A Compact Low-Power Chopper Low Noise Amplifier for High Density Neural Front-Ends.

This paper presents a low-power and area-efficient chopper-stabilized low noise amplifier (CS-LNA) for in-pixel neural recording systems. The proposed CS-LNA can be used in a multi-channel architecture, in which the chopper mixers of the LNA are exploited to provide the time division multiplexing (TDM) of several channels, while reducing the flicker noise and rejecting the Electrode DC Offset (EDO). A detailed noise analysis including the effect of the chopper stabilization on flicker noise, and a design flow to optimize the trade-off between input-referred noise and silicon area are presented, and utilized to design the LNA.

Software variability in service robotics.

Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments.

Low power switched-resistor band-pass filter for neural recording channels in 130nm CMOS.

In this work, we present a low-power 2 order band-pass filter for neural recording applications. The central frequency of the passband is set to 375Hz and the quality factor to 5 to properly process the neural signals related to the onset of epileptic seizure, and to strongly attenuate all the out of band biological signals and electrical disturbances. The biquad filter is based on a fully differential Tow Thomas architecture in which high-valued resistors are implemented through switched high-resistivity polysilicon resistors.