Low-Voltage Ultra-Low-Power Current Conveyor Based on Quasi-Floating Gate Transistors

The field of low-voltage low-power CMOS technology has grown rapidly in recent years; it is an essential prerequisite particularly for portable electronic equipment and implantable medical devices due to its influence on battery lifetime. Recently, significant improvements in implementing circuits working in the low-voltage low-power area have been achieved, but circuit designers face severe challenges when trying to improve or even maintain the circuit performance with reduced supply voltage. In this paper, a low-voltage ultra-low-power current conveyor second generation CCII based on quasi-floating gate transistors is presented. The proposed circuit operates at a very low supply voltage of only ±0.4 V with rail-to-rail voltage swing capability and a total quiescent power consumption of mere 9.5 µW. Further, the proposed circuit is not only able to process the AC signal as it's usual at quasi-floating gate transistors but also the DC which extends the applicability of the proposed circuit. In conclusion, an application example of the current-mode quadrature oscillator is presented. PSpice simulation results using the 0.18 µm TSMC CMOS technology are included to confirm the attractive properties of the proposed circuit

Inverter-Based Low-Voltage CCII- Design and Its Filter Application

This paper presents a negative type second-generation current conveyor (CCII-). It is based on an inverter-based low-voltage error amplifier, and a negative current mirror. The CCII- could be operated in a very low supply voltage such as ±0.5V. The proposed CCII- has wide input voltage range (±0.24V), wide output voltage (±0.24V) and wide output current range (±24mA). The proposed CCII- has no on-chip capacitors, so it can be designed with standard CMOS digital processes. Moreover, the architecture of the proposed circuit without cascoded MOSFET transistors is easily designed and suitable for low-voltage operation. The proposed CCII- has been fabricated in TSMC 0.18μm CMOS processes and it occupies 1189.91 x 1178.43μm2 (include PADs). It can also be validated by low voltage CCII filters

Fully integrated CMOS power amplifier design using the distributed active-transformer architecture

A novel on-chip impedance matching and power-combining method, the distributed active transformer is presented. It combines several low-voltage push-pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50-Ω match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any off-chip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50-Ω input and output matching has been fabricated using 0.35-μm CMOS transistors. It achieves a power added efficiency (PAE) of 41 % at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time

A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning

Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18 $\mu$m CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01 mm$^2$ and has an energy-efficiency of 9.3 pJ$/$spike$/$synapse

A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in International Joint Conference on Neural Networks (IJCNN) 201