Memristive crossbar arrays for brain-inspired computing

While the speed-energy efficiency of traditional digital processors approach a plateau because of limitations in transistor scaling and the von Neumann architecture, computing systems augmented with emerging devices such as memristors offer an attractive solution. A memristor, also known as a resistance switch, is an electronic device whose internal resistance state is dependent on the history of the current and/or voltage it has experienced. With their working mechanisms based on ion migration, the switching dynamics and electrical behavior of memristors closely resemble those of biological synapses and neurons. Because of its small size and fast switching speed, a memristor consumes a small amount of energy to update the internal state (training). Built into large-scale crossbar arrays, memristors perform in-memory computing by utilizing physical laws, such as Ohm’s law for multiplication and Kirchhoff’s current law for accumulation. The current readout at all columns (inference) is finished simultaneously regardless of the array size, offering a huge parallelism and hence superior computing throughput. The ability to directly interface with analog signals from sensors, without analog/digital conversion, could further reduce the processing time and energy overhead. We developed memristive devices based on foundry compatible materials such as silicon oxide and halfnium oxide [1,2]. We demonstrated two nanometer scalability [3] and eight layer stackbility [4] with these devices. Furthermore, we integrated the halfnium oxide memristors into large analog crossbar arrays for analog signal and image processing [5], and the implemented multilayer memristor neural networks for machine learning applications [6,7]. The crossbar arrays were also used for other applications such as hardware security [8]. References: C. Li, et al. 3-Dimensional Crossbar Arrays of Self-rectifying Si/SiO2/Si Memristors , Nature Communications 8, 15666(2017). H. Jiang, et al. Sub-10 nm Ta Channel Responsible for Superior Performance of a HfO2 Memristor , Scientific Reports 6, 28525(2016). S. Pi, et al. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension , Nature Nanotechnology 14, 35-39(2019). P. Lin, et al. “Three-Dimensional Memristor Circuits as Complex Neural Networks”. Under review (2019). C. Li, et al. Analogue signal and image processing with large memristor crossbars , Nature Electronics 1, 52-59 (2018). C. Li, et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks , Nature Communications 9, 2385 (2018). C. Li, et al. Long short-term memory networks in memristor crossbar arrays , Nature Machine Intelligence 1, 49-57(2019). H. Jiang, et al. A provable key destruction scheme based on memristive crossbar arrays , Nature Electronics 1, 548-554(2018)

Three-dimensional hybrid circuits: the future of neuromorphic computing hardware

Recently there have been intensive research efforts to adopt emerging electronic devices for neuromorphic computing. However, the usage of these devices and arrays mainly was to implement parallel matrix multiplication in the two-dimensional (2D) space. This Perspective discusses the importance and implementation of three-dimensional (3D) hybrid circuits for neuromorphic computing, focusing on the integration density, data communication, and functional connectivity. We believe that 3D neuromorphic systems represent the future of artificial intelligence hardware with much-improved power efficiency and cognitive capabilities

A fully hardware-based memristive multilayer neural network

Memristive crossbar arrays promise substantial improvements in computing throughput and power efficiency through in-memory analog computing. Previous machine learning demonstrations with memristive arrays, however, relied on software or digital processors to implement some critical functionalities, leading to frequent analog/digital conversions and more complicated hardware that compromises the energy efficiency and computing parallelism. Here, we show that, by implementing the activation function of a neural network in analog hardware, analog signals can be transmitted to the next layer without unnecessary digital conversion, communication, and processing. We have designed and built compact rectified linear units, with which we constructed a two-layer perceptron using memristive crossbar arrays, and demonstrated a recognition accuracy of 93.63% for the Modified National Institute of Standard and Technology (MNIST) handwritten digits dataset. The fully hardware-based neural network reduces both the data shuttling and conversion, capable of delivering much higher computing throughput and power efficiency

A fast thermal-curing nanoimprint resist based on cationic polymerizable epoxysiloxane

We synthesized a series of epoxysiloxane oligomers with controllable viscosity and polarity and developed upon them a thermal-curable nanoimprint resist that was cross-linked in air at 110°C within 30 s if preexposed to UV light. The oligomers were designed and synthesized via hydrosilylation of 4-vinyl-cyclohexane-1,2-epoxide with poly(methylhydrosiloxane) with tunable viscosity, polarity, and cross-linking density. The resist exhibits excellent chemical and physical properties such as insensitivity toward oxygen, strong mechanical strength, and high etching resistance. Using this resist, nanoscale patterns of different geometries with feature sizes as small as 30 nm were fabricated via a nanoimprint process based on UV-assisted thermal curing. The curing time for the resist was on the order of 10 s at a moderate temperature with the help of UV light preexposure. This fast thermal curing speed was attributed to the large number of active cations generated upon UV exposure that facilitated the thermal polymerization process

A novel true random number generator based on a stochastic diffusive memristor

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator (TRNG) utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity and power consumption. The random bits generated by the diffusive memristor TRNG passed all 15 NIST randomness tests without any post-processing, a first for memristive-switching TRNGs. Based on nanoparticle dynamic simulation and analytical estimates, we attributed the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of Internet of Things (IoT)

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

The accumulation and extrusion of Ca2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses and represent a major advancement in hardware implementation of neuromorphic functionalities

Anatomy of Ag/Hafnia‐Based Selectors with 1010 Nonlinearity

Sneak path current is a significant remaining obstacle to the utilization of large crossbar arrays for non-volatile memories and other applications of memristors. A two-terminal selector device with an extremely large current-voltage nonlinearity and low leakage current could solve this problem. We present here a Ag/oxide-based threshold switching (TS) device with attractive features such as high current-voltage nonlinearity (~1010 ), steep turn-on slope (less than 1 mV/dec), low OFF-state leakage current (~10-14 A), fast turn ON/OFF speeds (108 cycles). The feasibility of using this selector with a typical memristor has been demonstrated by physically integrating them into a multilayered 1S1R cell. Structural analysis of the nanoscale crosspoint device suggests that elongation of a Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching of the device. Such mechanism has been quantitatively verified by the Ag nanoparticle dynamics simulation based on thermal diffusion assisted by bipolar electrode effect and interfacial energy minimization