Key Takeaways & Executive Findings
- •• • The device achieves a recognition accuracy of 95.03% on the MNIST dataset, demonstrating high fidelity in in-sensor optical processing and classification, which is critical for deploying neuromorphic systems in real-world pattern recognition tasks. • • Hierarchical synaptic plasticity is realized through distinct weight modulation timescales: electrical gating induces rapid, short-term changes, while UV-excited long-afterglow optical stimuli produce persistent, long-term potentiation, enabling multi-scale learning dynamics. • • The integration of a long-afterglow material converts transient UV excitation into sustained visible emission, extending the optical stimulus duration beyond milliseconds and facilitating cascade interactions between optical and electrical pathways for enhanced plasticity tunability. • • The dual-output architecture concurrently provides visible-light emission for direct display and electrical signal processing, achieving seamless integration of sensing, display, and computation within a single device framework, which reduces system complexity and energy overhead in neuromorphic hardware.
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Abstract
Emulating biological synaptic plasticity is essential for advancing artificial intelligence. However, in most existing synaptic phototransistors to date, both electrical and optical stimuli induce weight modulation within a comparable dynamic range, limiting plasticity tunability and richness. Here, we report a synaptic phototransistor that enables distinct weight modulation in response to electrical and optical inputs, achieving hierarchical, multi-scale plasticity with concurrent visible-light emission for direct display. The device integrates a long-afterglow material that converts transient ultraviolet (UV) excitation into persistent visible emission, serving as a temporally extended, memory-like optical stimulus. Compared to direct electrical gating, this delayed optical activation of the optoelectronic channel induces weight modulation on a significantly longer timescale, enabling hierarchical plasticity and cascade interactions between optical and electrical pathways. The dual-output architecture allows simultaneous optical visualization and electrical signal processing, effectively integrating optical perception with in-sensor computation. Leveraging this design, we demonstrate a UV-resolvable neural network capable of direct image display and achieving a recognition accuracy of 95.03% for handwritten digits. This work establishes a new paradigm for multimodal neuromorphic systems by seamlessly integrating sensing, display, and computation within a unified in-sensor architecture.
1. Introduction
Neuromorphic devices, including artificial synapses and neurons, have attracted significant attention owing to their high information processing efficiency and ultralow energy consumption. By emulating the computational principles of the human brain, they provide a promising hardware foundation for next-generation intelligent systems, particularly in applications such as image recognition, speech processing, and multimodal sensing. Artificial synapses, responsible for modulating signal transmission strength between neurons, are essential for implementing learning and memory functions. Over the past two decades, substantial progress has been achieved in the development of artificial synaptic devices, including field-effect transistors, phase-change memory, and ferroelectric memory. These devices offer analog, non-volatile, and energy-efficient conductance modulation and are essential for implementing brain-inspired learning mechanisms like spike-timing-dependent plasticity (STDP) and Hebbian learning. In particular, synaptic phototransistors have emerged as promising candidates for neuromorphic sensing systems, owing to their intrinsic integration of optical sensing and synaptic plasticity. Coupled with the high bandwidth, dense interconnectivity and ultralow energy consumption, synaptic phototransistors provide a compact and efficient platform for realizing bioinspired vision systems with in-sensor learning and adaptive capabilities.
However, most existing synaptic phototransistors exhibit comparable dynamic ranges in response to optical and electrical stimuli, thereby limiting the tunability of synaptic plasticity. This limitation arises from their reliance on similar physical mechanisms within a shared transport channel, such as field-induced carrier modulation and trap-state filling. Such mechanism-level overlap not only restricts the tunability of plasticity but also impedes the realization of hierarchical or modality-specific learning. Despite recent efforts to incorporate multimodal inputs, most reported optoelectronic synaptic transistors still yield only a single, undifferentiated response, failing to exploit the distinct temporal and spectral characteristics of optical and electrical stimuli. The device reported here addresses this bottleneck by integrating a long-afterglow material that converts transient UV excitation into persistent visible emission, creating a temporally extended optical stimulus that activates the optoelectronic channel on a significantly longer timescale than direct electrical gating. This delayed optical activation enables hierarchical plasticity and cascade interactions between optical and electrical pathways, while the dual-output architecture allows simultaneous optical visualization and electrical signal processing, effectively integrating optical perception with in-sensor computation.
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ZHANG Wenhui, MA Xiao, HUANG Weilong, YANG Chuiying, YANG Peng, HUANG Bingle, CHEN Huipeng (2025). UV-Responsive Phototransistor for Hierarchical Synaptic Plasticity. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3576-6
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Frequently Asked Questions
What is the measured recognition accuracy of the UV-resolvable neural network on the MNIST dataset, and how does it compare to conventional software-based approaches?
The device achieves a recognition accuracy of 95.03% on the MNIST dataset, which is competitive with software-based neural networks (typically >98%) but with the advantage of in-sensor processing, reducing latency and energy consumption associated with data transfer.
What are the failure mechanisms under prolonged UV exposure or electrical stress, and what is the operational lifetime of the device?
The paper does not provide explicit degradation data, but the long-afterglow material may suffer from photobleaching or trap-state saturation under continuous UV excitation. Electrical stress could lead to charge trapping in the dielectric, shifting threshold voltage. Accelerated aging tests are required to quantify operational lifetime.
What is the cost parity of this phototransistor against legacy silicon-based synaptic devices, and what are the scalability bottlenecks for wafer-scale integration?
The device employs solution-processed long-afterglow materials and organic semiconductors, potentially lowering material costs compared to silicon. However, scalability is limited by the uniformity of the long-afterglow layer and the integration of dual-output architecture. Wafer-scale fabrication would require advanced deposition techniques and encapsulation to prevent moisture degradation.
How does the long-afterglow material's persistent emission affect the device's dynamic range and response time, and can it be tuned for different wavelengths?
The long-afterglow material extends the optical stimulus duration from milliseconds to seconds, enhancing synaptic plasticity timescales. The emission wavelength is determined by the dopant, typically in the visible range (e.g., 500-600 nm). Tuning for specific wavelengths requires synthesis of different afterglow phosphors, which may affect compatibility with the optoelectronic channel.
What is the energy consumption per synaptic event, and how does it compare to biological synapses and other neuromorphic hardware?
The paper does not report energy per event, but the device operates with low power due to in-sensor computation. Biological synapses consume ~10 fJ per event; this device likely consumes picojoules to femtojoules, depending on the operating voltage and current. Further optimization is needed for ultralow-power operation.
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