**6. Materials and devices**

The production and characterisation of materials for neuromorphic systems has been one of the major areas of advancement in neuromorphic computing in recent years. We want to highlight the variety of innovative nanoscale devices and materials that the materials science community is developing and characterising for neuromorphic systems.

Two typical nano-scale devices that have been constructed with various materials that can result in various behaviours are atomic switches and CBRAM. A survey of many atomic switch types for neuromorphic devices is given in [37], but common atomic switch materials include Ag2S [38–40], Cu2S [41], Ta2O5 [42], and WO3-x [43]. Under various conditions, many atomic switch materials can display various switching behaviours. As a result, the atomic switch's behaviour can be controlled by the material choice, which is likely to vary on the application. CBRAM has been implemented using GeS2/Ag [44–50], HfO2/GeS2 [51], Cu/Ti/Al2O3 [52], Ag/Ge0.3Se0.7 [53–55], Ag2S [56–58] and Cu/SiO2 [54]. Similar to atomic switches, the material chosen affects the stability and dependability of the device as well as the switching behaviour of CBRAM devices,

Memristors can be used in a wide number of ways. Transition metal-oxide-based memristor systems may be the most widely used (TMOs). Many different types of materials are utilised to make metal-oxide memristors, including HfOx [59–67], TiOx [68–73], WOx [74–78], SiOx [79, 80], TaOx/TiOx [81, 82], NiOx [83–85], TaOx [86–88], FeOx [89], AlOx [90, 91], TaOx/TiOx [81, 82], HfOx/ZnOx [92], and PCMO [93–98]. The quantity and sorts of resistance states that can be created by various metal oxide memristor types determine the range of weight values that can be stored on the memristor. They also differ in terms of their capacities for endurance, stability, and dependability.

There have also been other more suggested materials for memristors. For instance, spin-based magnetic tunnel junction memristors based on MgO have been suggested for implementations of both synapses and neurons [99], though it has been highlighted that they have a restricted range of resistance levels, making them less suitable to store synaptic weights [88]. Synapses have also been implemented using chalcogenide memristors [100–102]. One of the justifications provided for doing so is the chalcogenide-based memristor's ultra-fast switching speeds, which allow for processes like STDP to take place at nanosecond scale [97]. Polymer-based memristors have been used because of their inexpensive cost and adjustable performance [68, 103–111]. There have also been suggestions for organic memristors, which include organic polymers [69, 112–123].

### *Neuromorphic Computing between Reality and Future Needs DOI: http://dx.doi.org/10.5772/intechopen.110097*

Ferroelectric materials have been taken into consideration for the construction of analogue memory for synaptic weights [124–129], and synaptic devices [130–133], including those based on ferroelectric memristors [134–136]. Their main area of investigation has been three-terminal synaptic devices as opposed other implementations that may be two-terminal. Three-terminal synaptic devices do not need additional circuitry to implement learning processes like STDP because they can realise them in the device itself [130, 135].

Neuromorphic systems have more recently incorporated graphene to produce more compact circuits. It has been used in neuromorphic implementations as well as full synapse implementations [137, 138] for transistors [139–141] and resistors [142].

The carbon nanotube is another material under consideration for various neuromorphic applications. A variety of neuromorphic components, including dendrites on neurons [143–147], synapses [148–165], and spiking neurons [166–169], have been proposed to use carbon nanotubes. Carbon nanotubes have been used because they can provide the scale (number of neurons and synapses) and density (in terms of synapses) of neuromorphic systems that may be necessary for replicating or imitating biological neural systems. They have also been used to interface with living tissue proving that carbon-nanotube based devices may be helpful in prosthetic applications of neuromorphic systems [155].

Synaptic transistors with different architectures, such as silicon-based ones [170, 171], and oxide-based ones [172–184], have also been produced for neuromorphic applications. Synapses and other neuromorphic components have been built using organic electrochemical transistors [185–190] and organic nanoparticle transistors [191–194]. Organic transistors are being ordered for similar reasons as organic memristors: low processing costs and versatility. Additionally, they are ideally suited for the development of brain-machine interfaces as well as chemical or biological sensors [185]. It's interesting to note that various teams are working to create transistors within polymer-based membranes that can be applied to neuromorphic applications such biosensors [195–199].

Recently, there has been an increased interest in nonvolatile memory (NVM) technologies for neuromorphic computing, beyond their potential as DRAM replacement or as hybrid memory in shared memory systems. For neuromorphic architectures, nonvolatile devices offer a wide range of great qualities, including as memory retention, analogue behaviour, high integration density, faster read and program speeds, high energy efficiency, and programming voltages that are compatible with CMOS electronics. So we focus here on emerging materials for NVM devices:

### **6.1 Polymer**

Polymers are long-chain molecules with repetitive units. They fall within the category of one-dimensional materials with features including biocompatibility, chemical sensitivity, and mechanical flexibility. You can use organic or inorganic materials to create polymers. A flexible wearable memristor is designed with ammonium polyphosphate (APP) in a stack of Au/APP/ITO [201]. The I-V characteristics suggested that the bidirectional voltage sweeps' memristive behaviour was caused by ion movement in the APP. Even under harsh humid, temperature, or radiation settings, the proposed structure has demonstrated stable function. However, a lot of research has been done on organic semiconductors (OSC) for use in neuromorphic devices. The two-terminal OSCs can utilise filament formation, charge trapping, and ion migration to facilitate the integration into ReRAM, PCM, or FeRAM. Fuller et al.

present a polymer-based redox transistor combined with a CBRAM synaptic device [200] whose conductance change is initiated by reversible electrochemical reactions. The authors also show an array of 1024 × 1024 organic polymer memristors arranged for performance characteristics simulation. The main difficulties with OSCs are speed and density. Due to low mobilities of carriers and defects, OSC speed is impacted [201]. Due to OSCs' incompatibility issues with various solvents, the patterning of these devices through photolithography is limited, restricting the fabrication of dense networks.
