← 返回论文列表 📄 下载原文 PDF  ISSCC 2019 · 14.5
ISSCC 2019Session 14 · MACHINE LEARNING & DIGITAL LDO CIRCUITSAI / ML

A 0.6-to-1.1V Computationally Regulated Digital LDO with 2.79-Cycle Mean Settling Time and Autonomous Runtime Gain Tracking in 65nm CMOS

⚡ 本页包含 AI 生成的分析内容,仅供参考

📋 论文概要

该论文提出了一种计算调节的数字低压差稳压器(LDO),通过自主运行时增益跟踪技术,实现了2.79个时钟周期的平均建立时间,解决了传统数字LDO因采样反馈和PVT裕度导致的瞬态响应慢的问题。

💡 主要创新点

核心指标
2.79-cycle mean settling time
重要性
发表年份
ISSCC 2019

🏷 关键词

数字LDO计算调节快速瞬态响应增益跟踪

📄 原文摘要

University of Washington, Seattle, WA Low-Dropout Regulators (LDOs) play an important role in enabling fine-grained supply-voltage domains for energy-efficient SoC design [1]. Digital LDOs are of particular interest due to integration and scalability advantages, but their transient response is slowed down by intrinsic limitations in sampled feedback systems. Design margins to ensure stability across worst-case PVT conditions further degrade transient response. Meanwhile, voltage domains continue to shrink in size, thus mandating a faster LDO response to compensate for reduced available decoupling capacitance (decap). Recently reported non-linear control and event-driven architectures offer fast recovery times [2] [3]. However, non-linear approaches face the challenge of ensuring stable mode transitions under random load current (IL) conditions. Event-driven LDOs trigger logic to control MOS devices based on threshold

👥 作者与机构

Xun Sun, Akshat Boora, Wenbing Zhang, Venkata Rajesh Pamula, Visvesh Sathe

分类:AI / ML · 年份:ISSCC 2019