A Multimode 28 GHz CMOS Fully Differential Beamforming IC for Phased Array Transceivers

A 28 GHz fully differential eight-channel beamforming IC (BFIC) with multimode operations is implemented in 65 nm CMOS technology for use in phased array transceivers. The BFIC has an adjustable gain and phase control on each channel to achieve fine beam steering and beam pattern. The BFIC has eight differential beamforming channels each consisting of the two-stage bi-directional amplifier with a precise gain control circuit, a six-bit phase shifter, a three-bit digital step attenuator, and a tuning bit for amplitude and phase variation compensation. The Tx and Rx mode overall gains of the differential eight-channel BFIC are around 11 dB and 9 dB, respectively, at 27.0-29.5 GHz. The return losses of the Tx mode and Rx mode are >10 dB at 27.0-29.5 GHz. The maximum phase of 354° with a phase resolution of 5.6° and the maximum attenuation of 31 dB, including the gain control bits with an attenuation resolution of 1 dB, is achieved at 27.0-29.5 GHz. The root mean square (RMS) phase and amplitude errors are <3.2° and <0.6 dB at 27.0-29.5 GHz, respectively. The chip size is 3.0 × 3.5 mm², including pads, and Tx mode current consumption is 580 mA at 2.5 V supply voltage.

Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence

Cyberattack is one of the severe threats in the digital world as it encompasses everything related to personal information, health, finances, intellectual properties, and even national security. Password-based authentication is the most practiced authentication system, however, is vulnerable to several attacks such as dictionary attack, shoulder surfing attack, and guessing attack. Here, a new keystroke dynamics-based hybrid nanogenerator for biometric authentication and identification integrated with artificial intelligence (AI) is reported. Keystroke dynamics offer behavioral and contextual information that can distinguish and authorize the individuals based on their typing rhythms. The hybrid electromagnetic-triboelectric nanogenerators/sensors efficiently convert the keystroke mechanical energy into electrical signals, which are fed into an artificial neural network based AI system. The self-powered hybrid sensors-based biometric authentication system integrated with a neural network achieves an accuracy of 99% and offers a promising hybrid security layer against password vulnerability.