InP-DHBT-on-BiCMOS Technology With $f_{T}/f_{\max}$ of 400/350 GHz for Heterogeneous Integrated Millimeter-Wave Sources

Kraemer, T.; Ostermay, Ina; Jensen, Thomas; Johansen, Tom K.; Schmueckle, F. J.; Thies, A.; Krozer, Viktor; Heinrich, W.; Krueger, Olaf; Traenkle, G.; Lisker, Marco; Trusch, A.; Kulse, P.; Tillack, Bernd

doi:10.1109/ted.2013.2264141

Cited by 25 publications

(16 citation statements)

References 20 publications

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“…Compared with GaAs heterojunction bipolar transistors (HBTs), InP HBTs are advantageous in terms of their DC gain, high frequency performance, power consumption and 1/f noise due to the inherent characteristics of the material in the system. After decades of development as a typical InP HBT device, InP DHBTs have gradually approached application frequency bands in the THz [1,2] range and are considered one of the most promising solutions for THz technology [3], making such transistors a topic of interest in the fields of high-speed, low-power millimeter wave, submillimeter and ultrafast devices and related integrated circuit technologies.…”

Section: Introductionmentioning

confidence: 99%

Extraction and verification of the small-signal model for InP DHBTs in the 0.2–325 GHz frequency range

Zhou

Sun

Liu

et al. 2018

IEICE Electron. Express

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This study presents the extraction and verification of a smallsignal model suitable for characterizing THz InP double heterojunction bipolar transistors (DHBTs). The π-type topology is adopted in the intrinsic model. Capacitances C cx and C ce are used to characterize the capacitive parasitics caused by the routing line connecting the collector terminal, base terminal and emitter terminal, respectively. The inductive parasitics introduced by the routing line are also considered. The initial values of the model parameters are extracted using a direct extraction method. The model and extraction method for the model parameters are verified by adopting an InP DHBT with 1 emitter finger and an emitter size of 0.5 µm × 5 µm. The simulation results correspond with the measured results in the frequency range from 200 MHz to 325 GHz.

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Section: Introductionmentioning

confidence: 99%

Extraction and verification of the small-signal model for InP DHBTs in the 0.2–325 GHz frequency range

Zhou

Sun

Liu

et al. 2018

IEICE Electron. Express

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“…5(b) [12][13][14]. Technology 1 can be CMOS-based, while chip lets can be fabricated on InP HBT, InP HEMT, or GaN HEMT.…”

Section: Advanced Inp Hemt Technologymentioning

confidence: 99%

Sub-millimeter wave InP technologies and integration techniques

Radisic

Leong

Scott

et al. 2015

2015 IEEE MTT-S International Microwave Symposium

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In this work, we describe recent advances in InP HEMT and InP HBT technologies that have led to circuits approaching 1 THz. At lower frequencies, these technologies have demonstrated record performance in terms of noise figure (NF), output power, or power-added efficiency (PAE). On the other hand, CMOS-based technologies are dominating semiconductor industry, because they offer high complexity, yield, and integration density. Recent advances in heterogeneous integration enable the combination of compound semiconductor device technologies with CMOS to create complex, compact, and low weight future systems.

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“…Compared with gallium arsenide (GaAs) HBTs, InP HBTs have several advantages, including: high dc gain, good high frequency performance, low power consumption and 1/ f noise, all of which originate from the inherent characteristics of the semiconductor materials involved. After decades of development, the InP DHBT has gradually found application in the THz frequency band, 1,2 and is considered as one of the most popular solutions for THz circuit technology 3 …”

Section: Introductionmentioning

confidence: 99%

Machine learning technique based extremely broadband small‐signal behavioral model forInP DHBTs

Cai

Liu

King

et al. 2020

Int J RF Microw Comput Aided Eng

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A novel modeling methodology for indium phosphide (InP) double heterojunction bipolar transistors (DHBTs) based on the theory of Bayesian inference, a well‐known method from the field of machine learning, is presented in this article. An extremely broadband small‐signal behavioral model, from 200 MHz to 325 GHz, is built, tested, and validated in this work, with excellent agreement obtained between the extracted model and the experimental data in the form of S‐parameters. A single finger InP DHBT device, with emitter size of 0.5 × 5 μm2 exhibiting an ft of over 550 GHz, is used in the verification example. Taking advantage of regression techniques based on machine learning concepts, the proposed black‐box behavioral model can more accurately predict the behavior of the device compared with the traditional equivalent circuit modeling method. Several sets of measured vs modeled data are shown, indicating the efficacy of the method.

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InP-DHBT-on-BiCMOS Technology With $f_{T}/f_{\max}$ of 400/350 GHz for Heterogeneous Integrated Millimeter-Wave Sources

Cited by 25 publications

References 20 publications

Extraction and verification of the small-signal model for InP DHBTs in the 0.2–325 GHz frequency range

Extraction and verification of the small-signal model for InP DHBTs in the 0.2–325 GHz frequency range

Sub-millimeter wave InP technologies and integration techniques

Machine learning technique based extremely broadband small‐signal behavioral model forInP DHBTs

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