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SleepRunner: A 28-nm FDSOI ULP Cortex-M0 MCU With ULL SRAM and UFBR PVT Compensation for 2.6–3.6- μW/DMIPS 40–80-MHz Active Mode and 131-nW/kB Fully Retentive Deep-Sleep Mode
SleepRunner是一款28nm FDSOI超低功耗MCU,优化逻辑/内存/电源管理,适用于物联网长寿命需求。
28nm FDSOI, 40MHz@2.6µW/DMIPS, 131nW/kB深睡眠功耗
超低功耗FDSOI物联网背偏置SRAM
▸利用FDSOI技术的前向背偏置能力优化功耗
▸采用超低泄漏FBB兼容SRAM单元降低静态功耗
▸双环路数字统一频率/背偏置调节系统快速响应工艺温度变化
Abstract
Preventing device obsolescence in Internet-of- things (IoT) is mandatory for its massive deployment to be ecologically sustainable. This calls for ultralow-power (ULP) reprogrammable microcontroller units (MCUs) for long lifetime, yet with sufficient computing performance to extract the meaningful information from the sensed data before transmitting it to the cloud. In this article, we present the SleepRunner MCU with logic/memory/power management co-optimization for best exploitation of the forward back biasing (FBB) capability in fully-depleted silicon-on-insulator (FDSOI) technologies. For low active power, we use ultralow-voltage (ULV) low- V t logic with upsized gate length and asymmetric FBB, a ULP SRAM macro with low read-access energy and switched-capacitor voltage regulators (SCVRs) for ULV supply generation from a single I/O voltage. The custom ULP SRAM macro is based on an ultralow- leakage (ULL) FBB-compatible bitcell for low SRAM retention power. In addition, a dual-loop digital unified frequency/back- bias regulation (UFBR) system efficiently compensates process and temperature variations with short wakeup from the zero-back-bias deep-sleep mode. Performance is measured for a synthetic benchmark and biomedical inference applications. T h em e a s u r e d4 0 - M H z2 . 6 -µW/DMIPS (3.3 µW/MHz) active and 131-nW/kB deep-sleep power consumptions with CPU Manuscript received July 9, 2020; revised October 27, 2020 and January 4, 2021; accepted January 21, 2021. Date of