Programmable van der Waals Heterojunction for Multifunctional Integration
An Gui , Yutao Song , Tong Li , Kaiwen Gong , Haozhe Li , Haoran Mu , Rong Yang , Lei Liao , Guangyu Zhang , Shenghuang Lin
SmartMat ›› 2026, Vol. 7 ›› Issue (4) : e70096
Machine vision systems in real-world applications require devices capable of adapting to multiple operational modes, including high-sensitivity photodetection, neuromorphic synaptic behavior, and optical memory. However, integrating these distinct optoelectronic functionalities into a single device presents a challenge due to their conflicting requirements for photoresponse time and retention. Here, a gate-programmable multifunctional transistor based on a MoS2/MAPbBr3 van der Waals (vdW) heterostructure is fabricated. By using the gate field to tune the interfacial electric field and trapping/release kinetics associated with shallow- and deep-level states, the device can be programmed to operate as a photodetector, synaptic transistor, and gate-bias-assisted multilevel optical memory. Under a positive gate voltage (VG), a pronounced photogating effect enables photodetection with responsivity (R) of ∼2397 A/W. At VG = –20 V, the device switches to synaptic mode. At a more negative gate bias, the device enters a gate-bias-assisted optical-memory mode under sustained gate bias. To illustrate the utility of this programmable multifunctionality, frequency-encoded image recognition is demonstrated with 96.4% accuracy. In addition, a device-enabled reservoir-computing (RC) scheme with software-assisted readout improves classification accuracy by ~20% through deep delay-based processing. This work demonstrates a multimodal transistor architecture that integrates multiple optoelectronic functions in a single device and suggests a compact hardware route toward adaptive in-sensor optical information processing.
memories / multifunctional / photodetectors / reservoir computing / synapses
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
| [65] |
|
| [66] |
|
2026 The Author(s). SmartMat published by John Wiley & Sons Australia, Ltd on behalf of Tianjin University.
/
| 〈 |
|
〉 |