The IWRL6432AOP mmWave Sensor device is an Antenna-on-Package (AOP) device that is an evolution within integrated single chip mmWave sensors that simplifies the board design. The device is based on FMCW radar technology. The device is capable of operation in the 57GHz to 63.5GHz 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 ARM Cortex M3, 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 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. IWRL6432AOP is specifically designed to have separate control for each of the above-mentioned power domains to control 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 some of 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. IWRL6432AOP 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