We present our design exploration of reconfigurable Threshold Logic Gates (TLG) implemented using silver–chalcogenide memristive devices combined with CMOS circuits. Results from simulations and physical circuits are shown. A variety of linearly separable logic functions including AND, OR, NAND, NOR have been realized in discrete hardware using a single-layer TLG. The functionality can be changed between these operations by reprogramming the resistance of the memristive devices.

A third-order single-bit CT-ΔΣ modulator for generic biomedical applications is implemented in a 0.15 µm FDSOI CMOS process. The overall power efficiency is attained by employing a single-bit ΔΣ and a subthreshold FDSOI process. The loop-filter coefficients are determined using a systematic design centering approach by accounting for the integrator non-idealities. The single-bit CT-ΔΣ modulator consumes 110 µW power from a 1.5 V power supply when clocked at 6.144 MHz. The simulation results for the modulator exhibit a dynamic range of 94.4 dB and peak SNDR of 92.4 dB for 6 kHz signal bandwidth. The figure of merit (FoM) for the third-order, single-bit CT-ΔΣ modulator is 0.271 pJ/level.

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.