Photopic Adaptation Mimicked by Y₂O₃-Based Optoelectronic Memristor for Neuromorphic Visual System

Abstract

Visual adaptation is one of the most significant features that helps organisms process complicated image information in time-varying environments. Emulating this function is highly desirable for energy-efficient image perception. In this work, we demonstrate an yttrium oxide (Y₂O₃)-based optoelectronic memristor and emulate photopic adaptation behavior in a single device. Decay amplitude and photosensitivity are indexed to describe the time-dependent characteristics of photopic adaptation. An intensity-dependent characteristic, namely Weber’s law, is also investigated in this work. Photopic adaptation originates from the trapping of photogenerated carriers in oxygen vacancies. Based on photopic adaptation behavior, a neuromorphic vision system capable of adapting to environmental brightness is constructed using the proposed optoelectronic memristor array. Memristor arrays can emulate sensing and adaptation functions in order to enhance images against bright backgrounds. Our work provides a feasible pathway toward self-adaptive neuromorphic vision systems.

1. Introduction

Due to the multifunctional integration of image sensing, storage, and processing for next-generation human–machine interactions and humanoid robots [1,2,3,4,5,6,7,8], bio-inspired neuromorphic vision systems have gained considerable attention in recent years. Conventional artificial visual systems are usually composed of different functional units according to their architectural design and manufacturing technologies. A lack of neuromorphic function and the separation of the physical architecture would result in both significant energy consumption and data delay problems [9,10,11]. In contrast, neuromorphic visual systems with an in-sensor computing architecture have considerable potential in highly efficient image perception. The development of a novel neuromorphic visual system is at the cutting edge of research in this field, exhibiting great promise for applications in neuromorphic intelligence technologies and machine vision [11,12,13].

In biological visual systems, the visual adaptation function is an important feature in achieving autonomic responses to environmental stimuli. This ability is beneficial in adapting to varying light environments [14,15,16]. For instance, an individual might experience temporary dizziness due to retinal overstimulation when suddenly exposed to bright light. Then, the retina gradually reduces its sensitivity over time, enabling improved visual perception. The process by which sensitivity gradually decreases under continuous light stimulation is photopic adaptation. Moreover, the perceived change is proportional to the stimuli intensity, which is known as Weber’s law [17,18,19], which is the critical capacity of the organism to identify and distinguish diverse perceptual stimuli from the background. Visual adaptation enables the human eye to perform detailed visual information analysis in a time-varying environment. However, in conventional machine vision systems, the adaptive processing function usually requires complex hardware circuitry and algorithms, which typically limits the operating efficiency [20,21]. Therefore, developing novel neuromorphic hardware to implement these biological adaptabilities is greatly desired for highly efficient visual perception. To date, much effort has been made in constructing optoelectronic devices to demonstrate visual adaptive processing functions [22,23,24,25,26]. He et al. proposed a photo-triggered organic active adaptation transistor (OAAT) and emulated visual active adaptation by combining photovoltaic- and field-effect modulation [23]. The OAAT exhibits excellent stability and uniformity, and this approach can be applied to a wide variety of organic semiconductors. Liao et al. reported a vision sensor array that uses a bottom-gate bilayer MoS₂ phototransistor; they demonstrated both scotopic and photopic adaptation by introducing trap states, offering a broad perception range and image contrast enhancement [26].

In this work, we reported a two-terminal memristor with photopic adaptation behaviors by utilizing the nanocomposite film of sodium alginate (SA) and Y₂O₃ NPs. The time-dependent characteristic of photopic adaptation is investigated by experimentally measuring decay amplitude and photosensitivity (P_t) values. In addition, the intensity-dependent characteristic of visual adaptation, i.e., Weber’s law, is also investigated in this work. The above photopic adaptation can be attributed to the trapping and detrapping behaviors of photogenerated electrons in the Y₂O₃. Furthermore, an optoelectronic memristor array with photopic adaptation behavior was fabricated, substantially improving the efficiency and accuracy of the image recognition process. The Y₂O₃-based optoelectronic memristor can be regarded as a promising candidate device for constructing self-adaptive neuromorphic vision systems.

2. Materials and Methods

Device Fabrication:

Memristors with Au/SA: Y₂O₃ NPs /FTO sandwich structures were fabricated on ITO substrates and then patterned into optoelectronic memristive arrays with a diameter of 500 μm using a metal mask. SA powder and Y₂O₃ NPs powder was purchased from Macklin Biochemical Co., Ltd., Shanghai, China. First, SA powder (100 mg) and Y₂O₃ NPs (200 mg) were mixed in deionized water (10 mL). The solution was mechanically stirred at room temperature for a long time (≥3 h), allowing the powder to dissolve fully and the SA: Y₂O₃ NPs nanocomposite film to be formed. The film was fabricated by spin-coating SA: Y₂O₃ NPs solutions on the ITO/glass substrate with a spin speed of 3000 rpm and spin time of 30 s. Finally, the Au electrodes were deposited on the top of the film to act as electrodes by using thermal evaporation methods.

Measurement and Characterization:

The optical signals were measured using a xenon lamp (LA-410UV, Hayashi, Tokyo, Japan) with an optical filter. Device conductance was monitored with a source meter (2636b, Keithley, Solon, OH, USA) and a probe station (TTPX, Lake Shore, Westerville, OH, USA). The positive current was defined as the flow from the top Au electrode to the bottom ITO electrode. The Y₂O₃ nanoparticles in mechanism part were annealed at 500 °C in a muffle furnace under an air atmosphere for 3 h.

3. Results and Discussion

As shown in Figure 1a, the optoelectronic memristor consists of an Au/SA: Y₂O₃ NPs/indium tin oxide (ITO) sandwich structure. A cross-sectional scanning electron microscopy (SEM) image of SA: Y₂O₃ is presented in Figure 1b; the film thickness is about 300 nm. In addition, Au films were deposited using thermal evaporation methods, and these served as the top electrodes (TEs). A read voltage of 0.5 V was applied to the Au top electrode, while the bottom ITO electrode was grounded, and the optical signal was applied to the device through the top electrode. As shown in the transmission electron microscopy (TEM) images (Figure 1c,d), Y₂O₃ NPs with a spherical shape exhibit an average particle size of ~50 nm. The lattice fringes of 0.430 and 0.305 nm correspond to the (211) and (222) planes of anatase Y₂O₃, respectively. Then, the crystallographic structure of Y₂O₃ NPs was uncovered by XRD measurements, as shown in Figure 1e. Y₂O₃ NPs have eight broad peaks at 20.5°, 29.1°, 33.8°, 35.9°, 39.8°, 43.5°, 48.5°, and 57.6°, corresponding to the (211), (222), (400), (411), (332), (134), (440), and (622) planes, respectively; this is consistent with the cubic phase of Y₂O₃ [27,28,29]. Figure 1f illustrates the absorbance spectra of SA: Y₂O₃ NPs nanocomposite and pure SA films. Using the SA film as a reference, the SA: Y₂O₃ NPs film exhibits strong light absorption at wavelengths smaller than 390 nm [30,31,32]. Therefore, in this work, optical modulation was performed using a xenon lamp with a UV (360–390 nm) optical filter.

As a critical feature of biological retina, visual adaptation comprises two primary forms, i.e., scotopic and photopic adaptation [33,34,35]. In this work, photopic adaptation is implemented using the characteristic that the output current decreases over time under high-intensity UV irradiation. Figure 2a shows the photoresponsivity evolution of our device under different irradiation conditions. In this work, the photocurrent was recorded under a fixed voltage of 0.5 V, contributing to its ultra-low power consumption (pW level). The memristor current exhibits a gradual increase at a light intensity of 1.7 mW/cm². By contrast, the output current increases initially and then decreases gradually within the following few seconds, when the optical intensity increases to 6.2 mW/cm². The above process is similar to photopic adaptation in biological visual systems. The devices have excellent uniformity across 50 continuous cycles (Supplementary Material, Figure S1), providing a stable basis for implementing photopic adaptation in future studies. In this case, the time-dependent decay amplitude and photosensitivity (P_t) are defined as follows:

Decay amplitude = (I_peak − I_t)/I_peak × 100%

P_t = ΔI_t/I_dark

where I_peak represents the transient output current under light stimulus, I_t is the current after ‘t’ seconds of light irradiation, and I_dark is the dark current. Then, we evaluated the photopic adaptation performance after light exposure for ‘t’ seconds (Supplementary Material, Figure S2) [23,36,37]. It can be seen that the decay amplitude increased with an increase in light intensity, as shown in Figure 2b; this is analogous with the photopic adaptation feature. The above result indicates that photopic adaptation is preferentially activated under high-intensity optical stimulus, protecting the receptors from fatigue [37,38]. In addition, as shown in Figure 2c, the photosensitivity after light exposure at 21.1 mW/cm² for 30, 50, 80, and 100 s decreased from 18 to 12.1, 10.1, 7.6, and 7.0, respectively, consistent with the photopic adaptation feature.

Moreover, Weber’s law is emulated by introducing additional flash stimulations with I_F varying from 8.3 to 21.1 mW/cm², as shown in Figure 2d and Supplementary Material Figure S3. The background intensity I_B is desensitized to steady states. It can be seen that the device current is only sensitive to flash stimulation as I_F is larger than I_B. This means that the device sensitivity of flash stimulation strongly depends on the background condition. The proportional relationship between sensitivity changes and background intensity is known as Weber’s law, which is expressed as follows:

$${\displaystyle \frac{S_F^D}{S_F}}-1={\displaystyle \frac{I_B}{I_0}}$$

(1)

where $S_F^D$ and S_F are the flash sensitivity in darkness and under background conditions, respectively. I₀ is equal to the background intensity required to reduce the sensitivity by half [37,38,39]. According to our experimental results (Supplementary Material, Figure S4), the constants $S_F^D$ and I₀ are calculated as 0.1 and 1.7 mW/cm², respectively. Figure 2e shows the curve of Weber’s law according to Equation (1), where S_F is inversely proportional to I_B with a slope of 1.30, resembling that of biological receptors.

We also investigated the operating mechanism of our Y₂O₃-based optoelectronic memristor. According to Figure 1d and Figure 2a, light-induced adaptation behavior can be attributed to the Y₂O₃ NPs in the nanocomposite film. Generally, the characteristics of oxide films are closely associated with their chemical compositions [40]. X-ray photoelectron spectroscopy (XPS) was used to investigate the surface composition and chemical states of Y₂O₃ nanoparticles. As shown in Figure 3a,b, the O1s XPS spectra of Y₂O₃ nanoparticles before and after annealing are fitted into three components. The peaks located at 529.0, 531.3, and 531.8 eV correspond to lattice oxygen (O_L), oxygen vacancies (O_V), and chemisorbed oxygen (O_C), respectively [41,42,43]. A large amount of O_V exists in the Y₂O₃ nanoparticles before annealing; however, after annealing, the amount of O_V clearly decreases. Figure 3c shows the responsivity evolution of our device before and after annealing. Before annealing, the response current rises abruptly when the device is irradiated by UV light and then decays gradually, indicating photopic adaptation. By contrast, Au/Y₂O₃(annealed)/ITO exhibits a gradual increase in response current under the same UV light. These results suggest that the photopic adaptation behavior can be attributed to electron trapping/detrapping in O_V. As shown in Figure 3d, upon light irradiation, electrons in the valence band and trap states are promptly excited into the conduction band, accompanied by a synchronously enhanced photocurrent. However, some photogenerated carriers will be captured by the trap sites in Y₂O₃, causing photocurrent attenuation. Thermal treatment reduces inherent trap sites effectively, thereby reducing the trapping probability of photogenerated electrons. As above, photopic adaptation behavior is absent in annealed devices [44,45,46].

In addition, in our memristive array, image sensing and adaptation behavior under a bright background can be emulated in our memristive array. We constructed a 7 × 7 device array to perceive the ‘heart-shaped’ pattern under a bright light background (Supplementary Material Figure S5); a light stimulus of 8.3 mW/cm² was applied to all 7 × 7 pixels as a bright background. Light signals with different intensities (6.2 and 15.2 mW/cm², 2 s) were applied to parts of the units as ‘heart-shaped’ patterns, respectively, as illustrated in Figure 4a,b. For a flash intensity of 6.2 mW/cm², the ‘heart-shaped’ patterns could not be identified from the bright background conditions (Supplementary Material Figure S6) because the device is only sensitive to flash stimulations, as I_F is larger than I_B in the photopic adaptation state. For a flash intensity of 15.2 mW/cm², the device array with a larger response current value was saturated in the instantaneous exposure environment, hindering image recognition. Interestingly, the ‘heart-shaped’ patterns could be gradually identified from the bright background conditions due to the decreased sensitivity under continuous optical irradiation, as shown in Figure 4c. The recognition accuracy of images against bright backgrounds can be improved from 14.34% to 92.32%, with the light pulse number increasing to 5, as shown in Figure 4d. These results indicate that our device can be utilized as exposure control elements for the next generation of intelligent circuits, exhibiting considerable potential for application in artificial intelligence, i.e., autonomous driving, and vision.

4. Conclusions

In summary, we demonstrated a Y₂O₃-based optoelectronic memristor and emulated the photopic adaptation of biological retina. Moreover, the time-dependent nature of photopic adaptation and the changing sensitivity, dependent on background conditions (i.e., Weber’s law), were also investigated. The corresponding mechanism can be attributed to the trapping of photogenerated electrons in oxygen vacancies. We emulated image sensing and adaptation functions based on photopic adaptation. Our work provides a promising hardware platform for future self-adaptive neuromorphic vision systems and human–machine interfaces.