Title

Memristive crossbar arrays for brain-inspired computing

Conference Dates

May 19-23, 2019

Abstract

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:

  1. C. Li, et al. "3-Dimensional Crossbar Arrays of Self-rectifying Si/SiO2/Si Memristors", Nature Communications 8, 15666(2017).
  2. H. Jiang, et al. "Sub-10 nm Ta Channel Responsible for Superior Performance of a HfO2 Memristor", Scientific Reports 6, 28525(2016).
  3. S. Pi, et al. "Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension", Nature Nanotechnology 14, 35-39(2019).
  4. P. Lin, et al. “Three-Dimensional Memristor Circuits as Complex Neural Networks”. Under review (2019).
  5. C. Li, et al. "Analogue signal and image processing with large memristor crossbars", Nature Electronics 1, 52-59 (2018).
  6. C. Li, et al. "Efficient and self-adaptive in-situ learning in multilayer memristor neural networks", Nature Communications 9, 2385 (2018).
  7. C. Li, et al. "Long short-term memory networks in memristor crossbar arrays", Nature Machine Intelligence 1, 49-57(2019).
  8. H. Jiang, et al. "A provable key destruction scheme based on memristive crossbar arrays", Nature Electronics 1, 548-554(2018).

Additional Files
35 - New Applications I - Qiangfei Xia.pdf (247 kB)

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