Halide perovskite materials have reached important milestones in the photovoltaic field, positioning them as realistic alternatives to conventional solar cells. However, unavoidable kinetic phenomena have represented a major concern for reliable steady-state performance assessment from standard current-voltage measurements. In particular, the dynamic hysteresis of current-voltage curves requires relatively long stabilization to achieve a credible figure for the power conversion efficiency. Hysteresis is caused by complex current transient phenomena that become active during staircase voltammetry. Here, we address the root of this problem. We pinpoint the dynamic characteristics behind the slow transient responses to strategically predict a minimum time delay and thus estimate the power conversion efficiency under steady-state conditions. Circuit-element analysis and impedance spectroscopy confirm our predictions based on an advanced transient study. Our results fundamentally explore the possibility of reducing data time acquisition in a reliable performance assessment, providing disruptive solutions and perspectives toward systematic production of photovoltaic perovskites.

The current interruption method allows a real-time estimation of the physiological state of tissues from their electrical properties.

Memristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights. Here, we develop a nonlinear model to explain the variation with time of the voltage and the resistance and compare it to experimental results on ionic–electronic halide perovskite memristors. We find separate experimental signatures of the capacitive discharge and inductive current increase. We show that the capacitor produces an increase step of the resistance due to the influence of the series resistance. In contrast, the inductor feature associated with inverted hysteresis causes a decrease of the resistance, as observed experimentally. The chemical inductor feature dominates the potentiation effect in which the conductivity increases with the voltage stimulus. Our results enable a quantitative characterization of highly nonlinear electronic devices using a combination of techniques such as time transient decays and impedance spectroscopy.

Perovskite memristors have emerged as leading contenders in brain-inspired neuromorphic electronics. Although these devices have been shown to accurately reproduce synaptic dynamics, they pose challenges for in-depth understanding of the underlying nonlinear phenomena. Potentiation effects on the electrical conductance of memristive devices have attracted increasing attention from the emerging neuromorphic community, demanding adequate interpretation. Here, we propose a detailed interpretation of the temporal dynamics of potentiation based on nonlinear electrical circuits that can be validated by impedance spectroscopy. The fundamental observation is that the current in a capacitor decreases with time; conversely, for an inductor, it increases with time. There is no electromagnetic effect in a halide perovskite memristor, but ionic-electronic coupling creates a chemical inductor effect that lies behind the potentiation property. Therefore, we show that beyond negative transients, the accumulation of mobile ions and the eventual penetration into the charge-transport layers constitute a bioelectrical memory feature that is the key to long-term synaptic enhancement. A quantitative dynamical electrical model formed by nonlinear differential equations explains the memory-based ionic effects to inductive phenomena associated with the slow and delayed currents, invisible during the ‘off mode’ of the presynaptic spike-based stimuli. Our work opens a new pathway for the rational development of material mimesis of neural communications across synapses, particularly the learning and memory functions in the human brain, through a Hodgkin–Huxley-style biophysical model.