Fig 1 Schematic of the human visual-perception process
Published:31 August 2023,
Published Online:05 May 2023,
Received:22 November 2022,
Revised:08 April 2023,
Accepted:19 April 2023
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Optoelectronic devices are advantageous in in-memory light sensing for visual information processing, recognition, and storage in an energy-efficient manner. Recently, in-memory light sensors have been proposed to improve the energy, area, and time efficiencies of neuromorphic computing systems. This study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal–oxide–semiconductor (MOS) charge-trapping memory structure—the basic structure for charge-coupled devices (CCD)—and showing its suitability for in-memory light sensing and artificial visual perception. The memory window of the device increased from 2.8 V to more than 6 V when the device was irradiated with optical lights of different wavelengths during the program operation. Furthermore, the charge retention capability of the device at a high temperature (100 ℃) was enhanced from 36 to 64% when exposed to a light wavelength of 400 nm. The larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al2O3/MoS2 interface and in the MoS2 layer. A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the device. The array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91% accuracy. This study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception, adaptive parallel processing networks for in-memory light sensing, and smart CCD cameras with artificial visual perception capabilities.
In this modern era, artificial intelligence and the Internet of Things have led to the rapid expansion of sensory nodes, which produce an increasing volume of raw data
Recently, two-dimensional (2D) transition-metal dichalcogenide (TMD)-based optoelectronic devices have been broadly considered because of their attractive optical and electrical features
Fig 1 Schematic of the human visual-perception process
2D TMD materials can be grown via chemical vapor deposition (CVD) or transferred using physical and chemical exfoliation methods. Particularly, the mechanical exfoliation method has enabled the fabrication of high-quality TMDs, such as MoS2, WS2, ZrS2, MoSe2, NbSe2, TiS2, TaS2, and WSe2, for several applications
In this study, MOS memory architecture is examined using a light-sensitive 2D material-based charge-trapping layer. This makes the solution-processable technology promising as a low-cost and low-thermal-budget coating technique. This is necessary when dealing with highly sensitive optical components on a chip and is critical for allowing back-end-of-line compatibility. Alongside other 2D materials, MoS2 is very attractive owing to its transition from an indirect bandgap of 1.2 V to a direct bandgap of 1.8 eV. This is sufficient to balance the limitations of zero-bandgap graphene for use in electronic devices
Recently, researchers have reported that the threshold voltage of a transistor or memory can be shifted when the optical light is illuminated. The memory window of a transistor was modulated using different light wavelengths
MoS2 based CTM | Operating condition | Endurance | Retention | Ref. |
---|---|---|---|---|
MoS2/high-k | +26 V/−26 V/200 ms | 120 cycles | 2000 s at RT |
|
MoS2/high-k | +12 V/100 μs, −10 V/10 μs | 8000 cycles | 10000 s at RT |
|
Al2O3/MoS2/Al2O3 | +60 V/−60 V | 1000 cycles | 10 days at RT |
|
MoS2/high k (TTO) | +4 V/−4, 1 ms | 1000 cycles | 1000 s at RT |
|
MoS2/CrPS4 | +30 V/−30 V | 625 cycles | ------- | 73 |
HfO2/MoS2/SiO2 | +16 V/−16, 1 s | 550 cycles | 10000 s at RT |
|
GDY/MoS2 | −80 V/+80 V | 3000 cycles | 10000 s at RT |
|
GBM3 | +16 V/−16 V, 100 μs | ------- | 1200 s at RT |
|
MoS2/h-BN | +5 V/−5 V, 100 ms | 105 cycles | 108 s at RT |
|
Al2O3/MoS2/Al2O3 | +6 V/−6 V, 1 μs | Electrically programmed/erased: 106 cycles Optically programmed/electrically erased: 1000 cycles Wavelength: 600 nm, 550 nm, 500 nm, 450 nm, 400 nm | 10 years at 100℃ | This work |
The complete fabrication process of the light-sensitive MOS devices is shown in
Fig 2 Schematic of the fabrication process of the light-sensitive MOS memory devices
Fig 3 Effect of spin coating vs. drop casting of MoS2 flakes on the memory electrical performance.
a, b High frequency (80 kHz) C–V curves of the D1 and D2 devices with a memory window of 0.6 V and 2.8 V, respectively. c, d Retention tests of devices D1 and D2
To check the difference between the spin-coated and drop-cast MoS2 flakes of the D1 and D2 samples, the surface morphologies of both samples were analyzed, as shown in
Fig 4 Effect of spin coating vs. drop casting techniques on MoS2 flakes surface coverage.
a SEM image of MoS2 flakes using the spin-coating technique showing a lower density of flakes. b SEM image of MoS2 flakes using the drop-casting technique showing the large density of flakes
To validate each layer in the D2 device, secondary ion mass spectrometry (SIMS) analysis was performed, as shown in
Fig 5 Drop casted MoS2 material characterization.
a The SIMS depth profile of the D2 device, b XRD spectra of the drop-cast MoS2 film, c Mo 3d and S 2s, and d S 2p XPS peaks of the drop-cast MoS2 film
The crystal structure of drop-cast MoS2 was measured using X-ray diffraction (XRD), as shown in
The Raman spectrum of the drop-cast MoS2 film is shown in
Fig 6 MoS2 structural characterization and the resulting charge trapping memory electrical performance.
a Raman spectrum of the drop-cast MoS2 layer, b C–V characteristics of the D2 device for 50 repeated programmed and erased cycles with a frequency of 80 kHz, c C–V characteristics of the device with sweeping voltages of +4/−4 to +10/−10 in the programmed and erased conditions, and d memory window of the device with sweeping voltages (inset shows the frequency-dependent C–V curve of the D2 device)
1
where Ct is the capacitance of the device per unit area, ΔVt is the memory window of the device at VFB, and q is the elementary charge. Using the above equation, the calculated trapped charge density of the D2 device is approximately 9.2 × 1013cm−2.
It was confirmed that the D2 device showed a considerable improvement in the memory window under low operating voltages. This improvement in the D2 device is due to the large number of electrons trapped at the Al2O3/MoS2 interface and MoS2 layer. To confirm the interface trapping in the D2 device, we measured the frequency-dependent C–V characteristics of the device, as shown in the inset of
The cycle-to-cycle uniformity of the device was calculated with operating voltages of +6/−6 during the forward and backward states, as shown in
Fig 7 Reliability characteristics of MoS2 based memory.
a Cycle-to-cycle uniformity of the D2 device, b device-to-device uniformity of the 10 devices that were chosen randomly, c long-term endurance cycles of the device, and d high-temperature retention test of the device
In addition to the electrical features, we also measured the light-sensing features of the D2 device using irradiation with different light wavelengths, as portrayed in
Fig 8 Optical charaterization of the MoS2 based in-memory sensor.
a Schematic of the device with optical light illumination, b C–V curves of the device using different optical light wavelengths from 600 to 400 nm with an interval of 50 nm, c wavelength-dependent threshold voltage of the device, d repeatability of C–V curves for 50 continuous cycles with optical programming (+6/−6) and electrically erasing (−8/+8), e optically programmed and electrically erased endurance of the device with illumination at a wavelength of 400 nm, and f high-temperature retention stability of the device
This means that owing to the illumination of the optical light, the device trapped more electrons compared to the dark condition. Furthermore, we examined the effect of different light wavelengths from 550 to 400 nm with a 50 nm interval using a similar programming voltage, intensity, and illumination time. We recorded a tremendous shift in the threshold voltage from 3.3 V to more than 6 V. The maximum shift in the threshold voltage was observed at a wavelength of 400 nm. Thus, it was confirmed that the MoS2 layer trapped more electrons at a wavelength of 400 nm
To validate the potential of the MoS2-based in-memory light sensor for artificial visual perception, we analyzed the synaptic features of the optical D2 device. Optical light (400 nm) with an intensity of 50 mW cm−2 was used for programming, while the device was erased electrically. The C–V curves of the device, when optically programmed and electrically erased, are shown in
Fig 9 Application of the in-memory optical sensor in artificial visual perception.
a C–V curve of the D2 device using an optical light of 50 mW cm−2 (400 nm) from 1 to 75 µs with an interval of 5 µs; b Memory window of the device, which is optically programmed; c Memory window of the device that is electrically erased; d A small CNN model is used to make a binary classification over the CIFAR-10 dataset; e The kernels (left) are obtained from the ideal software test. The offline mapping kernels (right) are transferred from the corresponding ideal kernel values by illuminating and programming the device. f The confusion matrix of the test results for 764 images in the CIFAR-10 dataset. The yellow-colored diagonal elements in the matrix represent the correctly identified cases
To verify the accuracy of the D2 device-based CNN, 3822 chosen objects with the abovementioned labels from the CIFAR-10 dataset were randomly separated at a ratio of 4:1 for the model training and testing processes. The kernel value can be remotely programmed by illuminating each pixel with specific programming optical pulses. The discrete kernel values were calculated from the 16 discrete levels of the programmable states, as shown in
In this study, we investigated drop-cast MoS2-based Al/Al2O3/MoS2/Al2O3/P+-Si MOS memory devices for application in artificial visual perception and in-memory light sensing. The device showed a decent memory window of approximately 2.8 V with an operating voltage of +6/−6 V, high-temperature retention (100 ℃) for 10 years, and excellent endurance (106 cycles) without any deterioration. The larger threshold voltage shift with the operating voltage revealed that a greater number of electrons were trapped at the Al2O3/MoS2 interface and MoS2 layer. Interestingly, the device showed a larger shift in the memory window from 2.8 V to more than 6 V when the optical light of different wavelengths was stimulated for 2 s during the program operation. A CNN was used to measure the optical sensing and electrical programming abilities of the device. The array simulation received the optical images transmitted over the blue light wavelength and performed inference computation to process and recognize the images with 91% accuracy. The demonstrated approach is promising for the development of future artificial retina networks for artificial visual perception and in-memory light sensing applications.
An MOS memory device was fabricated on a P+Si substrate. First, the Si substrate was wet-etched in a buffered oxide etchant for 3 min and later cleaned with deionized water and a nitrogen gun to remove the native oxide from the wafer. Furthermore, 3-nm-thick Al2O3 was used as a tunnel oxide layer by plasma-enhanced atomic layer deposition (PE-ALD) at 250 ℃ using Al (CH3), trimethylaluminum, and O2 plasma. Furthermore, a 70-nm-thick molybdenum disulfide (MoS2) film was deposited using the drop-casting method. Additionally, a 7-nm-thick Al2O3 layer was deposited by PE–ALD as a blocking oxide layer at 250 ℃. Finally, a 50-nm-thick Al film was deposited as the top electrode by direct current sputtering using a metal shadow mask with a diameter of 100 µm. Spin-coated MoS2-based MOS devices were also fabricated for comparison purposes.
Scanning electron microscopy (SEM, Nova Nano-SEM 450) was used to examine the surface morphologies of the spin-coated and drop-cast MoS2 samples. The crystal structures of the spin-coated and drop-cast MoS2 layers were characterized using a Bruker D8 Advance X-ray diffraction (XRD) system with a Cu Kα (λ = 1.5405 Å) source at 40 kV. X-ray photoelectron spectroscopy (XPS) was performed on the drop-cast MoS2 sample in a high vacuum using a Kratos Amicus XPS system equipped with a monochromatic Al Kα X-ray source operating at 10 kV. Raman spectra were obtained for the drop-cast MoS2 sample using a Wintec Apyron Raman spectrometer equipped with a 532 nm laser source excitation. Electrical and optical measurements were performed using a Keysight B1500 A semiconductor device analyzer and tunable light source (Newport Corporation).
For neuromorphic vision systems, this study was simulated based on PyTorch, which is one of the most widely used machine learning frameworks for training deep neural networks. The pattern recognition task included the binary pattern classification of images extracted from the CIFAR-10 dataset. We selected the labels "dog" and "automobile" as our classification targets for the CNN, which better exploited the device property of long-term potentiation (LTP)/long-term depression (LTD) alongside a small model.
The authors acknowledge financial support from the Semiconductor Initiative, King Abdullah University of Science and Technology, Saudi Arabia (KAUST Research Funding (KRF) under Award No. ORA-2022-5314). The authors also acknowledge the support from the KAUST Core labs, including the Microfluidics lab.
N.E.A. conceived the idea of smart memory that can sense and compute and supervised the project. A.N. conceived the idea of MoS2-based charge-trapping memory. D.K. and L.J. carried out the fabrication of the devices. D.K. characterized the devices and analyzed the data. H.L. carried out the neuromorphic computing simulations. D.K., H.L., and N.E.A. wrote the manuscript. D.K., H.L., A.R., A.N., and N.E.A. discussed the experimental results. All authors commented on and discussed this work.
The authors declare no competing interests.
Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41377-023-01166-7.
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