Recent advances in artificial neuromorphic applications based on perovskite composites

Huaxin Li ^a, Qingxiu Li ^a, Tao Sun ^a, Ye Zhou ^c and Su-Ting Han *^b
aInstitute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
^bDepartment of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, P. R. China. E-mail: suting.han@polyu.edu.hk
^cInstitute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China

First published on 20th July 2024

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Wider impact Perovskite materials have emerged as excellent candidates for artificial neuromorphic devices due to their exceptional physical, electronic, and optical properties. Nevertheless, their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and potential toxicity, have limited their widespread application in microelectronics. This review summarizes the approach of using materials engineering to integrate high-performance novel materials (including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials) with perovskites to leverage their complementary strengths, which have great potential to enhance ion migration, aligning energy levels, improving photoresponsivity, and achieving effective surface passivation. Such a rational composite strategy could lead to significant advancements in the development of perovskite composite devices including memristors, transistors, photodetectors, sensors, LEDs, and artificial neuromorphic systems. Through rational design and optimization of material combinations, the performance and functionality of optoelectronic devices and artificial neuromorphic computing devices can be further improved to provide a strong support for future scientific research and engineering applications.

1 Introduction

The groundbreaking architecture introduced by von Neumann in 1945, built upon principles outlined by Alan Turing in the 1930s, forms the foundation of contemporary computers. Despite its significance, the traditional von Neumann architecture is burdened by the physical separation of processing units and memory units, inevitably leading to elevated power consumption and inefficiency, which fail to meet the urgent demand for high computing power in the intelligence era.^1–4 In contrast, the human brain is a biocomputing system endowed with ultra-fast, ultra-low power consumption (at a mere ∼20 W) and the ability to process multiple complex computations in parallel.^5,6 With an event-driven mode of operation and highly parallelized computation, the human brain is far superior to modern computers in performing complex tasks such as recognition, acquisition, and decision-making. The human brain has ∼10¹¹ neurons and ∼10¹⁵ synapses, which form a complex network responsible for handling our senses, thinking, memory, emotions, languages, movements, and various other functions, enabling self-repair, self-learning, and self-evolution.^7,8 Learning from the human brain could address intelligent hardware development challenges. Inspired by bionics, neuromorphic computing systems have emerged as a field of intense study since the concept was coined by Mead at Caltech in the 1980s, promising to revolutionize our approach to computing.^9–11 Artificial neuromorphic computing chips based on conventional, complementary metal oxide semiconductor (CMOS) circuits represent a significant advancement. Yet, implementing individual neuronal and synaptic functions in CMOS circuits often requires multiple circuit modules consisting of transistors and capacitors. This inevitably increases the complexity of the architecture, excessive high-power consumption, diminished local fault-tolerant robustness, and constrained integration scale and chip density. Consequently, pursuing innovative neuromorphic devices is the optimal way to achieve brain-like computing capabilities in the future.

Massive efforts have been devoted to developing novel memory devices, including two- and three-terminal devices such as memristors, phase-change memories, ferroelectric memories, magnetic memories, floating-gate transistors, etc., to realize neuromorphic with neuron and synapse functions in individual devices.^4,12–16 For example, the memristor is a typical two-terminal, non-linear resistor with a memorization function, which features the unique ability to save information even after the power off, while being able to classify it into two morphologies, digital and analog, based on its physical phenomena.^17–19 The conductance state of the memristor analog morphology depends on the history voltage or current and has been demonstrated to model synaptic plasticity. Another three-terminal computing device for brains, the “synaptic transistor,” can simultaneously process and store data to simulate brain plasticity. The underlying logic of these neuromorphic devices is based on the intrinsic physical phenomena of the dielectric material in response to external stimulation (electrical and optical). In general, for ideal neuromorphic devices to serve as building blocks for neuromorphic systems, several details need to be considered, including low operating power consumption, nonlinearity, programmable symmetry, and retention.^20,21

To address these challenges, scientists have concentrated on identifying breakthrough materials with superior properties. Over the past decade, numerous efforts have been made in the exploration of novel materials for use in artificial neuromorphic devices as well as enhancing the application of traditional functional materials, including organics (polymers,^22,23 small organic molecules,^24,25etc.), two-dimensional (2D) materials (black phosphorus (BP),^26,27 hexagonal boron nitride (h-BN),²⁸ MoS₂,^29,30etc.), one-dimensional (1D) materials,³¹ oxides^32–35 and ferroelectric materials (α-In₂Se₃,^36,37 CuInP₂S₆,^38,39etc.), in their integration into artificial neuromorphic devices. For example, polymers are widely used in memristors and organic electrochemical transistors (OECTs) due to their chemical sensitivity and biocompatibility. In contrast, the chemical and electrochemical environments of polymer electrolytes facilitate plasticity in multi-gated coupled transistors as well as localized or even globally coupled activities that mimic biological neural networks.^40–43 Additionally, owing to superior optical functionality, tunable bandgap engineering, and compatibility with CMOS platforms, 2D van der Waals (vdW) heterojunctions are pioneering advancements in the development of neuromorphic sensors, encoders and processors and are also promising to enable functionally integrated all-in-one devices.^44–46 Typical physical and chemical properties of representative materials are summarized in Table 1. Nevertheless, due to the limitations associated with the intrinsic properties of these materials, such as stability, electrical conductivity, light absorption, and large-area fabrication, they often fail to satisfy more demanding applications. Thus, the quest for new materials and the exploration of their rational composite strategies to address the limitations of existing materials have become significant areas of research and ongoing challenges.

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A perovskite, initially identified as a mineral composed of CaTiO₃ in the Ural Mountains by the German mineralogist Gustav Rose, was subsequently named “perovskite” to honor the Russian scientist Count Lev A. Perovski.^47,48 The term “perovskite” has been adopted to classify materials with the same structure as CaTiO₃, characterized by the ABX₃ structure in which A and B sites represent cations of different radii, while the X position is allocated to the anion that forms a bond with the cation.⁴⁹ Perovskite materials have been utilized in solar cells since 2009, and numerous studies have been carried out to achieve an impressive increase in efficiency from an initial 3.8% to over 25% in just a decade, concurrently fueling enthusiasm for the deployment of perovskites in various other electronic domains.^50,51 Due to the outstanding photovoltaic conversion efficiency, long-term carrier dynamics, high carrier mobility, and tunable optical and electrical properties, perovskites are ideal candidates for optoelectronic and artificial neuromorphic devices. The high ion mobility and low-energy excited states provide perovskite materials with the potential to construct fault-tolerant neuronal networks.^52–54 Furthermore, the electrical conductivity of perovskites enables devices to respond quickly to external stimuli (electricity, light, magnetism, etc.), effectively mimicking the behavior of biological neurons. Additionally, ion transport can be tailored through structural modulation of perovskite materials to facilitate synapse formation and connectivity.^53,55,56 However, challenges remain in harnessing perovskites for artificial neuromorphic devices. The perovskite is limited by its solution-process method and crystallization process, necessitating that each process step is strictly and precisely controlled. Non-ideal crystallization, including surface defects, lattice defects, oxidation defects, and inhomogeneity, compromises the electrical performance and lifespan of devices. For instance, the defect density of MAPbI₃ thin films prepared by the spin-coating method is approximately 10¹⁷ cm⁻³, which inevitably and deeply impacts the energy band structure, carrier transport properties, and optoelectronic performance of perovskites.⁵⁷ Moreover, the well-documented instability of perovskites (e.g., sensitivity to humidity, oxygen, temperature, light, and circuit) poses a persistent challenge for researchers. As an ionic crystalline material, the perovskite is more delicate and less stable than crystalline silicon, facing issues such as oxidation, high-temperature intolerance, short lifespan, and high decay rate. These issues are the main barriers to commercialization and require additional investment to improve the stability and durability.^51,58,59

Recently, perovskites have received widespread attention for their comprehensiveness when combined with other materials. Thanks to materials engineering, a wide range of materials, such as organics, nanomaterials, oxide semiconductors, ferroelectrics, and novel CPMs, are utilized in the form of composites with perovskites to counterbalance the deficiencies or amplify their strengths. These synergistic effects manifest at the physical mechanism level as follows: (1) modulation ion migration. In artificial neuromorphic devices based on perovskite memristors, the intrinsic aspect relies on ion migration (electron/hole transport) in perovskite crystals and exhibits voltage–current hysteresis effects.⁶⁰ Unfortunately, the intrinsic low activation energy (E_a) of perovskites leads to a rapid return of ions to their initial positions, resulting in less pronounced hysteresis and lower synaptic plasticity weights. Fortunately, the coupling of ferroelectric materials with perovskites provides opportunities for the development of perovskite neuromorphic devices. The polarization reversal and polymorphic behavior of ferroelectric materials introduce tunable nonlinear properties to perovskite neuron-mimetic devices, thereby establishing them as ideal candidates for brain-like computation.⁶¹ (2) Energy level alignment. Lattice mismatches occur at the contact interface between perovskite materials and adjacent materials due to variations in lattice parameters and ionic radii, leading to distortions and mismatches in the energy band structure. However, the mismatched energy bands result in large energy band shifts at the interface, which induces high-energy trapping or injection of electrons and holes, increasing the non-radiative complexation of electron–hole pairs and generating significant carrier complexation rates.^62–64 These issues can lead to degradation of device performance, such as a slower switching speed and an unstable current output of the memristor and transistor. The alignment of energy band edges and the adjustment of energy levels can be achieved by rationally designing and controlling the energy band structure of oxide semiconductor materials, 2D materials and organic semiconductors. This approach aims to optimize the interfacial properties and charge transport characteristics of perovskite devices. For example, organic molecules or metal ions can modify the surface energy level of the material, and the oxide semiconductor or the 2D material layer can effectively suppress interfacial defects, reduce energy band shifts, and thus significantly improve the device performance. (3) Enhance photoresponsivity. Typical perovskite optoelectronic devices (such as LEDs, photodetectors, solar cells, etc.) demonstrate capabilities in terms of photoconductivity, the separation of photogenerated carriers (electrons and holes), and photoluminescence (PL). However, the intrinsic defects of perovskites may lead to increased recombination rates of electrons and holes, thereby diminishing the photogenerated charge separation effect. Additionally, perovskites are sensitive to the external environment. They are susceptible to moisture, oxidation, temperature, etc., leading to instability in material properties and subsequently diminishing the photo-responsive efficiency and external quantum efficiency (EQE). Moreover, the intrinsic band gap of perovskites constrains the optical response to the visible infrared region, while a wider spectral range can be achieved with the aid of other materials.^65–67 To surmount the limitation of the photo-response efficiency and EQE of single-component perovskites, the integration of organic semiconductors, oxide semiconductors, zero-dimensional (0D) materials, 2D materials, and CPMs with perovskites could significantly enhance the capabilities for the fabrication of multifunctional optoelectronic devices. (4) Surface passivation. In practice, perovskite materials often encounter issues related to their inherent defects or environmental factors (such as light, temperature, humidity, etc.), which lead to adverse effects like impeded charge migration and increased non-radiative re-combinations, adversely affecting the optoelectronic performance. Recent research suggests that the application of surface modification, interfacial engineering, or passivation layers can markedly reduce the intrinsic defect density and prolong the lifespan of perovskites. For instance, small organic molecules or inorganic substances can serve as capping agents or passivation layers, protecting the perovskite photosensitive layer from environmental factors such as humidity and oxides and thus significantly improving its stability.^68,69 In summary, integrating various materials with perovskites offers a novel approach for developing neuromorphic devices while concurrently enhancing the performance and efficiency of optoelectronic devices. With rational design and optimal composite structures, controlled ion migration, optimal interfacial energy levels, more efficient light absorption and charge separation, and superior stability can be achieved. Consequently, extensive investigation into the integration of diverse materials with perovskites for device applications holds great scientific significance and practical value. However, a comprehensive review of this topic has not been published.

In this review, we provide a comprehensive overview of the existing challenges of perovskite-based devices, specifically addressing and summarizing the research progress of neuromorphic devices based on composites of perovskites with different materials. Firstly, we overview various morphologies of perovskites, encompassing both three-dimensional and low-dimensional structures, and detail their chemical compositions in Section 2. Secondly, we present an in-depth perspective on the working mechanisms of perovskites in two- and three-terminal devices in Section 3. Thirdly, we summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and CPMs, to develop advanced devices such as memristors, transistors, photodetectors, sensors, LEDs, and artificial neuromorphic systems in Section 4. Finally, Section 5 addresses the challenges and future research directions for perovskites and the development of neuromorphic devices based on perovskite complexes. Through detailed review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites. The overview of this review article is illustrated in Fig. 1.

2 Overview of perovskites

Perovskites, a semiconductor material with exceptional physical properties like high mobility, long carrier lifetime, and low trap density, have received significant attention as ideal candidates for high-performance, low-energy memory and neuromorphic computing platforms. Perovskites can be categorized into three main types based on their composition: (1) inorganic perovskites, (2) organic perovskites, and (3) hybrid organic–inorganic perovskites (HOIPs). Inorganic perovskites are characterized by inorganic cations and anions, including compounds like lead titanium trioxide (PbTiO₃)⁷⁰ and barium titanium trioxide (BaTiO₃).⁷¹ Organic perovskites, entirely consisting of organic cations and anions, such as MDABCO–NH₄I₃ (where MDABCO refers to N-methyl-N′-diazabicyclo[2.2.2]octonium), form a unique class.^72,73 HOIPs, integrating both organic and inorganic cations and anions, are exemplified by MAPbI₃ (MA = methylammonium) or FAPbI₃ (FA = formamidinium). Furthermore, by manipulating the organic and inorganic compositions, we achieve modulation of structural dimensions, including 3D, 2D, 1D, and 0D. 1D perovskites, known for their large specific surface area and adjustable photovoltaic properties, are suited for flexible optoelectronic devices and sensors. The layered structure and quantum-limited domain effect endow 2D perovskites with exceptional photoelectric conversion efficiency and stability, highlighting their significance in solar cells, photoelectronics, catalysis, and photoelectric storage. Conversely, 3D perovskites are favored in photodetectors, LEDs, and photocatalysts due to their high structural stability and optimal photoelectric properties.

The 3D perovskite structure is predominantly defined by the ABX₃ configuration, where cations of varying radii fill A and B sites and the X site denotes the anion bonded to the cation; a schematic is shown in Fig. 2 (left panel). This specific arrangement facilitates the creation of coordination octahedra through corner-sharing, leading to the formation of the [BX₆]⁴⁻ unit. As early as the 1980s, MAPbX₃-type (X = Cl, Br or I) perovskites were synthesized, and in the 1990s, metal halide perovskite solar cells were systematically investigated, which established the foundation for perovskites in the field of optoelectronic devices.^74–76 3D perovskites are ideal candidates to replace silicon-based products in the construction of storage and neuromorphic devices due to their inherent physical properties and their prominence in semiconductors, including ferroelectricity, dielectricity, and resistive effects. The preparation methods for 3D perovskite thin films have been well-developed, with the most widely used technique being the solution-processed spin-coating method.⁷⁷ Nonetheless, the spin-coating method often suffers from issues like an inhomogeneous film layer, high defect density, and the challenge of achieving precise control of thickness, which affects the stability and efficiency of the memory device. Fortunately, physical vapor deposition (PVD)⁷⁸ methods, including pulsed laser deposition (PLD),⁷⁹ magnetron sputtering, and molecular beam epitaxy (MBE),⁸⁰ have also been developed to obtain high-quality and low-defect perovskite films.

To address the problem of increased density of highly defect densities in 3D perovskite films, low dimensional (LD) perovskites have been extensively studied as a potential solution in recent years. Notably, modifications in perovskite dimensions can be observed from both the molecular angle perspective and the morphological aspect, as shown in Fig. 2 (bottom panel). The former involves altering the material composition at the molecular level and the molecular structure, while the latter focuses on “slicing” 3D structural bodies to yield LD perovskites with unique morphologies while maintaining the chemical structure. Dimensionality reduction at the molecular level typically involves separating layers (lines or polyhedrons) from each other within bulk crystals by incorporating organic cations or ion clusters as new A sites, which enables the bulk crystals to exhibit the unique characteristics of the individual building blocks. This is achieved by chemically modifying or alloying the A, B, and X sites to enhance the structural stability while also reducing the material's structure from 3D to 2D, 1D, or even 0D.^81–83 In contrast to dimensionality reduction at the molecular level, the morphological approach to engineering perovskite materials achieves low-dimensional (LD) perovskites by ‘slicing’ their bulk structure. This method preserves the chemical composition and retains the crystalline architecture characteristic of 3D perovskites, which consists of corner-sharing octahedra. As depicted in Fig. 2 (top panel), various methods have been employed to thin these materials to nanoscale dimensions. Recently, innovative approaches such as chemical vapor deposition (CVD),⁸⁴ one-step solution self-assembly,⁸⁵ colloidal chemistry,⁸⁶ hot-injection,^87,88 template-assisted strategies,⁸⁹ and vapor phase methods⁹⁰ have been successfully utilized to reduce the thickness of 3D perovskites ABX₃. These techniques have led to the formation of LD nanostructures in diverse morphologies, including 2D nanosheets,⁹¹ 1D nanowires (NWs),⁹² and 0D quantum dots (QDs).^93,94

3 Fundamentals of perovskites for neuromorphic computing

In principle, an essential aspect of perovskites applicable in neuromorphic devices is its ability to undergo ion/vacancy migration when driven by an external field (such as an electric field and a light field), featuring a time delay for these ions/vacancies to recover to their initial positions after the removal of external fields. Perovskite neuromorphic devices typically include two-terminal (memristors) and three-terminal (field-effect transistors) devices. The two-terminal memristor, wherein resistive switching (RS) is realized by reversible and controllable charge storage, exhibits great potential in the field of neuromorphic computing due to its simple structure, low power consumption, tunable conductance, and rapid response speed. The three-terminal transistor exploits quantum tunneling to control electron storage in the perovskite channel layer through electric fields, providing an approach to the development of high-performance, high-density, and large-capacity perovskite storage devices. Typical two- and three-terminal device structure schematics and their electrical property curves are shown in Fig. 3(a) and (b). Moreover, the photosensitivity of perovskite materials coupled with programmable gate voltages enables tunable light-responsive characteristics. This section will elaborate on the common mechanisms and crucial parameters of perovskites as a dielectric layer in two- and three-terminal devices (memristors and field-effect transistors), emphasizing important parametric metrics of artificial synapses and neurons.

3.1 Main mechanism of memristors

Memristors are categorized as volatile and non-volatile. Volatile memristors demonstrate a reversible resistance state that is maintained only when power is supplied, making them well-suited for tasks requiring temporary data storage or dynamic processing. On the other hand, non-volatile memristors retain their resistance state even without power, ensuring long-term data retention and making them suitable for permanent memory applications in computing architectures.^95,96 The resistance switching mechanism of the memristor is controlled by physical/chemical processes within the resistive dielectric propelled by field effects, including ion/vacancy migration, charge trapping/de-trapping, redox reactions, interfacial effects, etc., thereby facilitating information storage and retention within the memristor device. In this section, we will summarize the common types of resistive mechanisms observed in perovskite memristors, encompassing the valence change mechanism (VCM), electrochemical metallization mechanism (ECM), mechanism regulated by interfacial barriers, space charge limited current (SCLC) mechanisms, and phase change mechanism (PCM), alongside an exposition of their specific mechanisms and operational principles, as schematically depicted in Fig. 3(c).

3.2 Main mechanism of field-effect transistors

3.3 Key performance metrics of artificial synapses and neurons

Artificial neuromorphic devices, emerging as a pivotal research avenue in the interdisciplinary field of neuroscience and artificial intelligence, hold a significant value for the realization of brain-like computation in artificial intelligence. Mimicking the functionalities of biological neurons and synapses, as illustrated in Fig. 4(a), these devices facilitate the construction of neural networks that resemble the human brain's intricate architecture.¹³⁰ Synaptic plasticity is one of the all-important features of artificial neuromorphic devices, rooted in the ability of synaptic connections in the device to change connection strengths or weights (physical parameters including current, conductivity, and resistivity) reflecting historical experience, and is fundamental to the realization of learning and memory functions. Artificial neuromorphic devices achieve synaptic plasticity by modeling the adjustability of parameters such as currents, voltages, and thresholds between pre- and post-synaptic neurons. Moreover, these devices can implement the electronic or optical methods to simulate synaptic activation and renewal processes, thus closely simulating the dynamics of synaptic plasticity observed in biological nervous systems.^131,132 The basic functions of neurons and their synapses in the neural system are summarized in Fig. 4(b). Post-synaptic currents (PSC) typically arise following external stimulation of the pre-synaptic membrane and involve excitatory and inhibitory post-synaptic currents (EPSC/IPSC). The phenomenon of synaptic plasticity is characterized by two primary forms: short-term synaptic plasticity and long-term synaptic plasticity. The short-term synaptic plasticity refers to a series of physical changes, including short-term potentiation/depression (STP/STD), elicited by a stimulus with a short duration (lasting only a few seconds to a few minutes). It is mainly shown as paired-pulse facilitation (PPF), paired-pulse depression (PPD), spike-number-dependent plasticity (SNDP), and spike-voltage-dependent plasticity (SVDP). Conversely, the long-term synaptic plasticity refers to the plasticity of neurons that lasts for a longer period (days or even years) after repeated stimulation, including long-term potentiation/depression (LTP/LTD). Typical long-term synaptic plasticity includes spike-rating-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) as an important tool for exploring interactions and neuronal connections’ adaptation to adjust input patterns and strength.

4 Perovskites and different materials composites in neuromorphic devices

4.1 Organic materials and perovskite complexes

An organic material is a broad term used to describe a wide range of substances that contain carbon atoms, and almost all living organisms on Earth are made up of carbon-based compounds. Owing to their intricate chemical structures, organic compounds are incredibly versatile. They can be modified in many ways to endow them with unique optical, electrical, thermal, and biological properties, making them well-suited for a diverse range of applications. For example, incorporating functional groups such as diazo groups, cinnamoyl, acrylates, etc., can modify the light-sensitive properties of the organics to make them single/multiple light-responsive functionalities.¹³³ Moreover, the wettability can also be modified by surface modification with hydrophilic/hydrophobic groups or surfactants.^134,135 Conjugated systems of organic materials significantly contributed to the development of organic semiconductors and delivered the prospect of future innovations in the field of organic electronics, paralleling the achievements of silicon in the semiconductor industry. Attributable to their unique optical and electrical properties as well as abundant synthesis space for chemical modification, organic semiconductor materials (including π-conjugated polymers, small-molecules, and composites) have established a strong position and significant commercial potential in the fields of organic light-emitting transistors (OLETs), organic light-emitting diodes (OLED), organic solar cells, and electrochromic devices.^136–138 For example, organic semiconductor materials such as polythiophene (P3HT) and phenyl-C₆₁-butyric acid methyl ester (PCBM) and other hybrid materials are utilized to assemble solar cells, emerging as a hotspot for research on new renewable energy sources.^139–142 Homogeneous small molecule semiconductor and polymer light-emitting materials in the field of OLEDs can achieve more colorful, high-definition image effects.^143,144 In the following, we will discuss the effects of different types of organics, including organic semiconductors (small-molecule organics and polymers) and organic salts coupled with perovskites, on enhancing the performance of optoelectronic devices.

4.2 Nanomaterials and perovskite complexes

Nanomaterials with their unique physical and chemical properties alongside highly programmable architecture structures are widely used in the fields of energy, optoelectronics, and biomedicine. For instance, 1D nanowires are instrumental in pushing forward the development of low-power, miniaturized memories, while 2D materials such as graphene, transition metal dihalides (TMDs), BP, and others are celebrated for their exceptional qualities in transistors and artificial synaptic devices.^165,166 Furthermore, nanoparticles hold potential in crafting high-density memory devices, including flash memory and memristors. Nonetheless, both individual nanomaterials and perovskites encounter specific challenges in certain applications. For example, the energy band structure and optical properties of nanomaterials might not meet the precise requirements of certain devices, whereas the perovskite is challenged by issues related to stability and photoelectric conversion efficiency. To address these challenges, compositing nanomaterials with perovskites has emerged as a highly promising research field. Here, we explore the different dimensions (including 2D, 1D and 0D) of nanomaterials combined with perovskite materials for their applications in electronic devices and neuromorphic computing.

4.3 Metal oxide semiconductors and perovskites

Oxide semiconductors include typical two-element oxides such as SiO_x, TiO_x, ZnO_x, CuO_x, HfO_x, NiO_x, etc., and three-element and multi-element oxides like SrTiO₃, SrZrO₃, SrRuO₃, etc., which are commonly used in non-volatile data memory devices.^19,131 Among them, two-element oxide semiconductors are highly regarded both in industrial and in academics for their simple structure, controlled components, high mobility, simplified preparation methods (e.g., magnetron sputtering, sol–gel, and plasma oxidation), and compatibility with CMOS backchannel integration.^211–213 Despite the practicality of metal oxide semiconductors, they are somewhat limited by challenges such as the higher forbidden bandwidth and dielectric constant and poor performance in light absorption and light emission, which restrict their suitability for optoelectronic devices. Additionally, achieving optimal substrate matching remains a challenge, impacting both the material crystal quality and the overall performance of devices.

There have been reports in the literature confirming the use of perovskite films alongside oxide semiconductors to enhance the optical and electrical properties of various devices. Depending on the vacuum level, the ideal n-type metal oxides exhibit compatibility with the energy levels of most perovskite layers, permitting efficient extraction of carriers and the inhibition of the recombination of electron–hole pairs.²¹² However, another challenge persists in the chemical instability found at the metal oxide/perovskite interface. The direct interaction between the perovskite and the metal oxide surface can lead to the deterioration of perovskite's structural integrity. A more refined approach should be employed to ensure that the metal oxides serve as passivation layers on the perovskite surface, enhancing stability while preserving the intrinsic properties of both materials and preventing undesirable chemical interactions at the interface. To address this problem, Zhang et al. constructed CsPbX₃/ZnO type II heterojunction nanocrystals (PZNCs) via an in situ growth strategy as shown in Fig. 10(a).²¹⁴ This approach effectively employs ZnO as a passivation layer for the CsPbX₃ perovskite to eliminate the trap states on its surface and grain boundaries, which achieved more than 500 hours (Fig. 10(b)) without significant fluorescence degradation under exposure to air and illumination conditioning as well as excellent fluorescence-converting LED performance (Fig. 10(c)). To modulate the photo-response spectrum of the phototransistor effectively, Shi's team designed a transistor incorporating a single-crystal Cs₂AgBiBr₆ perovskite and indium–gallium–zinc oxide (a-IGZO) thin-film heterojunction as the light-absorbing active layer as shown in Fig. 10(d).²¹⁵ The transistor exhibits a large memory window (the switching ratio is ∼10⁷ higher than under dark conditions) alongside capabilities for photoprogramming and electrical erasure characteristics under 455 nm and 365 nm light irradiation as shown in Fig. 10(e) (the intensity is 12.7 mW cm⁻²). The proposed mechanism indicates that upon light irradiation, the built-in electric field at the Cs₂AgBiBr₆/IGZO interface instigates the spontaneous migration of photogenerated electrons in Cs₂AgBiBr₆ towards IGZO. The interface energy band between IGZO and SiO₂ bends, culminating in electron accumulation within the channel (Fig. 10(f)). Moreover, Cs₂AgBiBr₆/IGZO phototransistors under double-pulse stimulation were able to efficiently simulate photosynaptic PPF plasticity, as well as STP and LTP plasticity under continuous light pulses (Fig. 10(g)–(j)). Finally, their pattern recognition EMNIST data set, which was simulated by artificial neural networks, achieved an accuracy of 83.8%.

Developing highly transparent and flexible optical devices based on perovskites is another significant challenge. Owing to the sensitivity and stability of the inorganic CsPbCl₃ perovskite to UV light, Yang et al.²¹⁶ adopted ITO/SnO₂/CsPbCl₃/TAPC/TAPC:MoO₃/MoO₃/Ag/MoO₃ (TAPC: 4,4′-cyclohexylidenebis[N,N-bis(4-methyl phenyl)benzenamine]) two-terminal devices to establish photonic artificial synapses as depicted in Fig. 10(k). High flexibility and high transmission were demonstrated on perylene, PET, and glass substrates as shown in Fig. 10(m). In this structure, CsPbCl₃ served as the UV light-absorbing layer, while SnO₂ and TAPC function as the electron-transport layer and hole-transport layer, respectively. The energy level alignment between SnO₂ and CsPbCl₃ heterostructures induces exciton segregation at the heterojunction interface as shown in Fig. 10(l), laying the foundation for optically mediated charge trapping in CsPbCl₃-based photonic artificial synapses. This system exhibits typical synaptic functions such as PPF, SDDP, SNDP, SFDP, and artificial neuromorphic properties such as STP and LTP under UV light (365 nm) with light intensities of 1.25–50 μW cm⁻². In contrast, under red light illumination at a wavelength of 665 nm, the above photon synapse phenomena were not observed, enabling the potential for image recognition and detecting different light colors as shown in Fig. 10(o). This work provides ideas for the development of multifunctional photonic synapses for intelligent recognition as well as transparent and flexible perovskite-based bionic smart devices.

4.4 Ferroelectric materials and perovskite heterojunctions

The ferroelectric material is a high-functioning insulator with a unique self-charging surface and memory capabilities. When subjected to an external electric field, these materials exhibit a unique ability to reconfigure their charge distribution, maintaining this new state even after the removal of the external stimulus (Fig. 11(a) and (b) display the structural re-arrangement of CuInP₂S₆ and WTe₂ in different polarization states, respectively).²¹⁷ The ferroelectric memory device takes advantage of the ferroelectric effect to achieve data storage and has emerged as a potential candidate for the next generation of data storage devices with low power consumption, long endurance, and high switching speed.²¹⁸ A well-known mechanism for the application of perovskites in artificial synapses is attributed to the diffusion and drift of ions/vacancies in perovskite films, which produce an intrinsic electric field opposite to the supplied electric field and hysteresis. However, once the external electric field is removed, these ions/vacancies revert to their original positions, leading to poorer synaptic plasticity. Benefited from the ferroelectric material poling and memory characteristics, a thoughtfully engineered ferroelectric/perovskite heterojunction is anticipated to revolutionize the modulation of the ion migration within the perovskite film and allow for the construction of storage device model with ultra-low-power, high-speed and high-reliability.

To effectively modulate non-volatile ion migration within metal halide perovskites, Jeong et al. developed a three-terminal transistor for the first time with a ferroelectric gate to modulate the conductance state of the perovskite channel (Fig. 11(c)).²¹⁹ By utilizing a ferroelectric gate of the polymer poly(vinylidene fluoride-co-tri fluoroethylene) [P(VDF-TrFE)], the transistor can maintain a remnant polarization, effectively immobilizing migrating ions in the perovskite (CsPbBr₃) and exhibit a non-volatile binary storage state. One aspect of the P(VDF-TrFE) under a negative gate voltage, the movable cations are attracted to the top surface and create a reliable upward polarization; conversely, a positive gate voltage reverses the polarization direction. This unique behavior results in the transistor possessing both memory characteristics and a stable on/off ratio of 1.9 × 10³ as shown in Fig. 11(d) and (e). Most importantly, the investigation revealed that perovskite transistors emulate biological synapses, such as LTP, LTP, PPF, STDP, etc., as shown in Fig. 11(f) and (g). These transistors have the potential to mimic biological synapses physically and offer insights into the identification of diverse ion–electron hybrid conductors in the artificial intelligence hardware. Furthermore, Gao et al.⁶¹ investigated P(VDF-TrFE) as a top-gate insulator to modulate ion mobility within a three-terminal MAPbI₃ memristor as shown in Fig. 11(h) and comprehensively analyzed the mechanisms of Schottky barriers, perovskite ion mobility, and ferroelectric polarization effect factors on both volatile and non-volatile operational modes.²²⁰ Initially, they observed that the MAPbI₃ memristor, when integrated with a non-poled ferroelectric layer, is affected by the Schottky barrier at the Au(−)/MAPbI₃ interface, exhibiting typical bipolar volatile properties with stable set voltages (3.44 V/−3.42 V) and cycle-to-cycle and cell-to-cell uniformity as shown in Fig. 11(i). Subsequently, both vertical and horizontal ion migration of MAPbI₃ under gate positive/negative polarization states (+20 V/−20 V for 60 s) can change the contact barrier at the Au(−)/MAPbI₃ interface, modulating the device conductance level (Fig. 11(j) and (k)). It was demonstrated that the polarization state accelerates the ion transport within the perovskite film and promotes the non-volatile resistive switching process. Finally, they ventured into the field of bio-inspired applications by using volatile/non-volatile properties in non-poled/poled modes to simulate the response of pain receptor neurons to stimulus-induced injury as shown in Fig. 11(l).

In conclusion, the integration of ferroelectric materials with perovskites provides an effective and practical idea for low-power, tunable, and high-speed storage devices. Despite the potential, several challenges remain to be addressed: (1) lowering the threshold voltage could potentially be achieved by adjusting the defects in perovskite thin films or reducing electrode distances with advanced photolithography; (2) constructing two-terminal perpendicular device structures facilitating alignment of the electric field with the direction of polarization, is posited to superior device performance; and (3) further investigation of the poling reversal mechanism on perovskite ion migration will provide clearer guidance for designing ferroelectric engineered devices.

4.5 Crystalline porous materials and perovskites

Emerging CPMs represent a novel category of lightweight, porous framework materials composed of organic and metallic linkage units via covalent connections or organic structure units via coordination bonds. These include metal–organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs), and zeolites. The intrinsic porous structure, molecular tunability, and facile chemical functionalization of CPMs have endowed their applications in a broad field, including catalysis, batteries, and energy, showcasing rapid development.^221–227 However, the limited electrical conductivity and charge transfer capability of CPMs hinder their widespread application in the field of electronic devices. To address this issue, strategic molecular engineering of CPMs or the incorporation of dopants, such as redox-active molecules, cations, and conductive polymers, enhance their conductivity. Research has been dedicated to advancing the application of CPMs in electronic devices and neuromorphic computing in the recent decade. For example, Li et al. effectively modulated the porosity of the COFs to induce the formation of uniform Ag CFs in RRAM devices. This modification enabled the achievement of multilevel memory behaviors and synaptic responses to alcohol chemical media, further facilitating the recognition of different concentrations and classes of alcohols via the KNN algorithm.²²⁸ The HOFs@Au-based memristor reported by Zhang et al. exhibits gradual conductance under continuous voltage scanning or pulsed algorithms, effectively simulating the synaptic functionality of biological neurons and constructing color coding algorithms.²²⁹ Despite these advancements, there have been few reports or studies on electronic devices (such as memristors, transistors, flash memory, sensors, etc.) employing composite media of perovskites and PCMs. Herein, we will elaborate on the potential directions of the development, applications, and dissection of the possible physical/chemical mechanisms of perovskites and PCM composites in electronics.

Although halide perovskites hold significant promise for important research in the areas of photovoltaic devices and photodetectors, they face significant challenges, including a high extinction coefficient, wide absorption bandgap, high non-radiative recombination, and environmental sensitivity. Fortunately, a promising strategy for rational encapsulation of perovskite ions through a chemical or physical barrier has been proven effective.^230,231 With the tunable pore size and pore shape of CPMs, it is possible to precisely match different perovskite crystal sizes and shapes effectively, significantly enhancing the compatibility between CPMs and perovskite crystals. Additionally, by serving as a domain-limiting factor, anchoring perovskite grains in a specific area using porous materials inhibits inter-grain crosstalk or ion escape during the ion migration process, Qiao et al. innovatively synthesized MOFs (named PCN-333 (Fe)) with stable configurations and mesoporous cages (∼5.5 nm and ∼4.2 nm), and successfully “confined” CsPbBr₃ perovskites in the cages of MOFs to form CsPbBr₃@PCN-333 (Fe) composite crystals as synergistic photocathode materials as shown in Fig. 12(a).²³² DFT calculations elucidated that the suitable distance between the perovskite and the cage facilitates significant interfacial charge transfer from CsPbBr₃ to PCN-333(Fe), making this composite an ideal candidate for photocatalysis, and employed as a synergistic photocathode in light-assisted Li–O₂ batteries and exhibited a high cathode discharge voltage and better cycling stability as shown in Fig. 12(b). This highlights the essential role of the CsPbBr₃ and PCN-333(Fe) synergistic effect in advancing the efficiency and durability of photocathode materials. To improve the environmental stability of perovskites, Wu et al. encapsulated MAPbI₃ QDs within the interstices of Fe-porphyrin-based MOFs (named PCN-221(Fe_x)) and schematic illustrations for synthesis as shown in Fig. 12(c).²³³ This innovative approach was successfully practiced in the field of artificial photosynthesis for the photocatalytic production of CO/CH₄ (as shown in Fig. 12(d) for CO/CH₄ yields of different Fe fractions). This strategy ensures that the encapsulated QD crystal structure remains intact while facilitating swift electron transfer from the photogenerated perovskites to the Fe sites, effectively separating the electron–hole pairs. The above two studies provide new ideas for the development of novel, stable, and efficient photocatalysts, as well as broaden the perspective on the integration of PCMs and perovskites, particularly inspiring applications in optoelectronic devices.

The 2D COFs with semiconducting properties are emerging as promising candidates in the field of gas sensors, optoelectronic devices, and photocatalysts due to their highly desirable designability in the structure, high specific surface area, high porosity, abundance of active sites, and unidimensional channels.^235–237 Sensors, within this context, hold pivotal roles in environmental surveillance, food quality assessment, and medical diagnostics. However, autonomous systems with advanced sensor hardware and computational capabilities remain a significant challenge to break through the von Neumann computational architecture. Regarding the challenge of preparing continuous films of 2D COFs, Ye et al. introduced the Cs₂PdBr₆ perovskite as a semiconductor-connected colloid for COFs (named TpPa-1) to activate their sensitivities for NO₂ gas sensing as shown in Fig. 12(e).²³⁴ With a remarkable detection limit of 40 ppb for NO₂ gas, the sensor exhibits a sensitivity 70 times greater than 20 other gases (e.g., CO and NH₃) at the same concentration. They analyzed the adsorption of NO₂ molecules on three contrasting films (TpPa-1/Cs₂PdBr₆, Cs₂PdBr₆, and TpPa-1) by DFT and sum frequency generation (SFG); it was elucidated that the SFG intensity signals of the three films were increased by the treatment of NO₂, with the most pronounced enhancement of the background signal in the TpPa-1/Cs₂PdBr₆ film, which indicated that NO₂ adsorption occurred to generate stronger charge transfer between the interfaces as shown in Fig. 12(f). This study not only devises a straightforward and efficacious strategy for COF–perovskite system hybridization but also extends the application spectrum of both materials across various domains.

Although research on complexes of perovskites and emerging CPMs have been reported, there are few applications reported in memristors, transistors, sensors, and other electronic devices, and thus, there are still bottlenecks and problems that need to be explored and solved. Firstly, the interface contact issue between CPMs and perovskites should be rationally modulated and engineered to preserve the perovskite crystal structure while leveraging the benefits of both materials. Secondly, an in-depth investigation of the physical mechanism of the operated CPMs/perovskite composite devices promises to inspire design and improve the device performance. Thirdly, an in-depth exploration of CPMs/perovskites for applications in a variety of device areas, including data storage, sensors, transistors, and multimodal, multisensory artificial neuromorphic devices, is performed.

5 Conclusions and perspectives

Rapid advances in materials science and nanotechnology have provided a wide potential for artificial neuromorphic devices. This review explores the comprehensive application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and CPMs, to develop advanced devices such as memristors, transistors, photodetectors, sensors, LEDs, and artificial neuromorphic systems. The versatility of perovskites is enhanced by integration with these materials, leveraging their unique properties in the form of composites with perovskites to counterbalance the deficiencies or amplify their strengths, such as modulating the ion migration, aligning energy levels, enhancing conductivity, improving light absorption, and improving stability. Consequently, the devices exhibit superior performance, including high sensitivity, high speed, low power consumption, and excellent durability. In Table 2, we summarize the typical performances and working mechanisms of typical perovskite composite devices. Despite significant strides in the combination of perovskites with various materials through techniques like hybridization and the formation of heterojunctions, challenges remain in fully harnessing their potential for new equipment. There are still significant challenges that need to be overcome to break through the current bottlenecks. For instance, perovskites are highly sensitive to moisture and oxygen, leading to degradation and instability issues. Additionally, they tend to have a limited lifespan and are prone to defects that can detract from their efficiency. Efforts are being made to address these challenges through the development of new fabrication techniques and the exploration of novel material combinations. However, there is still much work to be done before these materials can become a viable alternative to traditional silicon-based technologies in the electronics industry.

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Firstly, an in-depth investigation of interface engineering and interaction mechanisms is performed. A well-thought-out and expertly executed strategy for combining perovskites with diverse materials can effectively amplify their benefits and showcase their dual functionality. Meanwhile, when working with varying material compositions, it becomes crucial to design a compatible approach that aligns with the specific traits of each material, and this represents a significant challenge that must be addressed. With regard to materials selection, it is necessary to consider the key physical and chemical properties when it comes to perovskite composites, such as bandgap modulation capability, photoelectric sensitivity, and environmental stability. Additionally, in terms of structural design, it is essential to delve into the structural design principles of perovskite composites in optoelectronic and neuromorphic devices. This includes exploring the impact of the layer structure, interfacial engineering, and dimensional effects on the performance of these devices.

Secondly, research on long-term stability and sustainability is performed. Achieving consistent and reliable device performance necessitates a thorough imperative to recognize the impact of both the perovskite surface and intrinsic defects within perovskite structures, as well as their susceptibility to environmental degradation. This aspect is critical for facilitating widespread production and ensuring the devices’ effective deployment. Strategically employing passivation agents offers a promising solution to these challenges.

Thirdly, it is important to address the challenges of integrating and scaling perovskite composites by considering advanced solutions for improving the homogeneity and engineering interfaces. This includes utilizing scalable deposition techniques and effectively transitioning from lab-scale to industrial-scale production while maintaining homogeneity and cost-effectiveness. Future research should prioritize innovative material solutions and hybrid structures to overcome these barriers and expand the application of perovskite composites in neuromorphic computing.

Finally, the exploration and application of innovative devices merit further attention. Besides the currently existing applications such as solar cells, photocatalytic materials, photodetectors, neural sensors, and synaptic devices, the potential of composites and perovskites promises significant advancements in various fields in the future. For instance, the unique attributes of these composites and perovskites are poised to revolutionize the energy storage sector, facilitating the development of highly efficient supercapacitors and lithium-ion batteries. As mentioned in Section 4.5, the MOF is integrated with perovskites for photocatalysis and new energy battery fields, while the COF is hybridized with perovskites synergistically to improve gas detection capabilities. Furthermore, in the field of artificial intelligence, the combination of composites and perovskites also provides new solutions for building efficient AI hardware.

In conclusion, synergistically integrating perovskites with various high-performance novel materials can significantly enhance device performance, especially in controlling ion migration in perovskites, energy level alignment, enhancement of optical responsiveness and effective surface passivation. This combination is crucial for the advancement of neuromorphic memory devices and optoelectronic equipment. Through rational design and optimization of material combinations, the performance and functionality of optoelectronic devices and artificial neuromorphic devices can be further improved to provide strong support for future scientific research and engineering applications. Moreover, we hope that the insights and comprehensive discussions presented in this review will deepen our understanding of neuromorphic devices based on composite perovskites and other materials. This knowledge is essential in guiding the design and production of high-performance electronic devices, paving the way for their application in novel devices geared toward next-generation electronic advancements.

Author contributions

Huaxin Li: writing – original draft, investigation, conceptualization, visualization, and writing – review and editing; Qingxiu Li: writing – original draft, investigation, and conceptualization; Tao Sun: visualization and supporting; Ye Zhou: funding acquisition; Su-Ting Han: conceptualization, funding acquisition, supervision, and writing – review and editing.

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant no. 62122055, 62074104, 52003162, and 52373248), the Guangdong Provincial Department of Science and Technology (grant no. 2024A1515011718, 2024A1515010006, and 2024B1515040002), and the Science and Technology Innovation Commission of Shenzhen (grant no. JCYJ20220531102214032 and 20231123155543001), RSC Researcher Collaborations Grant (Grant No. C23-2422436283), State Key Laboratory of Radio Frequency Heterogeneous Integration (Independent Scientific Research Program No. 2024010) and NTUT-SZU Joint Research Program (Grant No. NTUT-SZU-113-01).

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