An apparatus for handling microelectronic devices comprises a pick arm having a pick surface configured for receiving a microelectronic device thereon, drives for moving the pick arm and reorienting the pick surface in the X, Y and Z planes and about a horizontal rotational axis and a vertical rotational axis, and a sensor device carried by the pick arm and configured to detect at least one of at least one magnitude of force and at least one location of force applied between the pick surface and a structure contacted by the pick surface or a structure and a microelectronic device carried on the pick surface.

A sensor element includes, when three axes orthogonal to one another are set to be an A-axis, a B-axis, and a C-axis, first piezoelectric plate that is configured to have an X cut quartz crystal plate and outputs a charge in response to an external force along the A-axis direction, second piezoelectric plate that is configured to have a Y cut quartz crystal plate, is stacked in the A-axis direction with the first piezoelectric plate, and outputs a charge in response to the external force in the B-axis direction, and third piezoelectric plate that is configured to have a Y cut quartz crystal plate, is stacked in the A-axis direction so as to interpose the second piezoelectric plate between the first piezoelectric plate and the third piezoelectric plate and be arranged to turn around the A-axis, and outputs a charge in response to the external force in the C-axis direction.

Actuation devices and methods for using same are disclosed. The actuation device includes a first actuation layer including a plurality of first actuators operable within a first frequency range, and a second actuation layer provided on the first actuation layer, the second actuation layer including a plurality of second actuators operable within a second frequency range different from the first frequency range.

In embodiments a sensor includes a carrier material and an insulation layer.

A measuring gripper unit is disclosed having a base body, at least one guide element and at least one clamping device. The clamping device is arranged on the base body and configured to clamp a material web. The guide element is also located on the base body and configured to guide the measuring gripper unit on a guide rail. Furthermore, the measuring gripper unit includes a sensor device which is configured to capture measurement data during the operation of the measuring gripper unit 100 in a stretching unit.

An apparatus for collision avoidance by surface proximity detection includes a plurality of piezoelectric elements disposed adjacent to a surface of an object, a memory storing instructions, and at least one processor configured to execute the instructions to control a first one among the piezoelectric elements to generate an acoustic wave along the surface of the object, and receive, via a second one among the piezoelectric elements, an acoustic wave signal corresponding to the generated acoustic wave. The at least one processor is further configured to execute the instructions to filter the received acoustic wave signal, using a band-pass filter for reducing noise of the received acoustic wave signal, obtain a proximity signal for proximity detection, from the filtered acoustic wave signal, using a linear time-invariant filter, and detect whether an obstacle is proximate to the surface of the object by inputting the obtained proximity signal into a neural network.

An apparatus for collision avoidance by surface proximity detection includes a plurality of piezoelectric elements disposed adjacent to a surface of an object, a memory storing instructions, and at least one processor configured to execute the instructions to control a first one among the piezoelectric elements to generate an acoustic wave along the surface of the object, and receive, via a second one among the piezoelectric elements, an acoustic wave signal corresponding to the generated acoustic wave. The at least one processor is further configured to execute the instructions to filter the received acoustic wave signal, using a band-pass filter for reducing noise of the received acoustic wave signal, obtain a proximity signal for proximity detection, from the filtered acoustic wave signal, using a linear time-invariant filter, and detect whether an obstacle is proximate to the surface of the object by inputting the obtained proximity signal into a neural network.

A sensor (1) which consists of a first electrode (3a), a ferroelectric layer (2) and a second electrode (3b) is described. The second electrode (3b) is connected to ground and the ferroelectric layer (2) is arranged between the first and second electrodes (3a, 3b).

A robot end effector for gripping a workpiece, the robot end effector comprising a base portion and at least two fingers connected to the base portion, the fingers having opposed front sides. At least one of the fingers is movable toward and away from the other such that the workpiece can be gripped and released. At least one strain element is attached to a surface of at least one of the fingers, e.g., the active finger. Electrical circuitry is operative to provide power to the at least one strain element and receive strain signals detected by the at least one strain element. In some embodiments, one or more of the fingers is bifurcated, with one or more strain elements located on each of the bifurcated portions.

A tactile sensing system of a robot may include: a plurality of piezoelectric elements disposed at an object, and including a transmission (TX) piezoelectric element and a reception (RX) piezoelectric element; and at least one processor configured to: control the TX piezoelectric element to generate an acoustic wave having a chirp spread spectrum (CSS) at every preset time interval, along a surface of the object; receive, via the RX piezoelectric element, an acoustic wave signal corresponding to the generated acoustic wave; select frequency bands from a plurality of frequency bands of the acoustic wave signal; and estimate a location of a touch input on the surface of the object by inputting the acoustic wave signal of the selected frequency bands into a neural network configured to provide a touch prediction score for each of a plurality of predetermined locations on the surface of the object.