Title :
An inverter-based neural amplifier for neural spike detection
Author :
Sungho Kim ; Frey, Urs
Author_Institution :
Frey Initiative Res. Unit, RIKEN Quantitative Biol. Center, Kobe, Japan
Abstract :
An inverter is employed as an analog amplifier for realizing a front-end amplifier for neural spike detection. The amplifier operates in two phases, a biasing and an amplification phase. A high-pass filter is realized by repetitively resetting the neural amplifier and the high-pass cutoff frequency can be adjusted by the resetting frequency. The neural amplifier is designed in a 0.18μm 1P5M CMOS process and post-layout periodic small-signal and periodic noise simulations using SpectreRF were carried out. The neural amplifier achieves an input-referred noise of 3.29μVRMS from 6Hz to 10kHz, a power consumption of 3.2μW from a 0.8V power supply, and the efficiency factors of noise and power are 2.53 and 5.12 at a reset switching frequency of 10Hz, respectively.
Keywords :
CMOS analogue integrated circuits; amplifiers; high-pass filters; integrated circuit noise; neural chips; power consumption; CMOS process; SpectreRF; amplification phase; analog amplifier; biasing phase; efficiency factors; frequency 10 Hz; front-end amplifier; high-pass cutoff frequency; high-pass filter; input-referred noise; inverter-based neural amplifier; neural spike detection; periodic noise simulations; post-layout periodic small-signal simulation; power 3.2 muW; power consumption; power supply; reset switching frequency; resetting frequency; size 0.18 mum; voltage 0.8 V; inverter-based amplifier; neural amplifier; neural spike detection;
Conference_Titel :
Very Large Scale Integration (VLSI-SoC), 2013 IFIP/IEEE 21st International Conference on
Conference_Location :
Istanbul
DOI :
10.1109/VLSI-SoC.2013.6673243
