Low-power 60GHz industrial mmWave radar sensor in wafer scale package | YFF | 111 | -40 to 105

The

IWRL6432W mmWave Sensor device is an integrated single chip mmWave sensor based on

FMCW radar technology. The device is capable of operation in the 57GHz to 63.9GHz

band and is partitioned into mainly four power domains:

* RF/Analog Sub-System:

This block includes all the RF and Analog components required to transmit

and receive the RF signals.

* Front-End Controller

sub-System (FECSS): FECSS contains processor responsible for radar

front-end configuration, control, and calibration.

* Application Sub-System

(APPSS): APPSS is where the device implements a user programmable

ARM Cortex M4 allowing for custom control and automotive interface

applications. Top Sub-System (TOPSS) is part of the APPSS power domain and

contains the clocking and power management sub-blocks.

* Hardware Accelerator

(HWA): The HWA block supplements the APPSS by offloading common

radar processing such as FFT, Constant False Alarm rate (CFAR), scaling, and

compression.

The

IWRL6432W is specifically designed to have separate control for each of the

above-mentioned power domains to control the states (power ON or OFF) based on use

case requirements. The device also features the capability to exercise various

low-power states like sleep and deep sleep, where low-power sleep mode is achieved

by clock gating and by turning off the internal IP blocks of the device. The device

also provides the option of keeping some contents of the device, like Application

image or RF profile retained in such scenarios.

Additionally, the device is built with TI’s low power 45nm RF CMOS process and

enables unprecedented levels of integration in an extremely small form factor.

IWRL6432W is designed for low power, ultra-accurate radar systems in the industrial

(and personal electronics) space for applications such as building/factory

automation, commercial/residential security, personal electronics, presence/motion

detection, and gesture detection/recognition for human machine interfaces