Key Takeaways & Executive Findings
- •• • The device achieves a photocurrent dissymmetry factor (g_ph) dynamically tunable up to -0.06 under positive bias, enabling hardware-level synaptic weight adjustment for enhanced image contrast. • • Under negative bias, the device operates as a highly sensitive CPL detector, enabling efficient chiroptical signal acquisition and imaging for cancer cell samples. • • In OCNN-based cancer detection, CPL detection accuracy reaches 83%, approaching the theoretical 87% and outperforming natural light detection at 65%. • • The dual-mode operation (negative bias for detection, positive bias for synaptic processing) integrates sensing and computation in a single hardware platform, reducing signal transmission losses and improving processing efficiency.
Abstract
Conventional cancer diagnostic techniques, such as tissue sampling and microscopy, are invasive and prone to misdiagnosis, driving the need for non-invasive, precise alternatives. Chiral biophotonics, exploiting circularly polarized light (CPL), offers unique polarization-selective interactions with biological tissues, enabling higher imaging contrast and molecular-level discrimination. However, current CPL detection technologies are passive and single-mode, lacking dynamic tunability and parallel processing capabilities. Meanwhile, AI-assisted diagnostics rely on separated sensing and computing units, suffering from poor integration and transmission inefficiency. Here, we report a near-infrared (NIR) chiral organic synaptic photodiode with electrically tunable dual-mode operation, enabling simultaneous CPL detection and neuromorphic processing. Under negative bias, the device operates as a highly sensitive CPL detector for chiroptical signal acquisition. Under positive bias, it exhibits history-dependent synaptic behavior with photocurrent dissymmetry factor (g_ph) dynamically tunable up to -0.06. By integrating this device into an optical convolutional neural network (OCNN), we achieved intelligent cancer detection with CPL-based imaging. Experimental results demonstrate that CPL detection accuracy reaches 83%, approaching the theoretical 87%, significantly outperforming natural light detection at 65%. The device enhances image contrast and feature extraction, laying a foundation for intelligent, adaptive diagnostic systems.
1. Introduction
Conventional cancer diagnostics rely on invasive tissue sampling and microscopy, which carry risks of misdiagnosis and patient discomfort. Chiral biophotonics, particularly circularly polarized light (CPL), has emerged as a non-invasive alternative, exploiting polarization-selective interactions with biological tissues to achieve higher imaging contrast and molecular-level discrimination. However, existing CPL detection systems are passive and single-mode, lacking dynamic tunability and the ability to process complex optical information in parallel. Their insufficient anisotropic factors and limited tunability severely restrict practical effectiveness in accurately identifying biological signals.
In parallel, AI-assisted diagnostic technologies, including smartphone-based multimodal imaging and memristor-based biosensors, have demonstrated potential for high-throughput data interpretation. Yet these systems rely on separated sensing and computing units, leading to poor integration and limited signal transmission efficiency. Optical convolutional neural networks (OCNNs) offer high-speed parallel processing but typically lack intrinsic chirality recognition. The urgent need is for integrated devices that combine high-sensitivity CPL detection with real-time signal processing. This work addresses that bottleneck by developing a dual-mode electrotunable NIR chiral organic synaptic photodiode that simultaneously performs CPL detection and neuromorphic processing, enabling intelligent cancer detection within a unified hardware platform.
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Liuting Shan, Yongjie Cai, Lixuan Liu, Weilong Huang, Xiaolong Li, Shuai Liu, Yang Yang, Zhixiang Wei, Jingchang Sun, Huipeng Chen (2026). Dual-mode electrotunable near-infrared chiral organic synaptic photodiodes for intelligent cancer detection. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3786-8
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Frequently Asked Questions
What is the operational mechanism enabling dual-mode behavior in the photodiode, and how does bias polarity control the transition between detection and synaptic modes?
Under negative bias, the device operates as a highly sensitive CPL detector, generating photocurrent responses (I_L and I_R) that reflect the chiral characteristics of the incident light. Under positive bias, the device exhibits history-dependent synaptic behavior, where the photocurrent dissymmetry factor (g_ph) can be dynamically tuned up to -0.06, allowing hardware-level synaptic weight adjustment. This bias-controlled dual-mode operation enables seamless integration of detection and processing functions.
How does the device achieve a photocurrent dissymmetry factor (g_ph) of -0.06, and what are the implications for imaging contrast in cancer detection?
The g_ph of -0.06 is achieved through the chiral organic semiconductor's selective absorption of CPL, combined with the synaptic behavior under positive bias that dynamically adjusts the photocurrent difference. This tunability enhances image contrast by amplifying the chiral-specific signals, as demonstrated by the improved clarity of cancer cell images under CPL compared to natural light.
What is the measured accuracy improvement of CPL-based detection over natural light detection, and how does it compare to theoretical limits?
In the OCNN-based system, CPL detection accuracy reaches 83% after multiple iterative cycles, approaching the theoretical value of 87%. This significantly outperforms natural light detection, which achieves only 65% accuracy. The improvement is attributed to the enhanced image contrast and feature extraction enabled by the chiral synaptic device.
What are the scalability and integration challenges for translating this device into clinical diagnostic systems?
The device demonstrates proof-of-concept in laboratory settings, but clinical translation requires addressing scalability of fabrication, integration with existing imaging platforms, and validation on larger patient cohorts. The dual-mode operation simplifies hardware by eliminating separate sensing and computing units, potentially reducing system complexity and cost, but further engineering is needed to ensure reproducibility and reliability in clinical environments.
How does the device's performance under CPL compare to conventional optical detection methods in terms of sensitivity and specificity?
The device's sensitivity to CPL, combined with synaptic processing, enables higher imaging contrast and molecular-level discrimination compared to conventional optical methods. The 83% accuracy in cancer detection, approaching the theoretical 87%, indicates high specificity, while the enhanced contrast under CPL conditions suggests improved sensitivity for detecting subtle chiral differences in biological tissues.
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