Novel Optimization Method using Machine-learning for Device and Process Competitiveness of BCD Process

Kim, Junhyeok; Yoo, Jaehyun; Jung, Jae‐Hyun; Kim, Kwangtea; Bae, Jaehyun; Kim, Yoon-suk; kwon, Ohkyum; Kwon, Uihui; Kim, Daesin

doi:10.23919/sispad49475.2020.9241590

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [ 181 ] introduces an optimization method for Bipolar-CMOS-DMOS process development based on an Automatic Multi-objective Optimization solution and NN.…”

Section: Maintenancementioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

View full text Add to dashboard Cite

Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, maintenance—has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

show abstract

“…Ref. [ 181 ] introduces an optimization method for Bipolar-CMOS-DMOS process development based on an Automatic Multi-objective Optimization solution and NN.…”

Section: Maintenancementioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The laterally diffused metal oxide semiconductor (LDMOS) devices play increasingly implant roles in power management integrated circuits (PMICs). The PMICs fabricated with the bipolar-CMOS-DMOS (BCD) process own the advantages of highpower density, high speed, and easy integration [1,2]. Many researches have been published about LDMOS performance enhancement [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Process Optimization Method of the Mini-LOCOS Field Plate Profile for Improving Electrical Characteristics of LDMOS Device

Yu,

Shao,

Gao

et al. 2023

IET Circuits, Devices & Systems

View full text Add to dashboard Cite

In this work, the effects of the mini-local oxidation of silicon (LOCOS) field plate’s bottom physical profile on the devices’ breakdown performance are analyzed through technology computer-aided design simulations. It is indicated that the “abrupt” bottom profile could certainly do with an optimization. This paper introduces an effective process improvement method by etching bias power adjustment and time reduction. The upgradation of the field plate physical profile has been proved by transmission electron microscope cross-section analysis. The angle for the bottom surface of mini-LOCOS field plate θ2 is improved from 11.9° to 12.6°, and the thickness ratio of Hup/Hbottom (field plate oxide thickness for the upper and bottom, respectively) is increased from 71.8% to 76.6%. Finally, the optimized laterally diffused metal oxide semiconductor devices have been fabricated, and both figure of merit curves and safe operation area curves are measured. The specific on-resistance Ron,sp could achieve as low as 11.3 mΩ mm2, while breakdown voltage BVds,max arrives at 37.4 V, which is nearly 19.3% improved.

show abstract

Analysis and Optimization of an Analog MOSFET with a Slit Well at Channel Center Towards Higher Output Resistance

Fujii¹,

Yoo²,

Jeong³

et al. 2022

ESSDERC 2022 - IEEE 52nd European Solid-State Device Research Conference (ESSDERC)

View full text Add to dashboard Cite

Novel Optimization Method using Machine-learning for Device and Process Competitiveness of BCD Process

Cited by 4 publications

References 6 publications

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

A Process Optimization Method of the Mini-LOCOS Field Plate Profile for Improving Electrical Characteristics of LDMOS Device

Analysis and Optimization of an Analog MOSFET with a Slit Well at Channel Center Towards Higher Output Resistance

Contact Info

Product

Resources

About