Micro-scale chiplet assembly control with chiplet-to-chiplet potential interaction
Micro-scale chiplet assembly control with chiplet-to-chiplet potential interaction
复制标题
具有小芯片间潜在相互作用的微尺度小芯片组装控制
DOI：
10.1109/cdc45484.2021.9683066
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Baras
中科院分区：
文献类型：
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作者：
Ion Matei;A. Plochowietz;J. Kleer;J. Baras
我们解决了同时控制浸入介电流体中的微观物体（小芯片）的问题。由数千个电极阵列形成的电场用于利用介电泳力传输和定位小芯片。我们对小芯片行为（包括小芯片与小芯片之间的交互）使用集总、二维、基于电容（非线性）的运动模型。使用高速相机和图像处理算法跟踪小芯片的位置。我们使用模型预测控制（MPC）方法来导出控制输入（即电极电位），将小芯片塑造成所需的图案。为了通过输入数量来缩放问题，控制输入被参数化为平滑的时间和空间相关函数，避免了将每个电极电势视为单独的控制输入的需要。我们使用自动微分来计算小芯片势能以及 MPC 损失和约束函数的梯度。我们在一个场景中展示了我们的方法，其中九个小芯片被运输到一组最终位置，而第十个小芯片保持静止。
We address the problem of simultaneous control of micro-objects (chiplets) immersed in dielectric fluid. An electric field, shaped by an array of thousands of electrodes, is used to transport and position chiplets using dielectrophoretic forces. We use a lumped, 2D, capacitive based (nonlinear) model of motion for the chiplets behavior that include chiplet to chiplet interactions. The chiplet positions are tracked using a high speed camera and image processing algorithms. We use a model predictive control (MPC) approach to derive control inputs (i.e., electrode potentials) that shape the chiplets into a desired pattern. To scale the problem with the number of inputs, the control inputs are parameterized as smooth time and space dependent functions avoiding the need to consider each electrode potential as a separate control input. We use automatic differentiation to compute gradients of the chiplet potential energy, and of the MPC loss and constrain functions. We demonstrate our approach on a scenario where nine chiplets are transported to a set a final positions while a tenth one is kept stationary.