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There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is expected to play a key role in this form of computing and, in particular, phase-change memory (PCM), arguably the most advanced emerging non-volatile memory technology. Given a lack of comprehensive understanding of the working principles of the brain, brain-inspired computing is likely to be realized in multiple levels of inspiration. In the first level of inspiration, the idea would be to build computing units where memory and processing co-exist in some form. Computational memory is an example where the physical attributes and the state dynamics of memory devices are exploited to perform certain computational tasks in the memory itself with very high areal and energy efficiency. In a second level of brain-inspired computing using PCM devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate the training of deep neural networks. PCM technology could also play a key role in the space of specialized computing substrates for spiking neural networks, and this can be viewed as the third level of brain-inspired computing using these devices.

Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.

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Understanding and possibly recovering from the failure mechanisms of phase change memories (PCMs) are critical to improving their cycle life. Extensive electrical testing and postfailure electron microscopy analysis have shown that stuck-set failure can be recovered. Here, self-healing of novel confined PCM devices is directly shown by controlling the electromigration of the phase change material at the nanoscale. In contrast to the current mushroom PCM, the confined PCM has a metallic surfactant layer, which enables effective Joule heating to control the phase change material even in the presence of a large void. In situ transmission electron microscope movies show that the voltage polarity controls the direction of electromigration of the phase change material, which can be used to fill nanoscale voids that form during programing. Surprisingly, a single voltage pulse can induce dramatic migration of antimony (Sb) due to high current density in the PCM device. Based on the finding, self-healing of a large void inside a confined PCM device with a metallic liner is demonstrated for the first time.