MEMS介绍个人和消费电子产品

　　Introducing MEMS to Personal and Consumer Electronics

　　作者：Nicholas Cravotta

　　Collecting sensor data is relatively straightforward; analyzing the data and using it in sophisticated consumer or medical applications is not. This article examines some of the challenges that arise when you introduce a simple sensor into a complex system.

　　The ability to accurately track motion using sensors based on MEMS （microelectromechanical systems） technology has completely transformed the health and fitness markets. MEMS technology is a natural fit for monitoring fitness as it can be embedded in shoes and clothing to track performance. What has caught many companies by surprise， however， is the number of niche applications in which MEMS technology can bring significant and unique value. For example， MEMS can be used to detect if an elderly person at home alone has fallen and can‘t get up or has stopped moving altogether. In both cases， a personal monitor can call for assistance over an RF link.

　　MEMS sensor technology is also quickly becoming an expected feature in portable and handheld consumer electronics devices， given the number of ways it can enhance usability. For example， the ability to sense tilt， rotation， and gestures is becoming a standard feature of gaming peripherals. Portable devices are using MEMS to determine in which direction to display text and images so that users can view them from the right angle. Advanced features such as dead reckoning can be used to provide local GPS functionality.

　　Historically， MEMS devices have been quite expensive. However， higher sales volumes continue to drive down cost， making them viable within even lower-cost systems. As a result， MEMS technology is being introduced for use in a wide variety of personal electronics applications， including medical monitors， training equipment， and PDAs/handsets.

　　One of the barriers to the rapid adoption of a technology is the ease with which developers can evaluate and design with the components based on that technology. Technologies that are difficult to work with， either because they only are available in a surface-mounted package or do not have available drivers or software， are less likely to be considered for serious evaluation. On the whole， MEMS devices are simple sensors. They come in small， standard packages， which make them easy to work with， and they are available with analog or digital interfaces. As the digital interfaces are typically I²C or SPI， any MCU can interface to them.

　　The primary challenges engineers face when designing with MEMS is not being able to integrate the sensor into a system or collect data. The first challenge is dealing with the potential software complexity associated with analyzing captured data. The second is correlating MEMS data with data from other sensors in the system. The third is being able to evaluate and develop MEMS technology under real-world operating conditions.

　　Multifaceted software challenges

　　Collecting positioning data — even in three dimensions — is a straightforward process. Using this data to

　　track the motion of a user’s arm or to simulate a localized GPS function using dead reckoning， however， requires some extraordinary engineering.

　　Both MEMS and MCU manufacturers understand the importance of providing software to accelerate design. Given that MEMS are， in essence， basic sensors， the low -level software required to access them is fairly simple. In fact， many MCU vendors provide libraries that can connect to a MEMS device and abstract operation through an API that provides logical access to sensor data without requiring system developers to have to delve into the hardware implementation details of the sensor.

　　This level of software support， however， is too low level for many applications since accessing MEMS data still requires a direct connection to the sensor. Open systems such as an Android -based phone really need OS-level drivers to proliferate use. The problem that arises is that various handsets may use a different sensor or implement it in a specific way that affects operation. The situation resembles one that plagued peripheral vendors in the early days of the PC where a driver might work on some systems but not others.

　　The final level of software support required to make MEMS truly a commodity technology is the availability of algorithmic software. Consider how access to device orientation has become a standard feature in portable device applications. Any application can take advantage of the reformatting of the display between landscape and portrait mode when the device is turned on its side. As functions such as localized positioning and motion recognition become standard features， more systems will be able to utilize these features to create wholly new applications. For example， a device could detect whether or not it is falling and protect against data corruption by suspending write functions in case of impact.

　　MCU vendors are recognizing that supporting MEMS is becoming increasingly important and that offering sensor drivers is as much a requirement as providing drivers for other peripherals. Some companies are already working on OS-level drivers. Texas Instruments， for example， already has some OS-level drivers for its OMAP and DaVinci architectures and anticipates bringing these drivers down to MCUs like the CC430 within one year. These drivers will support sensor interfacing and processing on a logical level so that the same application code and libraries can operate with different sensors. In addition， with an OS-level driver， any application running on the system could easily access the sensor just like any other system-managed resource.

　　Algorithmic software is also becoming a key focus. The more advanced algorithmic functions an MCU or MPU supports， the more value it brings to the system. Many of these algorithms， however， are non-trivial. Plotting x， y， z data is fairly simple （see Figure 1）。 Tracking position or speed is an order of higher complexity. Accurately recognizing even simple gestures is even more difficult.

　　The path for algorithmic software will likely follow the same path as video processing has taken over the years. Early on， processors could support video but developers had to write much of their own code. Third-party vendors began to offer specialized functions to accelerate design， and eventually these algorithms became part of the standard processor library. Today， developers have access to highly advanced video analytics such as object recognition and tracking.

　　Figure 1： The core data from a 3-D accelerator is x， y， z positioning data that is used to calculate more complex factors such as user speed or gestures.

　　MEMS technology is currently at the stage of providing engineers with the base tools to develop their own algorithms （see Figure 2）。 As market demand for advanced features continues to grow， these features will become available off-the-shelf. Today， however， developers will need to develop their own IP.