Mechanism and Impact of Bipolar Current Voltage Asymmetry in Computational Phase-Change Memory.

Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow. Using phase-change memory (PCM) as a model system, it is shown that this "current-voltage" non-ideality arises both from the material and geometrical properties of the devices. Further, we discuss the detrimental effects of such bipolar asymmetry on in-memory matrix-vector multiply (MVM) operations and provide a scheme to compensate for it.

Author Correction: Analog Coding in Emerging Memory Systems.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analog Coding in Emerging Memory Systems.

Exponential growth in data generation and large-scale data science has created an unprecedented need for inexpensive, low-power, low-latency, high-density information storage. This need has motivated significant research into multi-level memory devices that are capable of storing multiple bits of information per device. The memory state of these devices is intrinsically analog. Furthermore, much of the data they will store, along with the subsequent operations on the majority of this data, are all intrinsically analog-valued. Ironically though, in the current storage paradigm, both the devices and data are quantized for use with digital systems and digital error-correcting codes. Here, we recast the storage problem as a communication problem. This then allows us to use ideas from analog coding and show, using phase change memory as a prototypical multi-level storage technology, that analog-valued emerging memory devices can achieve higher capacities when paired with analog codes. Further, we show that storing analog signals directly through joint coding can achieve low distortion with reduced coding complexity. Specifically, by jointly optimizing for signal statistics, device statistics, and a distortion metric, we demonstrate that single-symbol analog codings can perform comparably to digital codings with asymptotically large code lengths. These results show that end-to-end analog memory systems have the potential to not only reach higher storage capacities than discrete systems but also to significantly lower coding complexity, leading to faster and more energy efficient data storage.

Self-Healing of a Confined Phase Change Memory Device with a Metallic Surfactant Layer.

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.

Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array.

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.