Stanford Advisors


All Publications


  • A Scalable Perovskite Platform With Multi-State Photoresponsivity for In-Sensor Saliency Detection ADVANCED MATERIALS Xing, X., Tripathi, A., Ng, S., Yantara, N., Gong, Y., Wu, Q., Tay, Y., Lew, W., Chai, Y., Basu, A., Mathews, N. 2026: e73243

    Abstract

    Artificial vision systems are increasingly central to edge intelligence, yet they often suffer from high data latency and energy consumption due to sensor-processor separation. In-sensor computing (ISC) provides a promising solution by integrating sensing and computation. However, current ISC devices remain constrained by scalability, uniformity, and processability. Here, we address these limitations via a reconfigurable perovskite-photovoltaic platform that can be facilely processed from solutions. This architecture allows precise, reconfigurable photoresponsivity tuning with ultra-low variability and supports fabrication on both rigid and flexible substrates. The device exhibits up to ±1120 mA W- 1 photoresponsivity and 1000 programmable states, with excellent air stability (30 days) and thermal reliability (80°C). The scalability of these devices is demonstrated via a proof of concept 32 × 32 array. The excellent uniformity and programmability of the array are utilized in energy-efficient face detection applications (achieving 95.2% sensitivity and 4.51 × speedup for subsequent computation) in addition to image feature extraction and MNIST digit recognition tasks (96.97% accuracy). Compared to previous ISC implementations, our system offers enhanced tunability, fabrication scalability, and functional stability. These results establish a practical perovskite-based ISC platform, offering new avenues for intelligent computing systems in robotics, wearable electronics, and neuromorphic vision.

    View details for DOI 10.1002/adma.73243

    View details for Web of Science ID 001753206200001

    View details for PubMedID 42057541

  • A Real-Time End-to-End Event-Based Tactile Sensing System IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS Lu, Y., Seong, K., Ng, S., Varku, S., Basu, A., Mathews, N., Kim, T. 2026
  • Capacitive tuning of thyristor oscillators enables neuron-like signal amplification PHYSICAL REVIEW APPLIED Ng, S., Mathews, N., Fenollosa, R., Rubio-Magnieto, J., Bisquert, J. 2026; 25 (2)

    View details for DOI 10.1103/yhwd-t2wh

    View details for Web of Science ID 001686444700003

  • Halide Perovskite Retinomorphic In-Sensor Computing ACS ENERGY LETTERS Ng, S., Yantara, N., Mathews, N. 2025
  • Optical Learning and Reconfigurable Logic Utilizing Halide Perovskite Thin Film Transistors SMALL Nirmal, A., Tay, D., Yantara, N., Timothy, S., Sharma, D., Tay, Y., Mathews, N. 2025; 21 (25): e2409373

    Abstract

    Metal halide perovskites with their superior electronic properties, solution processibility, and scalability, are promising candidates for optoelectronic applications such as solar cells and LEDs. Although of importance for ubiquitous, intelligent electronics, reports on the application of thin film transistors (TFTs) fabricated from perovskites are sparse, primarily due to operational conditions limitations and stability issues. Precise control of composition, microstructure as well as novel chemical treatments have been proposed as solutions to this quandary. Here, a room temperature operational n-type solution-processed Cs0.05(MA0.15FA0.7)Pb(Br0.5I2.4) triple cation transistor enabled using tin-doped indium oxide as source/drain contacts is reported. Optical learning is demonstrated by exploiting the inherent photo response of the perovskite channel and gate bias modulation capability of such perovskite TFT optical pixels. The pixels show modulatable learning and forgetting behavior as demonstrated by the training of alphabets. Noise-free optoelectronic relearning is also demonstrated. Finally, the intelligent transistor is also utilized to demonstrate reconfigurable logic, switchable between 'OR' and 'AND' states, demonstrating the versatility of the halide perovskite pixel.

    View details for DOI 10.1002/smll.202409373

    View details for Web of Science ID 001479869900001

    View details for PubMedID 40318174