Application and future trends of spinal cord stimulation

Neuropathic pain impacts 7-10% of the general population and seriously undermines quality of life despite available medications. Although initially approved to treat chronic neuropathic pain as an alternative to conventional medical management, spinal cord stimulation (SCS) is expanding its application prospect to the treatment for an assortment of indications including ischemic pain and neurodegenerative disorders, with new stimulation modalities, techniques, and electrode materials emerging every year. Despite its proven efficacy and cost-effectiveness when compared with the long-term application of insufficiently effective and potentially harmful medications, SCS is still largely neglected by pain physicians and neurosurgeons worldwide because of the exorbitant cost of the devices and possible complications. The mechanism of action, constituents and clinical applications, and performance of SCS are here reviewed, with a special focus on five indications amenable to SCS treatment, including failed back surgery syndrome (FBSS), complex regional pain syndrome (CRPS), painful diabetic neuropathy (PDN), critical limb ischemia (CLI) and Parkinson’s disease (PD). Among all the indications, only FBSS and CRPS have a mature application scenario, and SCS treatment for PDN has just recently been approved by FDA. The clinical study of more conditions that may benefit from SCS treatment, such as CLI and PD, is still underway. Market expectations and recent developments of SCS are further discussed to provide an outlook for the future trends of spinal cord stimulation

Designing Weakly Coupled Mems Resonators with Machine Learning-Based Method

We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis and prediction are validated with experimental data of coupled microcantilevers from a published work. As such, this newly proposed scheme could shed light upon the structural optimization methods for high-performance MEMS devices with high degree of design freedom

Customizing Mems Designs via Conditional Generative Adversarial Networks

We present a novel systematic MEMS structure design approach based on a 'deep conditional generative model'. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical ulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications

Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning

We present a systematic MEMS structural design approach via a "trial-and-error"learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial"to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology