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Single-chip low-power 57-GHz to 64-GHz industrial mmWave radar sensor

Texas Instruments

The

IWRL6432 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): HWA block supplements the APPSS by offloading common radar

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

compression.

IWRL6432

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.

IWRL6432 is designed for low power, self-monitored, 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

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