Ion-doped synaptic devices for neuromorphic applications

9 - Novel photoelectroactive memories and neuromorphic devices based on nanomaterials

We have investigated the analogue memory characteristics of an oxide-based resistive-switching device under an electrical pulse to mimic biological spike-timing-dependent plasticity (STDP) synapse characteristics. As a synaptic device, a Pr 0.7 Ca 0.3 MnO 3 based resistive — switching device exhibiting excellent analogue memory characteristics was used to control the synaptic weight by applying various pulse amplitudes and cycles. Furthermore, by adopting CMOS devices as neurons and TiN/PCMO devices as synapses, we implemented neuromorphic hardware that mimics associative memory characteristics in real time for the first time. Owing to their excellent scalability, resistive-switching devices, shows promise for future high-density neuromorphic applications.

As Moore's law approaches its limits, it is gradually harder to achieve the more and more complex tasks with computers with conventional von Neumann architecture. The hardware circuits for neuromorphic computing, with low power and high computing density, have attracted a lot of attention. Based on these requirements, how to choose the appropriate circuit components for neuromorphic computing becomes very important. Ambipolar synaptic devices with tunable and versatile behaviours, as a new type of circuit component, can mimic both excitatory and inhibitory behaviours, which has great potential in neuromorphic computing. In this chapter, we will introduce the definitions of ambipolar materials and synaptic devices. Then, the characteristics and applications of the synaptic devices will be discussed. The differences between conventional and ambipolar synaptic devices will also be compared. Finally, we will introduce the possibility of applying ambipolar synaptic devices in array to achieve more complex work.

The phenomenon of electric field mediated resistance switching in metal oxides is critical to nanoscale electronic devices ranging from conventional energy efficient MOSFETs to emerging memory devices as well as artificial neuron and synaptic devices. Such resistance transition can be facilitated by two main mechanisms: i) conduction of oxygen ions in non-stoichiometric compounds e .g. HfOx, AlOx. In this case the redox reaction of the oxide itself is manifested by oxygen anion transport, ii) the oxides serve as electrolyte dielectrics to conduct electrochemically active metal cations, such as Ag or Cu, forming the so-called electrochemical metallization memory (ECM) devices. So far inert and electrochemically active transition metals such as Pt and Cu/Ag respectively have been implemented to demonstrate resistive switching in Metal-Insulator-Metal (MIM) devices for application in steep slope transistors and as non-volatile memory and selector device. However, post transition metal e. g. Sn with lower cohesive energy as well as lower negative heat of formation of metal oxide offers viable alternative to Cu and Ag in ECM type devices. In this study, we fabricated asymmetric two terminal MIM structures in a cross-bar array with atomic layer deposited HfO2 thin film sandwiched between Pt and post-transition metal (Sn). We demonstrate reversible resistance switching in HfO2. Filamentary conduction by cations beyond a threshold voltage was verified by varying device areas. A recent report demonstrated coexistence of both oxygen anion and metal cation transport being responsible for resistance switching in a single oxide depending on the anode metal. We present a comparative investigation based on current-time (I-t) measurements to distinguish contributions from oxygen anions and metal cations in the resistance switching process. Using Sn anode we demonstrate bidirectional threshold switching as well as bipolar memory switching. We further evaluate viability of Sn anode in terms of switching speed in comparison with Cu and Ag. Furthermore, we discuss considerations for bidirectional threshold switching and bipolar memory switching with Sn anode substantiated with our ongoing molecular dynamic simulations.

This thesis presents a comprehensive study of tailored semiconductor devices that bridge conventional photodetection with neuromorphic photomemory applications through strategic engineering of response time characteristics. The fundamental insight underlying this work is that response time represents the critical parameter determining whether an optoelectronic device functions as a conventional photodetector or enables synaptic functionality for brain-inspired computing systems. The research demonstrates that the same physical mechanisms, such as photon absorption, carrier generation, charge transport, and recombination, can be optimized for dramatically different temporal characteristics, enabling devices that adapt their functionality based on operational requirements. Fast response times in the microsecond to millisecond range enable high-speed photodetection for telecommunications and sensing applications, while prolonged response times through persistent photoconductivity (PPC) spanning seconds to days enable the memory and learning functions essential for artificial synapses. The research contributions are organized into four interconnected studies that collectively demonstrate this dual functionality principle. Chapters 2 and 3 focus on optimizing devices for fast photodetection applications. Chapter 2 presents flexible germanium/titanium nitride (Ge/TiN) photodetectors that achieve enhanced near infrared responsivity through plasmonic enhancement and strain engineering, demonstrating how structural modifications can improve conventional photodetection performance while maintaining fast response characteristics. Chapter 3 investigates self powered silicon metal-semiconductor-metal (MSM) photodetectors utilizing asymmetric electrode configurations, achieving efficient photodetection without external bias through engineered Schottky barrier heights. Chapters 4 and 5 shift focus on exploiting extended response times for neuromorphic applications. Chapter 4 demonstrates silicon/indium gallium zinc oxide (Si/IGZO) photomemory devices with persistent photoconductivity exceeding nine days, achieved through substrate conductivity engineering that optimizes band alignment to trap photocarriers and prevent recombination. Chapter 5 presents transparent quartz/IGZO tracking devices that combine optical transparency with neuromorphic functionality, enabling covert trajectory monitoring through synaptic plasticity behaviors. The connection between these studies lies in the systematic exploration of how response time engineering enables the same semiconductor materials to serve two different purposes. The Ge/TiN and Si photodetectors in Chapters 2-3 are optimized for response times in the millisecond range, suitable for high-speed optical communication and imaging applications. In contrast, the IGZO-based devices in Chapters 4-5 exploit response times spanning minutes to days, enabling synaptic behaviors including PPF, learning-forgetting-relearning cycles, and long-term memory retention. All four device systems are based on MSM-type architectures that share the same fundamental band-to band excitation and carrier transport principles, and flexibility, self-powered operation, photomemory, and trajectory tracking are employed as representative manifestations of fast and slow response-time engineering within this common framework. The research provides new insights into the mechanisms controlling persistent photoconductivity, demonstrates practical strategies for substrate and interface engineering, and establishes design principles for next-generation neuromorphic optoelectronic systems. The significance of this research extends beyond individual device performance improvements to establish a fundamental framework for engineering semiconductor devices with tunable temporal characteristics. By demonstrating that the same materials and fabrication processes can yield devices optimized for either fast photodetection or extended photomemory functions, this work opens new possibilities for semiconductor devices with tunable temporal characteristics and design-level flexibility in their functional role.

From the Ground Up-Materials Science in Singapore.

Artificial sensory systems (ASS) are pivotal to next-generation extended reality technologies, now evolving into flexible platforms for comfortable wear and immersive user experiences, while ensuring high performance and operational reliability. To address these demands, metal-based nanoparticles (NPs), such as noble metal, oxide, and multi-elemental NPs, have been extensively incorporated into functional materials of sensory and synaptic devices due to their tunable optical, electrical, and chemical properties, enhancing sensory precision, stability, and environmental adaptability. However, traditional NP fabrication methods often involve complex processing, residual contaminants, and scalability issues, limiting their effectiveness in ASS applications. State-of-the-art laser ablation in liquids (LAL) presents a promising alternative, offering scalable production of surfactant-free NPs with customizable physicochemical properties, though their application in electronics remains underexplored. This review delves into the transformative potential of LAL-fabricated NPs in ASS, covering the fundamental mechanisms of LAL, the role of process parameters, the derivative strategies for size modulation, the diversity of metal-based NPs, their applications in sensory and synaptic devices, and the challenges and perspectives for meeting industrial standards. Bridging the gap between LAL and ASS is poised to revolutionize both industrial manufacturing and academic research by offering scalable solutions to overcome intrinsic tradeoffs between flexibility and performance, fostering innovations in human-centric, immersive electronics.

Synaptic elements capable of reversible multilevel analog switching have the potential to become key components in future neuromorphic computing technologies and exceed the practical limits of current digital technology. Artificial neural networks based on analog synapses could potentially deliver performance one step closer to the human brain which far surpasses the efficiency of current computing capabilities by many orders of magnitude. For example, this could be achieved by mapping the synaptic weights of deep neural net on the multi-bit device and performing the multiply-accumulate operation in-memory during learning or inferencing cycles [1]. While different types of 2-terminal emerging non-volatile memory, such as phase change memory (PCM) or resistive RAM (ReRAM), permit some degree of reversible analog tuneability, they exhibit significant stochasticity, and asymmetry which are detrimental to learning accuracy. In addition to precise and predictable control of the resistance state, decoupling the write and read cycle is a major challenge for the practical application of such devices. Memistor-based three-terminal devices allow decoupling the read and write steps, potentially permitting drastic increase in device operational precision and reliability. Electrochemical copper plating devices of this type for the purpose of “adaptive switching circuits” were conceived as early as the1960s in the ADALINE neuron element [2] and more recently as “atomic transistors” [3], however these devices utilized liquid electrolytes which are a big challenge for practical fabrication of an integrated chip. Other systems, such as lithium-based intercalation materials have been used for solid-state three-terminal synaptic devices and scaled to sub-micron dimensions [4], [5]. However. Li+ based systems are chemically reactive and sensitive to atmospheric exposure while also possessing a residual open circuit potential which opposes continued potentiation and creates challenges for voltage-driven neural networks. Here, we report a solid-state, lithium-free, Cu+-based 3-terminal synaptic device capable of reversible multilevel switching. Cu+ is driven in or out of a channel to create a conductance change and Cu+ ion transport is achieved through a solid electrolyte by writing pulses of only 100mV with near-zero open circuit potential. We analyze device performance, physical, and chemical characteristics, and provide insight into operation mechanisms and limitations. Exceptionally high ionic conductivity of 0.36S/cm (significantly higher than any reported solid-state lithium-based system) was measured in the electrolyte by impedance spectroscopy which is critical for high speed device operation. The first device prototypes were over 100 microns in size with very low dynamic range and channel resistance of only several hundred ohms, which is too low for practical applications. We demonstrate a path for increasing the dynamic range and channel resistance of our devices, as well as first steps towards transfer to sub-micron scale fabrication. Figure 1 Impedance spectrum of solid copper electrolyte with inset micrograph of completed device in measurement setup (a). Schematics of device structure and operation (b). Channel resistivity change during cycling measured between writing pulses (two consecutive cycles of 5, +100mV, and 5,-100mV pulses each). Reference: [1] T. Gokmen, Y Vlasov, Frontiers in Neuroscience 2016, 10, 333 [2] B. Wirdow. Technical Report No1553-2, Office of Naval Research 1960 [3] Xie et al. Beilstein J. Nanotechnol. 2017, 8, 530–538 [4] Fuller et al. Li-Ion Synaptic Transistor for Low Power Analog Computing. Adv. Mater. 2017, 29 [5] J.Tang et al, presented at IEEE IEDM 2018, 13.1 Figure 1

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.

In this letter, we report on the resistive switching originating from the rupture/rejuvenation of single-crystalline Cu filaments in a nitrogen-doped porous carbon-based memristive device Cu/CN0.15/Pt. Cu filaments are confined in conical nanopores in CN0.15 thin films. Dislocations exist in the Cu filaments, resulting in obvious crystal lattice distortions. The Cu/CN0.15/Pt device shows outstanding high temperature retention performance for both ON and OFF states, indicating that it is promising for resistance memory applications. Furthermore, continuous RESET (ON-to-OFF switching) and SET (OFF-to-ON switching) processes could be realized indicating the adaptive learning ability of Cu/CN0.15/Pt, which has potential applications in synaptic devices.

Organic synaptic transistors: Biocompatible neuromorphic devices for in-vivo applications

2D materials with low-temperature processing hold promise for electronic devices that augment conventional silicon technology. To meet this promise, devices should have capabilities not easily achieved with silicon technology, including planar fully-depleted silicon-on-insulator with substrate body-bias, or vertical finFETs with no body-bias capability. In this work, we fabricate and characterize a device [a double-gate MoS2 field-effect transistor (FET) with hexagonal boron nitride (h-BN) gate dielectrics and a multi-layer graphene floating gate (FG)] in multiple operating conditions to demonstrate logic, memory, and synaptic applications; a range of h-BN thicknesses is investigated for charge retention in the FG. In particular, we demonstrate this device as a (i) logic FET with adjustable VT by charges stored in the FG, (ii) digital flash memory with lower pass-through voltage to enable improved reliability, and (iii) synaptic device with decoupling of tunneling and gate dielectrics to achieve a symmetric program/erase conductance change. Overall, this versatile device, compatible to back-end-of-line integration, could readily augment silicon technology.

Digital storage and analog storage shine in different fields mainly due to their strong stability and high information density, respectively, while the freely selective digital and analog resistance switching applications are a matter of great concern. Here, a multi‐functional device integrated with analog and digital resistive switching behavior in a simple structure is reported, achieving functions of freely selective non‐volatile digital storage, artificial synaptic behavior based on ferroelectric polarization‐inducing analog resistive switching behavior, neural network computing, and reconfigurable logic functions. The non‐volatile digital memory exhibits a low operating voltage (≈1—2 V) and high retention stability (> 2000 s). The analog resistive switching behavior exhibits 128‐level conductance and excellent artificial synaptic performance with a large dynamic range (≈196), high linearity (≈0.84), and low power consumption. The modulation effect of band bending, an often underappreciated yet crucial element, is considered to analyze the mechanisms behind the optimized device performance, further providing support and expansion for the multifunctional applications of In2Se3 devices. Reconfigurable logic functions and an artificial neural network (ANN) with outline learning accuracy (≈89%) for handwritten digit recognition are achieved. This design provides new insights into the analysis of 2D ferroelectric devices and holds great promise for simplified and multi‐functional storage‐computing devices.

We present a multiscale modeling platform that exploits ab-initio calculation results and a material-related description of the most relevant defect-related phenomena in dieledtrics (charge trapping and transport, degradation and atomic species motion) to interpret and understand the electrical characteristics of OxRAM memory devices for non-volatile memories and neuromorphic applications. Simulation results provide a deep and quantitative understanding of the factors controlling device operation. The proposed multiscale modeling platform represents a powerful tool for investigating material properties and optimizing device performances and reliability.