHz, suitable for mmWave applications.
- Methodological Trade-offs: The SBS method is significantly faster (7.03 s execution time) but requires assumptions (e.g., Cpga = Cpda), limiting its physical relevance and generalization.
- OA Superiority: OA-based hybrid methods (MPA, POA, TSA) produce physically relevant and reliable SSECMs without requiring simplifying assumptions, and accurately capture complex device physics (kink effect, RF-DC dispersion).
- Efficiency Winner: The TSA-based hybrid approach demonstrated the fastest execution time among the OAs (50.48 s for 50 iterations), making it the most efficient hybrid technique tested.
Technical Specifications
Section titled “Technical Specifications”| Parameter | Value | Unit | Context |
|---|---|---|---|
| Device Technology | GaN HEMT | N/A | Grown on 500 µm Diamond Substrate |
| Frequency Range | 0.1 to 40 | GHz | RF Characterization |
| Gate Length (L) | 0.25 | µm | Device Specification |
| Gate Finger Width (Wg) | 125 | µm | Device Specification (4-finger device) |
| Barrier Layer Thickness | 20 | nm | AlGaN |
| VGS Operating Range | -3 to 0 | V | Gate-Source Voltage |
| VDS Operating Range | 0 to 30 | V | Drain-Source Voltage |
| Minimum S-Parameter Error (TSA Hybrid) | 0.060343 | % | Cold-FET Pinch-off (VGS = -3 V, VDS = 0 V) |
| Transconductance (Gm) | 251.86 | mS | VGS = -1 V, VDS = 10 V (TSA Model) |
| Output Conductance (Gds) | 6.74 | mS | VGS = -1 V, VDS = 10 V (TSA Model) |
| Execution Time (SBS) | 7.03 | s | Total systematic scanning process |
| Execution Time (TSA Hybrid) | 50.48 | s | 50 iterations (Fastest OA) |
| Execution Time (MPA Hybrid) | 100.35 | s | 50 iterations |
| Execution Time (POA Hybrid) | 99.33 | s | 50 iterations |
Key Methodologies
Section titled “Key Methodologies”The study utilized two primary methodologies—Scanning-Based Systematic (SBS) and Optimization Algorithm (OA)-Based Hybrid—to extract the 16 parameters of the SSECM.
- Cold-FET and Unbiased Measurements: S-parameters were measured across 0.1 GHz to 40 GHz at two key conditions: Cold-FET pinch-off (VGS = -3 V, VDS = 0 V) for capacitance extraction, and unbiased (VGS = 0 V, VDS = 0 V) for parasitic inductance and resistance extraction.
- Total Capacitance Extraction: Total capacitances (Cgst, Cgdt, Cdst) were calculated from the imaginary parts of the Y-parameters (Im[Yij]) using simple linear regression analysis at pinch-off.
- Scanning-Based Systematic (SBS) Extraction:
- Parasitic pad capacitances (Cpga, Cpgi) were determined via a systematic search (scanning) across incremental values.
- The method assumes Cpga = Cpda and Cds = 0 at pinch-off to simplify the calculation of Cgs and Cpdi.
- Parasitic inductances (Lg, Ls, Ld) and resistances (Rg, Rs, Rd) were extracted from the slopes of the Z-parameters derived from unbiased measurements.
- OA-Based Hybrid Extraction (MPA, POA, TSA):
- The initial values and bounds for all 16 SSECM parameters were set using results from the direct extraction method.
- Optimization algorithms (MPA, POA, TSA) were initialized with hyperparameters (50 iterations, 2500 search agents) and reliability conditions to filter non-physical values.
- The OA iteratively minimized the objective function (Eab), which quantifies the weighted error between measured and simulated S-parameters across the full frequency range.
- The intrinsic elements (Gm, Gds, τ, Cgs, Cgd, Cds, Ri, Rgd) were calculated following the de-embedding of the extrinsic elements determined by the OA.
- Model Validation: The final SSECMs were validated across multiple active bias points (e.g., VGS = -1 V, VDS = 10 V) to confirm the frequency-independent behavior of the intrinsic elements and the model’s ability to replicate complex device physics.
Commercial Applications
Section titled “Commercial Applications”The accurate and reliable small-signal modeling of GaN-on-Diamond HEMTs is foundational for designing high-performance microwave and millimeter-wave circuits, supporting the following industries and applications:
- 5G and 6G Wireless Communication: Enabling the design of high-efficiency, wide-bandwidth Radio Frequency Power Amplifiers (RFPAs) required for next-generation base stations and massive MIMO systems.
- High Power RF Systems: GaN-on-Diamond technology offers superior thermal management, making these devices ideal for high-power density applications where self-heating is critical.
- Radar and Defense Systems: Used in high-frequency, high-power transmitters and receivers for advanced radar, electronic warfare, and satellite communication systems.
- Millimeter Wave (mmWave) Technology: The validated 40 GHz frequency range directly supports the development of high-speed data links and short-range communication systems operating in the mmWave spectrum.
- Internet of Things (IoT) Infrastructure: Providing reliable, high-frequency components for complex IoT network backbones and high-throughput gateways.
View Original Abstract
This article presents accurate, efficient and reliable small-signal model parameter extraction approaches applied to Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Firstly, a scanning-based systematic model parameter extraction methodology is developed. Then, newly reported Optimization Algorithms (OAs) namely Marine Predators Algorithm (MPA), Pelican Optimization Algorithm (POA) and Tunicate Swarm Algorithm (TSA) in combination with direct extraction method are utilized to develop hybrid model parameter extraction methodologies. Lastly, both the scanning-based systematic and OA-based hybrid modelling procedures are thoroughly validated and demonstrated on a GaN HEMT grown on diamond substrate to identify their pros and cons in distinct application settings. Moreover, reliability, accuracy, convergence behavior, complexity and execution time of MPA-, POA- and TSA-based hybrid extraction procedures are also discussed. We found that both classes of the approaches are able to produce an excellent agreement between the measured and modelled S-parameters for a wide frequency range up to 40 GHz. However, OA-based hybrid modelling procedures are more physically relevant.