The emulation of neuronal activity requires complex circuits that integrate multiple passive and active components, leading to a high circuit footprint. It is therefore apparent that developing a single device that can be used to emulate both synaptic and neuronal activity would allow less complexity and a much lower circuit footprint having significant impact on practical applications of neuromorphic systems. Herein, mixed halide perovskite-based transistors are demonstrated to exhibit volatile memristive behavior that responds to both light and electric fields, opening the path for optoelectronic control of neuron-like functions. Specifically, it is shown that by applying a low compliance current (ICC) during drain current–voltage (ID–VD) measurements, volatile memristive switching behavior is reported. A set of volatile ID–VD curves is presented under various gate biases, indicating a gate-enabled shift of the low-resistance state set voltage to higher values. The volatile nature of the device operated at low ICC allowed the demonstration of gate-tunable neuronal functions, including amplitude- and frequency-modulated spike firing. Furthermore, linear potentiation protocols and Leaky Integrate-and-Fire behavior is reported, while light pulses are shown to induce both photonic potentiation and graded optical neurons, opening the path for emulating neuron functions tunable by both light and electric fields.
Graphene flake dispersions can form conductive thin films via well-established, scalable deposition methods, such as spin-coating. These conductive graphene flake networks constitute sensing layers suitable for chemiresistive CMOS-compatible humidity sensors. Electrical noise is a parameter that affects sensor performance, and minimizing it requires thorough knowledge of the noise and its sources specific to the application. In this work, we present a phenomenological study of noise in resistive graphene sensors made from different graphene flake dispersions. We measured noise as a function of the graphene flake type, thickness of the graphene flake network, and sensor area. We conducted noise and sensitivity measurements to select the most suitable flake type for humidity sensing. We studied the influence of the temperature on the sensitivity and noise, and evaluated the humidity-dependent noise. Finally, a sensor operating mode is defined which enables humidity sensing well beyond the 1 % detection limit and with minimized resistance drift.

