Pressure‐Perceptive Actuators for Tactile Soft Robots and Visual Logic Devices

Abstract In this work, we present two case studies on the utilization of advanced nanoprobing on 20nm logic devices at contact layer to identify the root cause of scan logic failures. In both cases, conventional failure analysis followed by inspection of passive voltage contrast (PVC) failed to identify any abnormality in the devices. Technology advancement makes identifying failure mechanisms increasingly more challenging using conventional methods of physical failure analysis (PFA). Almost all PFA cases for 20nm technology node devices and beyond require Transmission Electron Microscopy (TEM) analysis. Before TEM analysis can be performed, fault isolation is required to correctly determine the precise failing location. Isolated transistor probing was performed on the suspected logic NMOS and PMOS transistors to identify the failing transistors for TEM analysis. In this paper, nanoprobing was used to isolate the failing transistor of a logic cell. Nanoprobing revealed anomalies between the drain and bulk junction which was found to be due to contact gouging of different severities.

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Simplifying Soft Robots Through Adhesive-backed Fabrics

2019 2nd IEEE International Conference on Soft Robotics (RoboSoft) ◽

10.1109/robosoft.2019.8722725 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tiana Miller-Jackson ◽

Rainier F. Natividad ◽

Chen-Hua Yeow

Keyword(s):

Soft Robots

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All-electrical control of scaled spin logic devices based on domain wall motion

2020 IEEE International Electron Devices Meeting (IEDM) ◽

10.1109/iedm13553.2020.9372127 ◽

2020 ◽

Author(s):

E. Raymenants ◽

D. Wan ◽

S. Couet ◽

L. Souriau ◽

A. Thiam ◽

...

Keyword(s):

Domain Wall ◽

Wall Motion ◽

Domain Wall Motion ◽

Electrical Control ◽

Logic Devices ◽

Spin Logic

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Real-Time Simulation for Control of Soft Robots With Self-Collisions Using Model Order Reduction for Contact Forces

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3064247 ◽

2021 ◽

Vol 6 (2) ◽

pp. 3752-3759

Author(s):

Olivier Goury ◽

Bruno Carrez ◽

Christian Duriez

Keyword(s):

Real Time ◽

Model Order Reduction ◽

Order Reduction ◽

Contact Forces ◽

Model Order ◽

Soft Robots ◽

Real Time Simulation ◽

Time Simulation

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A Pneumatic Generator Based on Gas-Liquid Reversible Transition for Soft Robots

Actuators ◽

10.3390/act10050103 ◽

2021 ◽

Vol 10 (5) ◽

pp. 103

Author(s):

Guolong Zhang ◽

Guilin Yang ◽

Yimin Deng ◽

Tianjiang Zheng ◽

Zaojun Fang ◽

...

Keyword(s):

Smart Materials ◽

Load Capacity ◽

Angular Displacement ◽

Quadratic Function ◽

Air Cooling ◽

Soft Robots ◽

Reversible Transformation ◽

Exponential Term ◽

Reversible Transition ◽

Gas Volume

The soft robots actuated by pressure, cables, thermal, electrosorption, combustion and smart materials are usually faced with the problems of poor portability, noise, weak load capacity, small deformation and high driving voltages. In this paper, a novel pneumatic generator for soft robots based on the gas-liquid reversible transition is proposed, which has the advantages of large output force, easy deformation, strong load capacity and high flexibility. The pressure of the pneumatic generator surges or drops flexibly through the reversible transformation between liquid and gas phase, making the soft actuator stretch or contract regularly, without external motors, compressors and pressure-regulating components. The gas-liquid reversible-transition actuation process is modeled to analyze its working mechanism and characteristics. The pressure during the pressurization stage increases linearly with a rate regulated by the heating power and gas volume. It decreases exponentially with the exponential term as a quadratic function of time at the fast depressurization stage, while with the exponential term as a linear function of time at the slow depressurization stage. The drop rate can be adjusted by changing the gas volume and cooling conditions. Furthermore, effectiveness has been verified through experiments of the prototype. The pressure reaches 25 bar with a rising rate of +3.935 bar/s when 5 mL weak electrolyte solution is heated at 800 W, and the maximum depressurization rate in air cooling is –3.796 bar/s. The soft finger actuated by the pneumatic generator can bend with an angular displacement of 67.5°. The proposed pneumatic generator shows great potential to be used for the structure, driving and sensing integration of artificial muscles.

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Task space adaptation via the learning of gait controllers of magnetic soft millirobots

The International Journal of Robotics Research ◽

10.1177/02783649211021869 ◽

2021 ◽

pp. 027836492110218

Author(s):

Sinan O. Demir ◽

Utku Culha ◽

Alp C. Karacakol ◽

Abdon Pena-Francesch ◽

Sebastian Trimpe ◽

...

Keyword(s):

Human Body ◽

Degrees Of Freedom ◽

Bayesian Optimization ◽

Small Scale ◽

Soft Robots ◽

Modeling And Control ◽

Walking Gait ◽

Physical Experiments ◽

Efficient Learning ◽

Task Environments

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

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