neutouchNANJING UNIVERSITY
NEUTOUCH LAB / NANJING UNIVERSITY

Teaching
machines
to feel.

Exploring tactile intelligence through skin-inspired sensing, neuromorphic electronics, and the seamless integration of matter and computation.

Explore our research ↗Our approach ↗
FIG. 01 — INTERACTIVE TACTILE RESPONSE FIELD
MOVE YOUR POINTER TO APPLY CONTACT
PHYSICS → SENSING → INTELLIGENCEINTERACTIVE RESEARCH VISUALIZATION / 001
AT THE INTERSECTION / 01

At NeuTouch Lab, we study how physical contact becomes meaningful information — connecting mechanics, emerging devices, electronic circuits, and intelligent systems to build the next generation of tactile technology.

01 / RESEARCH DIRECTIONS

Four scales.
One sense of touch.

Our research moves across scales, from material deformation and sensor physics to efficient hardware and intelligent perception.

01

Neuromorphic Circuits

↗
Concept illustration: Event-driven neural encoding diagram

Event-driven circuits that encode and process touch close to the sensor, inspired by biological neural systems.

02

Emerging Neuromorphic Devices

↗
Concept illustration: Multilayer tactile synaptic device diagram

Synaptic transistors that integrate sensing, memory and learning within a single physical element.

03

Hardware–Software Co-Design

↗
Concept illustration: Sensor and on-device computation diagram

Joint design of sensors, circuits and learning algorithms for efficient tactile intelligence.

04

Mechanics of Skin-like Materials

↗
Concept illustration: Flexible skin finite-element deformation diagram

Modeling soft-material deformation under contact to guide sensor design and interpret tactile stimuli.

OUR VISION

What if machines could feel the world — not just see it?

02 / RESEARCH FRAMEWORK

From contact
to cognition.

From physical signals to meaningful perception — four connected stages in a tactile intelligence system.

A continuous conceptual flow from a robotic fingertip touching a flexible sensor, through tactile signal encoding and neuromorphic computing, to robotic object understanding and a feedback loop.
FIGURE 02 / THE TACTILE INTELLIGENCE PIPELINEConcept illustration · not experimental dataView full figure ↗
  1. 01
    CONTACT & SIGNAL

    Sensing

    Skin-like tactile sensors convert pressure, contact position and deformation into measurable electrical signals.

    Electronic skin · Tactile arrays
  2. 02
    EVENT REPRESENTATION

    Encoding

    Continuous tactile signals become temporal features and sparse, event-driven spikes for efficient processing.

    Temporal features · Spike trains
  3. 03
    NEUROMORPHIC PROCESSING

    Computing

    Devices, circuits and algorithms cooperate to extract tactile features close to the sensor.

    Neuromorphic circuits · Edge AI
  4. 04
    PERCEPTION & FEEDBACK

    Understanding

    Tactile features inform object and contact-state recognition, with feedback guiding robotic interaction.

    Object recognition · Closed loop

A unified conceptual workflow for explaining research directions; not a claim that all depicted systems have been experimentally demonstrated by the lab.

03 / SELECTED PUBLICATIONS

Research made
tangible.

Selected work by Fengyuan Liu and collaborators. Explore experimental figures and the publication archive below.

RESEARCH HIGHLIGHT / 001ORIGINAL EXPERIMENTAL FIGURES
Original Figure 1: electronic skin on a robotic hand, biological and artificial tactile neural pathways
Science Robotics · 2022 · Original research

Printed synaptic transistor–based electronic skin for robots to feel and learn

A flexible computational electronic skin combines printed synaptic transistors, event-driven sensing and spiking neuron circuits to enable tactile responses through local learning.

12 × 14Printed transistor array
100%Reported device yield
2022Science Robotics
Read the research paper ↗
EXPERIMENTS FROM THE SAME STUDY

Devices & experimental validation

Three experiments connecting flexible devices, array uniformity and tactile learning in a robotic system.

Figure 2g presentation with background edited to white; original available on click
01 / FLEXIBLE DEVICE

Flexible printed devices

Printed devices on a polyimide substrate can bend with their support. The study characterizes device performance before and after mechanical bending.

Original Figure 2d through f, spatial maps of transistor on-current, threshold voltage and off-current
02 / ARRAY UNIFORMITY

Device array uniformity

Spatial maps of on-current, threshold voltage and off-current characterize device uniformity across the printed transistor array.

Original Figure 5f: robotic arm photographs before and after associative learning, showing acquired pain reflex
03 / ASSOCIATIVE LEARNING

Learning a tactile response

The robotic demonstration compares responses before and after associative learning, showing an acquired pain reflex in the artificial tactile neural pathway.

PUBLICATION ARCHIVE

Explore the published work.

Full list on Google Scholar ↗
Browse 14 publications · 2014–2025

2025

  1. Stochastic Nature of Large-Scale Contact Printed ZnO Nanowires Based Transistors

    F. Liu, A. Christou, R. Chirila, L. De Pamphilis, R. Dahiya

    Advanced Functional Materials 35 (2), 2412299

  2. Cognizant and Socially Aware Robotics

    F. Liu, L. K. John, R. Dahiya

    Computer 58 (10), 2025

  3. From printed devices to vertically stacked, 3D flexible hybrid systems

    F. Liu, A. Christou, A. S. Dahiya, R. Dahiya

    Advanced Materials 37 (10), 2411151

2024

  1. Toward all flexible sensing systems for next-generation wearables

    F. Liu, L. Lorenzelli

    Wearable Electronics 1, 137–149

2022

  1. Neuro-inspired electronic skin for robots

    F. Liu, S. Deswal, A. Christou, Y. Sandamirskaya, M. Kaboli, R. Dahiya

    Science Robotics 7 (67), eabl7344

  2. Printed synaptic transistor–based electronic skin for robots to feel and learn

    F. Liu, S. Deswal, A. Christou, M. Shojaei Baghini, R. Chirila, D. Shakthivel, et al.

    Science Robotics 7 (67), eabl7286

2021

  1. Development of a highly controlled system for large-area, directional printing of quasi-1D nanomaterials

    A. Christou, F. Liu, R. Dahiya

    Microsystems & Nanoengineering 7, 82

2020

  1. A flexible chip with embedded intelligence

    F. Liu, A. S. Dahiya, R. Dahiya

    Nature Electronics 3 (7), 358–359

  2. Monolayer MoSe₂-based tunneling field effect transistor for ultrasensitive strain sensing

    P. K. Dubey, N. Yogeswaran, F. Liu, A. Vilouras, B. K. Kaushik, R. Dahiya

    IEEE Transactions on Electron Devices 67 (5), 2140–2146

2019

  1. van der Waals contact engineering of graphene field-effect transistors for large-area flexible electronics

    F. Liu, W. T. Navaraj, N. Yogeswaran, D. H. Gregory, R. Dahiya

    ACS Nano 13 (3), 3257–3268

  2. Large-area soft e-skin: The challenges beyond sensor designs

    R. Dahiya, N. Yogeswaran, F. Liu, L. Manjakkal, E. Burdet, V. Hayward, et al.

    Proceedings of the IEEE 107 (10), 2016–2033

2018

  1. Piezoelectric graphene field effect transistor pressure sensors for tactile sensing

    N. Yogeswaran, W. T. Navaraj, S. Gupta, F. Liu, V. Vinciguerra, L. Lorenzelli, et al.

    Applied Physics Letters 113 (1)

2016

  1. Angle-selective perfect absorption with two-dimensional materials

    L. Zhu, F. Liu, H. Lin, J. Hu, Z. Yu, X. Wang, S. Fan

    Light: Science & Applications 5, e16052

2014

  1. Tunable, ultralow-power switching in memristive devices enabled by a heterogeneous graphene-oxide interface

    M. Qian, Y. Pan, F. Liu, et al.

    Advanced Materials 26 (20), 3275–3281

04 / PEOPLE

People behind
the science.

Meet the researchers building tactile intelligence at Nanjing University.

Prof. Fengyuan Liu
PRINCIPAL INVESTIGATOR

Prof. Fengyuan Liu

Associate Professor · Principal Investigator

Nanjing University

fengyuanliu@nju.edu.cn ↗

Lab members

Four master students · Two bachelor students
Haoyuan Qiu

Haoyuan Qiu

Master student

Mohan Cao

Master student

Dingkun Xia

Master student

Kaibo Wang

Master student

Yizhuo Zhao

Bachelor student

Jingqi Yin

Jingqi Yin

Bachelor student

05 / FROM THE LAB

Notes &
milestones.

People, ideas and events shaping the development of NeuTouch Lab.

OCT 2026Welcome master student Kaibo Wang to the group.↗
OCT 2026Fengyuan Liu attended the Micro/Nano Optical Technology and Application Conference in Chengdu.↗
SEP 2026Welcome bachelor students Yizhuo Zhao and Jingqi Yin.↗
SEP 2026Welcome master students Mohan Cao and Dingkun Xia.↗
Welcome master student Haoyuan Qiu to the group.
JUL 2026NeuTouch Lab is founded.↗
The NEUCODES project is completed. ↗
View original news archive ↗
06 / JOIN NEUTOUCH LAB

Curiosity
into discovery.

We welcome motivated researchers interested in circuits, mechanics, robotics, device physics, materials and cross-disciplinary tactile intelligence.

OPEN TO COLLABORATION

Build what touch can become.

Send your CV and a short statement of research interests to Fengyuan Liu to discuss opportunities in the group.

Circuit DesignTactile SensingRoboticsMechanicsDevices
Contact the lab ↗

Research opportunities

Across career stages and disciplines

  • Postdoctoral researchers

    Full-time research positions.

  • PhD students

    Doctoral research in tactile sensing.

  • Master students

    Master’s projects within the group.

  • Bachelor students

    Undergraduate research projects.

  • Research assistants

    Project-based research support.

International students

We welcome international students supported by a CSC scholarship or other fellowship schemes.