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Robust Silicon-Controlled Rectifier With High-Holding Voltage for On-Chip Electrostatic Protection

Wenqiang Song, Ruibo Chen, Zhuang Tong, Fei Hou, Feibo Du, Zhiwei Liu, Hongxia Liu

2021IEEE Transactions on Electron Devices23 citationsDOI

Abstract

In this article, a robust high-holding voltage silicon-controlled rectifier (HHSCR) is implemented and realized in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BCD process for on-chip electrostatic discharge (ESD) protection. The proposed HHSCR was constructed by embedding a NMOSFET in the p-well of a modified lateral silicon-controlled rectifier (MLSCR). Benefiting from the shunting effect of the embedded NMOSFET path, the proposed HHSCR achieved a high-holding voltage with a relatively high robustness. Transmission line pulse (TLP) test results show that the proposed HHSCR exhibits a high-holding voltage of 11.6 V and a failure current (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {t2}}{)}$ </tex-math></inline-formula> of 1.59 A at a finger width of 50 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>. Furthermore, the internal physical mechanism of the proposed HHSCR was explored by technology computer-aided design (TCAD) simulation.

Topics & Concepts

NotationRobustness (evolution)Electrostatic dischargeVoltageSiliconChipEmbeddingRectifier (neural networks)Electrical engineeringElectronic engineeringMaterials scienceTopology (electrical circuits)EngineeringMathematicsComputer scienceOptoelectronicsArithmeticChemistryGeneMachine learningBiochemistryStochastic neural networkArtificial neural networkArtificial intelligenceRecurrent neural networkElectrostatic Discharge in ElectronicsIntegrated Circuits and Semiconductor Failure AnalysisAdvancements in Semiconductor Devices and Circuit Design