Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Abstract

The biological brain has set a golden standard in computational efficiency, both due to its massive parallelism, and its ability to perform in-memory computing within the same ionic substrate. Ion-gated channels determine synaptic strength which is known to be a mechanism for memory storage, and the very same ions that pass through these gates encode data in the form of spikes. Communication, computation, and storage all occur within the same local medium. The brain's ability to perform in-memory processing within a unified ionic mechanism has driven many researchers to apply ion-driven non-volatile memories to emulate learning rules at the device-level. [1-12] The operating principles of resistive random access memory (RRAM) draw parallel with biological synapses. From a physical standpoint, the top electrode (TE) corresponds to a pre-synaptic terminal, the insulator layer acts as the substrate through which neurotransmitters are released, and the bottom electrode (BE) Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

Area-Type Electronic Bipolar Switching Al/TiO1.7/TiO2/Al Memory with Linear Potentiation and Depression Characteristics

“…Such a feature may be related to the non-CF-type switching mechanism, which barely has a self-strengthening effect. Such promising effects have hardly been acquired from two-terminal devices, except for Li-containing materials, 39 which have very low compatibility with the complementary metal-oxide-semiconductor device fabrication process. Therefore, this study also investigates the usefulness of the Al/TiO 1.7 /TiO 2 /Al sample for synaptic weight storage in an ANN structure.…”

Short-Term to Long-Term Plasticity Transition Behavior of Memristive Devices with Low Power Consumption via Facilitating Ionic Drift of Implanted Lithium

“…The STM-to-LTM transition occurred at 70 pJ with very low power consumption during an event, which was calculated by P/∆t, P = V•I, and ∆t = period of seven pulses [31,32]. The programming power consumption is remarkable in comparison to recent research results on memristive devices based on MIM [33,34], polymer [27,35,36], and two-dimensional materials [37,38]. After the transition from STM to LTM, the current level consistently remained at half the value of the input pulse's frequency (from 12 to 6 Mhz).…”

Reliable Resistive Switching and Synaptic Behaviors Based on a TiOx‐Doped N Memristor for Information Storage and Neuromorphic Computing

“…[ 2,8 ] As next‐generation storage technology, resistive random access memory (RRAM) has attracted wide attention in recent years because of its several advantages, including high density, low power consumption, and fast programming speed. [ 9–11 ] Furthermore, the ability to have multilevel resistance states in a single RRAM is responsible for the realization of synaptic applications that synapses require. [ 12–14 ] For instance, TiO x ‐based RRAM displays spike‐timing‐dependent plasticity (STDP) [ 12 ] and HfO x ‐based memory demonstrates learning−forgetting−relearning.…”