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A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

 

Electronic switches based on the migration of high-density point defects, or memristors, are poised to revolutionize post-digital electronics. Despite significant research, key mechanisms for filament formation and oxygen transport remain unresolved, hindering our ability to predict and design device properties. For example, experiments have achieved 10 orders of magnitude longer retention times than predicted by current models. Here, using electrical measurements, scanning probe microscopy, and first-principles calculations on tantalum oxide memristors, we reveal that the formation and stability of conductive filaments crucially depend on the thermodynamic stability of the amorphous oxygen-rich and oxygen-poor compounds, which undergo composition phase separation. Including the previously neglected effects of this amorphous phase separation reconciles unexplained discrepancies in retention and enables predictive design of key performance indicators such as retention stability. This result emphasizes non-ideal thermodynamic interactions as key design criteria in post-digital devices with defect densities substantially exceeding those of today’s covalent semiconductors. 

Analog compute‐in‐memory (CIM) systems are promising candidates for deep neural network (DNN) inference acceleration. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential security vulnerability is identified wherein an adversary can reconstruct the user's private input data from a power side‐channel attack even without knowledge of the stored DNN model. An attack approach using a generative adversarial network is developed to achieve high‐quality data reconstruction from power leakage measurements. The analyses show that the attack methodology is effective in reconstructing user input data from power leakage of the analog CIM accelerator, even at large noise levels and after countermeasures. To demonstrate the efficacy of the proposed approach, an example of CIM inference of U‐Net for brain tumor detection is attacked, and the original magnetic resonance imaging medical images can be successfully reconstructed even at a noise level of 20% standard deviation of the maximum power signal value. This study highlights a potential security vulnerability in emerging analog CIM accelerators and raises awareness of needed safety features to protect user privacy in such systems.

 

Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta₂O₅‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events.

 

Network features found in the brain may help implement more efficient and robust neural networks. Spiking neural networks (SNNs) process spikes in the spatiotemporal domain and can offer better energy efficiency than deep neural networks. However, most SNN implementations rely on simple point neurons that neglect the rich neuronal and dendritic dynamics. Herein, a bio‐inspired columnar learning network (CLN) structure that employs feedforward, lateral, and feedback connections to make robust classification with sparse data is proposed. CLN is inspired by the mammalian neocortex, comprising cortical columns each containing multiple minicolumns formed by interacting pyramidal neurons. A column continuously processes spatiotemporal signals from its sensor, while learning spatial and temporal correlations between features in different regions of an object along with the sensor's movement through sensorimotor interaction. CLN can be implemented using memristor crossbars with a local learning rule, spiking timing‐dependent plasticity (STDP), which can be natively obtained in second‐order memristors. CLN allows inputs from multiple sensors to be simultaneously processed by different columns, resulting in higher classification accuracy and better noise tolerance. Analysis of networks implemented on memristor crossbars shows that the system can operate at very low power and high throughput, with high accuracy and robustness to noise.