4.8 Article

Generation of Tunable Stochastic Sequences Using the Insulator-Metal Transition

Journal

NANO LETTERS
Volume 22, Issue 3, Pages 1251-1256

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.1c04404

Keywords

probabilistic computing; random number generation; p-bits; metal-insulator transition; resistive switching

Funding

  1. Swiss National Science Foundation [PZ00P2_185848, 200020179155]
  2. U.S. Office of Naval Research through the NICOP Grant [N62909-21-1-2028]
  3. Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) Energy Frontier Research Center (EFRC) - U.S. Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0019273]
  4. Norman Seiden Fellowship for Nanotechnology and Optoelectronics
  5. Israel Science Foundation [1031/21]

Ask authors/readers for more resources

Probabilistic computing is a new computing paradigm where data is represented by the probability of a metastable bit being in a particular state. Metal-insulator transitions can be used to generate random digital sequences, which can be controlled by inducing random metallization events in VO2. By using an electrical pump/probe protocol and a simple latch circuit, a true stochastic digital sequence can be achieved.
Probabilistic computing is a paradigm in which data are not represented by stable bits, but rather by the probability of a metastable bit to be in a particular state. The development of this technology has been hindered by the availability of hardware capable of generating stochastic and tunable sequences of 1s and 0s. The options are currently limited to complex CMOS circuitry and, recently, magnetic tunnel junctions. Here, we demonstrate that metal-insulator transitions can also be used for this purpose. We use an electrical pump/probe protocol and take advantage of the stochastic relaxation dynamics in VO2 to induce random metallization events. A simple latch circuit converts the metallization sequence into a random stream of is and Os. The resetting pulse in between probes decorrelates successive events, providing a true stochastic digital sequence.

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