Molecular Dynamics Simulation Of Nanomanipulation And Closed-loop Control Of The Probe In SEM

Nanomanipulation is an important part of nanotechnology. It is an importantcontent of the micro/nano manufacturing science and technology research. It is ahot research field received extensive attention in international robotics andnanotechnology fields. Nanomanipulation system is playing an increasinglyimportant role in the micro/nano-scale materials research and devicemanufacturing process. Efficient, reliable and stable nanomanipulation systemhas broad application prospects in optoelectronics, information storage, andmedical fields. The process of nanowire translation in SEM is researched in thispaper. To control nanowire translation in reliable and stable, the interactionforces between the nano-components are modeled and analyzed. To improve thepositioning accuracy of the probe, The BP neural network PID control algorithmis applied for the closed-loop control of the probe.Based on the theoretical analysis of the micro-nanomanipulation, theinteraction force models between nano-components are established in the nano-manipulation process. Aimed at the possible rotating and bending phenomenon inthe nanowires parallel movement process, theoretical analysis is to be done. Forthe rotation phenomenon of the nanowire, the Z-operation strategy is applied.The molecular dynamics simulation software LAMMPS is used to establishthe molecular dynamics models of the nanowire translation procedure. Theobtained data are imported into the visualization software VMD. In the VMD, thenanowire trajectory and its physical phenomena in the process of translationoperation are observed visually. The nanowire bending and rotation phenomenonare observed. By analyzing the simulation results, the proposed Z-operationstrategy is verified.PZT nanopositioning manipulation system model is established. BP neuralnetwork PID control algorithm is applied for closed-loop control of the probe positioning system. Simulink models of the BP neural network PID controller areestablished. Through the analysis of simulation results, it can be obtained that thedesigned BP neural network PID controller can effectively reduce the track errorof the system, improve the tracking accuracy of the system and the controlperformance of the system.