The approach toward a stretchable electronic substrate employs multiple soft polymer layers patterned around silicon chips, which act as surrogates for conventional electronics chips, to create a controllable stiffness gradient. Adding just one intermediate polymer layer results in a six-fold increase in the strain failure threshold enabling the substrate to be stretched to over twice its length before delamination occurs.

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Trilayer bending actuators are charge driven devices that have the ability to function in air and provide large mechanical amplification. The electronic and mechanical properties of these actuators are known to be functions of their charge state making prediction of their responses more difficult when they operate over their full range of deformation. In this work, a combination of state space representation and a two-dimensional RC transmission line model are used to implement a nonlinear time variant model for conducting polymer-based trilayer actuators. Electrical conductivity and Young's modulus of electromechanically active PEDOT conducting polymer containing films as a function of applied voltage were measured and incorporated into the model. A 16% drop in Young's modulus and 24 times increase in conductivity are observed by oxidizing the PEDOT. A closed form formulation for radius of curvature of trilayer actuators considering asymmetric and location dependent Young's modulus and conductivity in the conducting polymer layers is derived and implemented in the model. The nonlinear model shows the capability to predict the radius of curvature as a function of time and position with reasonable consistency (within 4%). The formulation is useful for general trilayer configurations to calculate the radius of curvature as a function of time. The proposed electrochemical modeling approach may also be useful for modeling energy storage devices.

Stretchable electronics have demonstrated promise within unobtrusive wearable systems in areas such as health monitoring and medical therapy. One significant question is whether it is more advantageous to develop holistic stretchable electronics or to integrate mature CMOS into stretchable electronic substrates where the CMOS process is separated from the mechanical processing steps. A major limitation with integrating CMOS is the dissimilar interface between the soft stretchable and hard CMOS materials. To address this, we developed an approach to pattern an elastomeric polymer layer with spatially varying mechanical properties around CMOS electronics to create a controllable material stiffness gradient. Our experimental approach reveals that modifying the interfaces can increase the strain failure threshold up to 30% and subsequently decreases delamination. The stiffness gradient in the polymer layer provides a safe region for electronic chips to function under a substrate tensile strain up to 150%. These results will have impacts in diverse applications including skin sensors and wearable health monitoring systems.

A B S T R A C T This paper attempts to demonstrate the applicability of artificial neural networks to the estimation of steel properties, cyclic strain-hardening exponent and cyclic strength coefficient, characterizing cyclic Ramberg-Osgood equation on the basis of monotonic tensile test properties. For this purpose, steel tensile data were extracted from the literature and two separate neural networks were constructed. One set of data was used for training the two networks and the remaining for testing purposes. Regression analysis and mean relative error calculation were used to check the accuracy of the system in the training and testing phases. Comparison of the results obtained from the neural networks and the values obtained from direct fitting of experimental data, indicated the reasonable prediction of cyclic strain-hardening exponent and cyclic strength coefficient, which are often used to characterize the cyclic deformation curve by a Ramberg-Osgood type equation.a i = network output value b = fatigue strength exponent b j = bias term associated with neuron j BHN = Brinell hardness c = fatigue ductility exponent E = modulus of elasticity f = nonlinear activation function K = cyclic strength coefficient MRE = mean relative error MSE = mean square error n = neuron n = cyclic strain-hardening exponent N = total number of training patterns p i = input signal generated for neuron i R = regression result RA% = percent reduction in area S u = ultimate tensile strength t i = target value w ji = weight from neuron i to neuron j Y j = output of neuron j ε = cyclic strain range ε/2 = cyclic strain amplitude Correspondence: R. Ghajar.