Emerging technologies have enabled efficient, high-speed realizations of ultra-dense crossbar arrays, driving the need for better insight in the transient operation of such systems. Previous work focused mostly on the effect of line resistance and its impact on steady-state response. In this paper, we develop a compact RC framework that includes the effects of parasitics. We use memristors as an exemplar device where interconnect parasitics (resistance, inductance, capacitance, and conductance) are extracted using ANSYS Q3D extractor for 5and 50-nm feature sizes. A model for the crossbar is presented, considering the stray and coupling capacitive parasitics of the crossbar. The derived model is based on state-space representation and provides more insight into the behavior of crossbar arrays containing either linear or nonlinear switching devices. The framework provides a closed-form solution to evaluate Elmore delay, as well as the steady-state response of the system. Signal delay is evaluated and compared for both grounded and floating interconnect inputs and verified against HSPICE, showing a perfect match.

The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge–Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around $180\times $ ( $20\times $ ) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than $300\times $ ( $240\times $ ) improvement on speed and $180\times $ ( $250\times $ ) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain $2\times $ training speedup over the Euler method, which makes it suitable for online training in real-time applications.

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is a dire need for devices to mimic the retina’s photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. Herein, we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina’s rod cells, paving the way for developing an efficient artificial retina network. The proposed device constitutes a hybrid nanocomposite of perovskites (methyl-ammonium lead bromide) and the ferroelectric terpolymer (polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene). A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light spectrum. The proposed photoreceptor mimics the spectral sensitivity curve of human photopic vision. The hybrid nanocomposite is stable in ambient air for 129 weeks, with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic polymer. The functionality of the proposed photoreceptor to recognize handwritten digits (MNIST) dataset using an unsupervised trained spiking neural network with 72.05% recognition accuracy is demonstrated. This demonstration proves the potential of the proposed sensor for neuromorphic vision applications.