Jihee Lee

14.2 A 502GOPS and 0.984mW dual-mode ADAS SoC with RNN-FIS engine for intention prediction in automotive black-box system

Advanced driver-assistance systems (ADAS) are being adopted in automobiles for forward-collision warning, advanced emergency braking, adaptive cruise control, and lane-keeping assistance. Recently, automotive black boxes are installed in cars for tracking accidents or theft. In this paper, a dual-mode ADAS SoC is proposed to support both high-performance ADAS functionality in driving-mode (d-mode) and an ultra-low-power black box in parking-mode (p-mode). By operating in p-mode, surveillance recording can be triggered intelligently with the help of our intention-prediction engine (IPE), instead of always-on recording to extend battery life and prevent discharge.

A 502-GOPS and 0.984-mW Dual-Mode Intelligent ADAS SoC With Real-Time Semiglobal Matching and Intention Prediction for Smart Automotive Black Box System

The advanced driver assistance system (ADAS) for adaptive cruise control and collision avoidance is strongly dependent upon the robust image recognition technology such as lane detection, vehicle/pedestrian detection, and traffic sign recognition. However, the conventional ADAS cannot realize more advanced collision evasion in real environments due to the absence of intelligent vehicle/pedestrian behavior analysis. Moreover, accurate distance estimation is essential in ADAS applications and semiglobal matching (SGM) is most widely adopted for high accuracy, but its system-on-chip (SoC) implementation is difficult due to the massive external memory bandwidth. In this paper, an ADAS SoC with behavior analysis with Artificial Intelligence functions and hardware implementation of SGM is proposed. The proposed SoC has dual-mode operations of high-performance operation for intelligent ADAS with real-time SGM in D-Mode (d-mode) and ultralow-power operation for black box system in parking-mode. It features: 1) task-level pipelined SGM processor to reduce external memory bandwidth by 85.8%; 2) region-of-interest generation processor to reduce 86.2% of computation; 3) mixed-mode intention prediction engine for dual-mode intelligence; and 4) dynamic voltage and frequency scaling control to save 36.2% of power in d-mode. The proposed ADAS processor achieves 862 GOPS/W energy efficiency and 31.4GOPS/mm2 area efficiency, which are 1.53× and 1.75× improvements than the state of the art, with 30 frames/s throughput under 720p stereo inputs.

27.2 A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring

There has been recent research into continuous monitoring of the quantitative anesthesia (ANES) depth level for safe surgery [1]. However, the current ANES depth monitoring approach, bispectral index (BIS) [3], uses only EEG from the frontal lobe, and it shows critical limitations in the monitoring of ANES depth such as signal distortion due to electrocautery, EMG and dried gel, and false response to the special types of anesthetic drugs [3]. Near-infrared spectroscopy (NIRS) is complementary to EEG [2], and can not only compensate for the distorted depth level, but also assess the effects of various anesthetic drugs. In spite of its importance, a unified ANES monitoring system using EEG/NIRS together has not been reported because NIRS signals have widely different dynamic ranges (10pA to 10nA), and also signal level variations from person to person and environment are not manageable without closed-loop control (CLC).

21.2 A 1.4mΩ-sensitivity 94dB-dynamic-range electrical impedance tomography SoC and 48-channel Hub SoC for 3D lung ventilation monitoring system

Electrical impedance tomography (EIT) has been studied to monitor lung ventilation because it is the only real-time lung imaging method without large equipment [1–2]. However, previous EIT systems just provided 2D cross-sectional image with limited spatial information of the lung and unneglectable volume detection error depending on the location of 2D EIT belt relative to the patients lung. In spite of its importance, the 3D-EIT has not been realized in lung monitoring because it has many design challenges such as noises incurred by complicated wiring, long cable length, wide variation in electrode contact and signal, and large personal-to-person impedance variation. In this paper, we present a portable 3D-EIT SoC for real-time lung ventilation monitoring with following 5 features: 1) The active electrodes (AEs) system to reduce coupling noise, 2) High output impedance current stimulator to inject stable current, 3) Impedance spectroscopy to enable both time-difference (TD) EIT and frequency-difference (FD) EIT, and to select an optimal frequency for TD-EIT, 4) Wide-dynamic range front-end circuit to detect variable ranges of signal with high-input impedance and CMRR, 5) Calibration to reduce the electrical characteristics variations of AEs.

Sticker-Type Hybrid Photoplethysmogram Monitoring System Integrating CMOS IC With Organic Optical Sensors

A sticker-type system with hybrid integration of CMOS IC and organic optical sensors is proposed to monitor photoplethysmogram (PPG) signals. To solve problems with the previous solely organic sensor-based works, CMOS IC is implemented in 180 nm technology under 5 V/1.5 V dual power supply. The silver-wire printed planar-fashionable circuit board (P-FCB) is used to connect the CMOS IC with organic sensors. The proposed hybrid system has the five following key features: 1) Power-efficient structure of organic sensor; 2) Integrated analog front-end and digital processor; 3) Degradation compensation scheme; 4) Large parasitic elements optimized design; and 5) Motion artifact rejection scheme. The sticker-type PPG monitoring system has mass of only 2g, including the batteries, and consumes only

A 48 μW, 8.88 × 10−3 W/W batteryless energy harvesting BCC identification system

233~\mu \text{W}

22.3 A 141µW sensor SoC on OLED/OPD substrate for SpO2/ExG monitoring sticker

to operate. The PPG signal could be acquired from various body parts (finger, wrist, and neck). The peripheral oxygen saturation level (SpO2 extraction results are verified by comparison with a commercial sensor device.

A 1.4-m

A BCC identification system which is fully compatible with previous radio frequency identification (RFID) systems is proposed in order to reduce power consumption, avoid security breaches, and enhance convenience via an intuitive interface. The BCC identification system is composed of a reader and a tag. The reader sends an RF wave to the tag, receives an identification code, and analyzes the received code. The tag harvests energy from the RF wave transmitted by the reader, and transfers the identification code to the reader. The energy harvester in the BCC identification tag increases power conversion efficiency (PCE) by up to 12% by adaptively changing the number of rectifier stages depending on input power. In addition, a transformer reusing an on-off keying (OOK) BCC transmitter is proposed to inform the reader of completion of energy harvesting of the tag by changing load impedance. As a result, a 48 μW, 8.88×10−3 W/W Figure-of-Merit (FoM) BCC identification system is implemented. This system can generate sufficient power in the tag with lower transmitted power from the reader compared to previous RFID systems.

\Omega

Recently, OLED is replacing semiconductor LEDs in many applications for broader light emission and low fabrication cost. In addition, Organic Photo Detector (OPD) and OLED can be fabricated on the same substrate with the same process and the OLED film itself can be used for the integration substrate of the other functional ICs. In this paper, we present a new SpO2/ExG monitoring sticker integrating OLED/OPD with CMOS SoC. It combines the superior optical properties of organic material with the advanced functional performance of CMOS SoC.