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# An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations

## Abstract

In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in a small area with high energy efficiency remains a challenge. In this work, we propose a circuit architecture that integrates monolayer MoS2 transistors in a two-transistor–one-capacitor (2T-1C) configuration. In this structure, the memory portion is similar to a 1T-1C Dynamic Random Access Memory (DRAM) so that theoretically the cycling endurance and erase/write speed inherit the merits of DRAM. Besides, the ultralow leakage current of the MoS2 transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The electrical characteristics of a single MoS2 transistor also allow analog computation by multiplying the drain voltage by the stored voltage on the capacitor. The sum-of-product is then obtained by converging the currents from multiple 2T-1C units. Based on our experiment results, a neural network is ex-situ trained for image recognition with 90.3% accuracy. In the future, such 2T-1C units can potentially be integrated into three-dimensional (3D) circuits with dense logic and memory layers for low power in-situ training of neural networks in hardware.

## Introduction

Artificial intelligence (AI) algorithms require significant computing power for running successive matrix calculations. Multiply accumulate (MAC) is the most critical operation in AI computation at the chip level. In-memory computing is a technology that uses memory devices assembled in an array to execute MAC operations1. As such, it has triggered extensive research interests because data transfer in a conventional von Neumann architecture has a bottleneck between memory and logic circuits2,3, and a memory device capable of in-memory computing can be used to carry out high-throughput MAC operations directly4,5. For an ideal in-memory computing, various features are preferred for its memory portion, including a nonvolatile characteristic, multi-bit storage capability, long cycling endurance, simple erase/write operation, etc.1,4,6.

Various types of memory devices have been investigated for performing MAC operations. Among them, nonvolatile memory devices, include resistive random-access memory (RRAM)7,8,9, phase change RAM (PCRAM)10,11,12,13, spin-transfer torque magnetoresistive RAM (STT-MRAM)14,15, and conventional FLASH16,17,18. Most nonvolatile memories can realize multi-bit storage, but they usually exhibit a stochastic nature, resulting in a learning accuracy loss in the neural network applications1,5. Their limited cycling endurance (FLASH ~105, RRAM and PCRAM 106–109) and relatively complex memory operation19 are also unsuitable for frequent weight update processes required for in-memory computing20. For example, FLASH usually requires high voltages for the write operation. RRAM/PCRAM requires continuous voltage pulses to tune the conductive filaments to control the electrical conductance, which complicates the multiplication operation5. STT-MRAM requires a relatively large current to program information in the storage element, which carries greater dynamic power dissipation and overall write energy cost4,15. On the other hand, volatile memory devices can also execute in-memory computing, such as static random accesses memory (SRAM)21,22,23 and dynamic random-access memory (DRAM)24,25,26. Theoretically, they have much higher programming speed and superior endurance(>1016)1,4, but in volatile memories, the stored information dissipates quickly, and a periodic refresh operation is required24. Furthermore, SRAM and DRAM belong to binary memory, and their main applications are limited in the binary-weighted network1,25,26. An overall comparison among different types of in-memory computing technologies is also concluded in Supplementary Table S1.

Other than exploring different memory technologies for in-memory computation, suitable channel material is also critical. Two-dimensional layered materials (2DLMs), well-known for their intrinsic nature of atomic thickness, allow aggressive channel length scaling owing to its superior electrostatic control that can substantially suppress short-channel effects27. In addition, unlike rigid silicon CMOS, 2DLMs can enable flexible electronic circuitry with multiple sensing functionalities, adding value towards a multifunctional hardware platform28. Among various 2DLMs, semiconductive transition metal dichalcogenides (TMDs) are promising due to their rich band structures and tunable bandgaps29, and molybdenum disulfide (MoS2) is one representative that has been extensively investigated in the past few years30,31. Compared to silicon and other TMDs with a narrower bandgap, monolayer MoS2 has a relatively wide bandgap (~1.8 eV) to enable a large current on/off ratio in its field-effect transistors (FETs)32. Now wafer-scale continuous MoS2 films can already be synthesized by chemical vapor deposition (CVD) methods33 and transferred to arbitrary substrates34. The device processing techniques have also been intensively investigated to address early criticism of 2D-FETs, such as the realization of Ohmic contact and integration of high-k dielectrics35,36,37. Therefore, recent exploration of 2DLMs has been expanded from fundamental investigations to the demonstration of circuit-level device applications, such as memories, logic gates, and sensors35,38,39. A 1T-1R structured in-memory computation unit has also been demonstrated lately, in which a MoS2 FET is used as a selector, and a HfOx-based RRAM is used to perform analog calculation40.

In this work, we explored and designed a MAC circuit architecture in a 2T–1C configuration, which includes two MoS2 FETs and one metal-insulator-metal capacitor. In such a structure, the 1T–1C portion acts as a DRAM cell. Owing to the ultralow leakage current of the MoS2 FETs, a voltage with 8-level (3 bits) quantization can be stored on a capacitor with longer than 10 s retention time, enough for additional complex operations. The stored voltage is connected to the gate of the second MoS2 transistor, in which the input drain bias Vd and gate bias Vg can determine the drain current Id to realize an analog multiplication operation. Moreover, the current in multiple 2T–1C rows can be converged together, giving an addition operation. Based on two identical 2T–1C cells, we demonstrate a simple MAC operation circuit, which is the core module for the convolution operation in an artificial neural network. A more complicated MAC array was trained against the MNIST handwritten digit database and used for image recognition. The successful recognition rate was found to reach 90.3%. Our 2T–1C MoS2 cells highlight the promising potential of in-memory computing and in situ training of neural networks based on emerging 2D semiconductors to overcome the bottleneck of von Neumann computing.

## Results and discussion

Figure 1a shows a wafer-scale MoS2 film grown using the CVD method (see SI). Raman spectra (Fig. 1b) gathered from different positions in the MoS2 film show acceptable spatial uniformity, which is vital for performing accurate analog calculations in our circuit. The transfer characteristics (Fig. 1c) of 24 MoS2 FETs on a 1 × 1 cm2 wafer exhibit large on/off current ratios (~107) and an acceptable homogeneity level. We fabricated a 2T–1C cell (optical microscopic image shown in Fig. 1d) to provide charge storage and analog computation. Figure 1e shows a circuit schematic of such a 2T–1C cell; the left 1T–1C structure forms a dynamic memory in which the MoS2 FET is labeled T1, and the MoS2 FET T2 on the right side is used to accomplish the multiplication calculation. Figure 1f schematically illustrates its 3D structure, and the fabrication process is described in the “Methods” section.

The refresh voltage Vre on the refresh line (RL) controls the ON/OFF state of transistor T1. During a write operation, T1 is turned on and the signal Vw applied by the weight line (WL) then charges the capacitor, which indicates the weight has been written into this 2T–1C cell. During the hold operation, T1 is turned off by applying a negative Vre. Due to the ultralow leakage current in the MoS2 channel in the OFF state (see Fig. S1), the charge stored in the capacitor can be held for a long time to maintain the voltage that acts as a gate voltage for T2. Since the input Vx is applied as a drain voltage to T2, the drain current (Id) in T2 is controlled with a combination of Vw and Vx. If the applied Vw and Vx locate in a relatively linear range of the output and transfer characteristics for the MoS2 FET, an analog multiplication operation between Id, Vx, and Vw can be realized, which will be discussed in detail later in this paper.

We now propose an array circuit based on such a MoS2 2T–1C unit cell to implement a MAC operation in an electrical circuit. The circuit diagram is displayed in Fig. 1g, which corresponds to a MAC operation $${Y}_{m}=\mathop{\sum }\limits_{k=1}^{n}{V}_{{\rm{x}}k}\times {W}_{{km}}$$ (Fig. 1h). In each unit, the weight Wnm is stored in the capacitor and updated using the RL and WL. The input voltage Vxn is then applied to the entire column n. Both Wnm and Vx determine the drain current Id in each MoS2 FET. Finally, the output currents in all rows are added to give a total current Im. The collected current then flows into the current block for further calculation. The relationship between $${I_{m}},\,{W_{nm}},\,{\mathrm{and}}\,{V_{x}}\,{\mathrm{is}}\,{I}_{m}=\mathop{\sum }\limits_{k=1}^{n}{\rm{g}}\,(V_{{\rm{x}}k},{W}_{{km}})$$, where $${\rm{g}}\left(x\right)$$ is a current–voltage transform function that depends on the transfer and output characteristics of transistor T2. Below we will try to build a correlation between Ym and Im.

We first characterize the properties of the 1T–1C storage module. Figure 2a shows a schematic diagram of the measurement circuit, in which one end of the capacitor is connected to an external oscilloscope (see Fig. S4 for more details). The internal resistance of the oscilloscope Rin is used to estimate the current flow (IQ) during read/write operations by measuring the voltage of Rin. To measure IQ, voltage signals Vw and Vre are applied to T1 (Fig. 2b) with pulse widths of 12 and 10 ms, respectively. Vw rises 1 ms earlier than Vre and falls 1 ms later than Vre to ensure the charge is entirely written onto the capacitor and prevent leakage current through T1. Vre and Vw were both set to 3 V during the write operation. The high Vre value turns on T1, allowing Vw to charge the capacitor to the same potential. A positive current pulse (IQ+) during the write operation indicates a charge flows into the capacitor. After the write operation completes, Vre is switched to −3 V to turn off T1. Due to the ultra-low leakage current (Fig. S1), the charged voltage on the capacitor can be stably maintained during the write operation. After waiting for 10 s, a read operation is triggered, where Vre = 3 V and Vw−read = 2 V. The polarity of the measured IQ pulse is now negative, indicating the capacitor potential is higher than 2 V and charge flows out of the capacitor. In contrast, if the capacitor potential is less than 2 V, the capacitor will be recharged again, giving a positive current pulse. To further characterize the dependence of Vw−write for reading IQ, the above measurements were repeated. Figure 2c shows the IQ pulses for reading under various values of writing Vw-wirte. To estimate the charge in the capacitor, after waiting for 10 s, Vw−read = 2 V is applied to compare with the retained capacitor voltage to read the remaining charge. The amplitude of the IQ pulse becomes larger as Vw increases. It is also noted that all IQ pulses are under 2 ms (Fig. 2c), which approximately equals the write time. The integral of the current overtime during a read cycle equals the charge Qread remaining after the waiting interval (10 s). In Fig. 2d, the calculated Qread vs. Vw curve is linear, indicating that the charge saved on the capacitor can still be differentiated after 10 s.

To test whether the voltage stored in the capacitor can effectively drive T2, we examined the time evolution of the drain current Id in T2 after completing a storage operation. Figure 3a shows a complete diagram of the measurement circuit used to measure a 2T–1C cell’s electrical behavior, and a storage cycle is shown in Fig. 3b. The magnified area in Fig. 3b shows the storage operation in detail. Vw = 2.4 V with a pulse width of 140 ms, and Vre = 3 V with a pulse width of 100 ms, i.e., Vw rises 20 ms earlier and falls 20 ms later than Vre. One should note that Id has a steep pulse during a storage operation. Since it synchronizes with Vre, this is mainly due to the parasitic capacitance between the gate electrode and the capacitor. After the storage operation completes and the capacitor is charged to 2.4 V, T1 is then turned off by applying a negative Vre (−3 V), and Vw is set to 0 V. Thus, the voltage potential on the capacitor entirely controls Id of T2, without the influence of Vw. During the 10 s holding time, the output current Id decreases from 302 to 292 nA, approximately a 3% loss. It indicates that most of the charge stored in the capacitor can be maintained over a 10 s period, which keeps its voltage potential nearly constant and provides persistent control of the channel current in T2. Such charge storage persists even the holding time is extended to 100 s with a loss of Id less than 10% (Fig. S5). Reproducibility tests show that Id in T2 remains nearly constant after more than 100 cycles (Fig. S6). Such desirable storage characteristics indicate that, upon tuning Vw and Vx, different values of Id in T2 could be obtained and maintained with an acceptable loss in 10 s, which provides various differentiable states.

To demonstrate this, we first explored the electrical characteristics of T2. Figure 3c shows the output characteristics with Vg ranging from 2.4 to 3.0 V in 0.1 V increments, where one electrical probe is added separately to apply Vg directly to T2 as Vw (Fig. S7a). A relatively small Vx is applied to obtain linear IdVd output characteristics. Then Vw is fixed at 2.4 V, and Vx varies from 0.05 to 0.35 V in 0.05 V increments. Figure 3d shows Idt curves (similar to that in Fig. 3b) under different applied Vx values. For each Idt curve, Vx is fixed to monitor the decrease of Id during one cycle (~10 s) to tell if the Id at each level can be distinguished without overlap with neighboring states. The right graph shows the variation in Id during one cycle. We then investigated the corresponding transfer characteristic, as plotted in Fig. 3e. Vx is fixed from 0.05 to 0.35 V in 0.05 V increments while Vg varies from 2.4 to 3 V, in which range the IdVg curves are all nearly linear. Figure 3f again shows the measured Idt curves in which Vx is fixed at 0.1 V, and Vw pulse varies from 2.4 to 3 V in 0.1 V increments. Like the results in Fig. 3d, the Id at each level can be distinguished in one cycle. In Fig. 3d, f, it is noteworthy that there remain charges on the capacitor at the beginning time due to the previous cycle’s operation, so that each Idt curve has an initial value equals to that after 10 s retention time.

As illustrated in Fig. 4a, we used two nearly identical 2T–1C cells to demonstrate a simple MAC operation. The sources of the two T2 cells are connected to sum up Id1 and Id2. Figure 4b shows that when a test step-waveform is applied to Vx, and Vw is set as various values, Id from T2 can be accurately controlled. Vx ranges from 0.05 to 0.35 V in 0.05 V increments during every test cycle, and the weighted voltage Vw ranges from 2.4 to 3.0 V in 0.1 V increments. The waveform Vx exhibits eight voltage levels (3 bits) with a pulse width of 0.1 s, while Vw also exhibits eight levels, spanning 7 voltage levels plus a zero level. This measurement imitates when Vw is stored in the 1T–1C unit, a series of operations can be performed to Vx to accomplish multiple calculations in a storage period. The overall speed depends on the response speed of T2 and the writing speed of T1. One should note that the output Id changes almost simultaneously with the input Vx, indicating a fast operation speed. The calculation speed depends on the response speed of the transistor T2, which is mainly determined by the cut-off frequency $${f}_{{\rm{T}}}=\frac{{g}_{{\rm{m}}}}{2\pi {C}_{{\rm{G}}}}$$, where $${g}_{{\rm{m}}}$$ is the transconductance, $${C}_{{\rm{G}}}$$ is the equivalent gate capacitance41. Thus the upper limit of $${f}_{{\rm{T}}}$$ approximately equals 127.47 kHz for our current transistor scale (details see Fig. S8), which can act as a reference value for the calculation speed. It is much lower than previously reported MoS2 RF devices42,43, mainly because the $${C}_{{\rm{G}}}$$ is significantly influenced by the device size and overlap region of the gate electrode. Thus the speed improvement has a large room through fabrication optimization and further down-scaling.

We have demonstrated storage and calculation capabilities with our 2T–1C cell. We now demonstrate how to implement a MAC operation in detail. Based on the above electrical characterization of a MoS2 FET, we can obtain linear IdVx curves at small Vx, which approach zero when Vx = 0. To realize the multiplication function between Id and the production of Vw and Vx, a linear correlation between Id and Vw is also anticipated, i.e., a linear transfer characteristic. However, similar to previous literature results44,45,46, Id has a quadratic dependence on Vw, despite under a relatively low drain voltage regime. To achieve the required linearity, we can propose a recalculated weight

$${W}_{c}={({V}_{w}-1.9)}^{2}+0.3$$
(1)

Now, Id and the product of Wc and Vx can fulfill the requirements of multiplication operation, i.e., $${I}_{{\rm{d}}}=\bar{k}{W}_{{\rm{c}}}{V}_{{\rm{x}}}$$. The conversion between Wc and Vw can be realized by an additional peripheral circuit design (Fig. S9a). Figure 4c shows the output current Id as a function of Wc, where data was extracted from Fig. 4b, and the fixed input Vx ranges from 0 to 0.35 V in 0.05 V increments. For each Vx value, the output current Id and the recalculated Wc show satisfying linearity. We then further investigated the relationship between Vx and the output current Id for different Wc values. As shown in Fig. 4d, Id is plotted as a function of eight Vx values with different Wc values. For each Wc value, the output current Id and Vx are also relatively linear. Similar electrical characteristics for the second 2T-1C cell are shown in Fig. S9b, c. In the future, more linear transfer characteristics can be investigated by surface treatment and contact engineering of MoS2 FETs, or using gapless graphene as an alternative channel material for T2. So the additional peripheral circuit for linearity conversion can be simplified or removed to realize MAC operation more efficiently.

When we multiply each Wc (3-bit) with each Vx (3-bit), we obtain the mathematical product Y with 64 different values

$$Y={W}_{c}(i)\,\times\, {V}_{x}(j)\cdot (i,j=1,2,\ldots,8)$$
(2)

Figure 4e shows the measured Id values of the two 2T–1C cells as a function of their corresponding Y values separately. Id is relatively linear with Y for both cells. We then accumulate Y1 (cell 1) and Y2 (cell 2), defined as Ysum = Y1(i) + Y2(j) (i, j = 1, 2, …, 64), while the corresponding sum of the output current is defined as Isum = Id1(i) + Id2(j) (i, j = 1, 2, …, 64). Figure 4f shows a linear relationship between Isum and Ysum.

Thus, we have shown that MAC operations can be successfully performed based on our MoS2 2T–1C units. Furthermore, during the retention period, it is enough to implement multiple MAC operations upon inputting a sequence of Vx on T2. Thus our 2T–1C MoS2 device can be potentially used for in-situ training that can significantly improve the recognition accuracy of neural networks47. Therefore, our results suggest a potential path of 2D semiconductors for future post-Moore applications.

Finally, we built a fully connected neural network (FNN) model with a 3-layer network for handwritten digit recognition. As shown in Fig. 5a, the 400 input neurons correspond to the 20 × 20 pixels in one image while 10 output neurons corresponded to the recognition of digits 0–9, respectively. Here, each pixel has a grayscale value from 0 and 255 (8 bits). We used 4000 images to train the simulation model and another 1000 images for testing.

To process the 8-bit grayscale data, we established an 8-bit MAC composed of 32 2T/1 C cells (Fig. S11). The trained Win (weight of the simulation model) corresponded to quantized voltage with 256 levels (8-bit) stored in the cells. The FNN structure is shown in Fig. S12. Each 8-bit MAC works as a neuron to process the input grayscale data for each pixel. The complete FNN diagram consists of 400 × 20 neurons to form forward propagation from the input layer to a hidden layer. We used back-propagation to train our FNN simulation (see Supplementary Notes for more details). A flowchart for the training and test is shown in Fig. S13. After the FNN completed 100 training epochs against 4000 handwritten images, we performed a recognition test using 1000 handwritten images. The average recognition accuracy of our neural network simulation model reached 90.3%. Figure 5b shows the recognition confusion matrix for the 1000 images test. Figure 5c shows the relationship between recognition rate and training epoch, where the recognition rate rises quickly during the initial 10 training epochs primarily due to a large number of training images.

Considering that the size of an 8-bit grayscale input image occupies too many 2T–1C cells, we attempted to reduce the bit depth of the input grayscale images. We find that when an 8-bit input grayscale image is reduced to 1-bit, there is no evident decrease in recognition rate (Fig. 5d). We also simulated the influence of noise in our neural network by randomly choosing pixels and resetting them to random values. As shown in Fig. 5e, the in-set displays images with 0% and 30% noise levels. In the simulation, each well-trained weight is a 32-bit floating type by ex situ training, and it needs to be quantized to meet the finite weight levels. When the trained weights are re-quantized from 8 bits to 1 bit, as shown in Fig. 5f, we find that a 16-level (4-bit) weight is sufficient for our neural network to reach high recognition accuracy. The in-set in Fig. 5f shows the interval distribution of the 20 × 200 quantized 16-level weights (the quantized 256-level weights are shown in Fig. S14). Figure 5g shows a color map of the trained weights after being quantized to 16-levels. The size of the colormap is $$20\times 200$$. These results suggest that two 2T–1C cells are enough for a neuron to store a 4-bit quantized weight.

In conclusion, we experimentally demonstrated an in-memory computing architecture that integrates MoS2 FETs in a 2T–1C configuration for MAC operations. Owing to the large current on-off ratio of MoS2 FETs, the charge stored on the capacitor leaks slowly to present a long retention time so that a multi-level voltage can be retained. Based on the electrical characteristics of MoS2 FETs and an additional peripheral circuit, the analog multiplication operation can be realized with a re-calculated weight parameter. By connecting two or more 2T–1C unit cells in parallel, the output current is summed to provide the accumulation portion of a MAC operation. In addition, a neural network model was built based on the experimental data to provide image recognition with an average 90.3% accuracy. Our MoS2 2T–1C circuit is still a prototype device at the current research stage, and its performance requires further improvement by optimizing material quality and fabrication. Nevertheless, our demonstrated results offer a promising research platform for in-memory computation and in situ training of neural networks.

## Methods

### Fabrication of MoS2 2T–1C cell arrays

Device fabrication begins by using photolithography (Microwriter ML3) to pattern the source/drain region and bottom capacitor plate on a monolayer MoS2 film grown on a sapphire substrate. The channel width/length of T1 and T2 are defined as 30/20 and 90/20 μm using ICP etching, respectively. Next, a seed layer (3 nm SiO2) was evaporated on the MoS2 film using electron beam evaporation, followed by annealing (200 °C, 10 min) in a high vacuum furnace to remove any resist residue and ensure low contact resistance. A 20-nm-thick HfO2 layer was then deposited using atomic layer deposition at 180 °C. The oxide stack containing 3 nm SiO2 and 20 nm HfO2 serves as a high-k gate dielectric of MoS2 FETs and the capacitor’s insulating layer as well. CF4/Ar plasma etching was used to form an interconnect opening in the dielectric layer to connect the source in T1 to the gate in T2. Finally, 30 nm Au was deposited as gate electrodes of the MoS2 FETs and the top plate of the capacitor.

### Characterization and electrical measurements

All measurements were gathered in an ambient environment at room temperature. For capacitor characterization, capacitance–voltage curves were measured with a Keysight E4990A Impedance Analyzer. The MoS2 FETs were characterized using a semiconductor parameter analyzer (Agilent B1500A). For dynamic memory and 2T–1C cell measurements, the Agilent B1500A was used for supplying voltage signal and detecting the channel current, and a waveform generator (Aligent 33260A) was also used to supply waveforms to the test circuit, while an oscilloscope (DS 1054Z) was used for capturing output signal voltage.

## Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

## Code availability

The codes used for simulation and data plotting are available from the corresponding authors upon reasonable request.

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## Acknowledgements

This work was supported by the National Key Research and Development Program (2016YFA0203900), Shanghai Municipal Science and Technology Commission (18JC1410300), Innovation Program of Shanghai Municipal Education Commission (2021-01-07-00-07-E00077), and National Natural Science Foundation of China (61925402, 61851402, 62090032, and 61874031).

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All authors discussed the results and commented on the paper.

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Correspondence to Peng Zhou or Wenzhong Bao.

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Peer review information Nature Communications thanks Jianhua Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wang, Y., Tang, H., Xie, Y. et al. An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations. Nat Commun 12, 3347 (2021). https://doi.org/10.1038/s41467-021-23719-3

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