(Invited) Post-CMOS Compatible Silicon MEMS Nano-Tactile Sensor for Touch Feeling Discrimination of Materials

Semiconductor silicon has realized various kinds of valuable electron devices. CMOS technology is the present standard silicon process to fabricate integrated circuits and systems. Therefore, the word “Post-CMOS” has been an important word which means that the technology makes it possible to fabricate various kinds of MEMS sensors/actuators/passives with highly sophisticated modern CMOS devices. Also, it emphasizes that MEMS devices fabricated by post-CMOS technology has “high compatibility” with “silicon FABs” that are most universal and distributed fabrication facilities in the world. High performance devices and fine microstructures can be fabricated by universal post-CMOS fabrication processes at present. Post-CMOS compatible process is a key technology to realize ultra-high-performance silicon MEMS tactile sensors in this study. We, humans have very sophisticated sense of touch on our fingertip skin, and we can recognize and distinguish various and delicate difference of touch feelings obtained by “sweep motion” of fingertip on various kinds of materials and objects. Our fingertip skin has the highest density of force and vibration (mechanical) receptors like Meissner’s corpuscles and Merkel disks under the surface skin layer where fine pitch patterns of fingerprint are formed on. It is known that human’s fingertip has a very high spatial resolution below 100µm or less, and can recognize existence of 13nm-pitch patterns as reported recently. The high performance of our fingertip sense of touch has never been realized by any tactile sensor device. In order to reproduce artificial sense of touch like the fingertip, very high performances on “spatial resolution” and “input sensitivities” are required to integrate all on a small tactile sensor. Therefore, CMOS-compatible fabrication technology is the most suitable process to fabricate such high-performance tactile sensors with integrated functions. Based on a silicon post-CMOS compatible process, we have realized high performance silicon MEMS tactile sensor called “Nano-tactile sensor”, which is comparable to the performance of fingertip sense of touch. In this tactile sensor device, all the mechanical structures are made from single crystalline silicon which is the active layer of SOI wafers. No elastomer/polymer structures are used in the mechanical sensing structure, and softness and elasticity necessary to tactile sensing are realized by silicon micro mechanical structures. All the fine mechanical movements and sensing circuits with strain-sensitive diffusion resistors (i.e. piezoresistors) are designed by 2D-CAD, and the characteristics can be controlled by layout pattern sizes. The contactor parts of the tactile sensor have curved shape which is similarly designed to the cross-section of a fingerprint, and its suspension springs are designed to have similar spring constant with human’s fingertip skin surface. In the latest version of our “Nano-tactile sensors”, six contactors with fingerprint-like shape are integrated at a pitch of 500µm to get distributed tactile images at a high spatial resolution. The pitch of 500µm corresponds to the average value of human’s fingerprint patterns. Each fingerprint-like contactor reproduces vertical motion (by micro roughness) and horizontal motion (by frictional force) of a fingerprint closely under sweeping motion of fingertip in touch feeling measurement. Spatial resolution of our tactile sensor reaches to sub-micron (200nm) in the finest version, and its force resolution of input reaches below 50µN range. These high performances are enough high to reproduce part of our fingertip sense of touch. The Nano-tactile sensors can get touch feeling waveforms of “hair surface condition”, “skin texture and the condition”, and touch feelings of various kinds of “papers” and “clothes” at a high spatial resolution like our fingertip skin. In the lecture, experimental results of very high-resolution tactile sensing are presented, and their importance and novelty are discussed. Machine learning based on deep neural network (DNN) has become very important for sensing data analysis. We think DNN is very important to understand/reproduce human sense of touch since a trained DNN behaves similarly with human’s senses based on our experiences. Since a lot of sample data are required for such applications, we have developed a measurement system called “Touch-Feeling Scanner”. A number of tactile sensing data can be measured by the touch-feeling scanner integrating a Nano-tactile sensor device. Obtained signals with the scanner were applied to train a DNN for discrimination of different kinds of clothes. 10 kinds of cloth samples have been successfully discriminated at a correct percentage over 95% as an example. Combination of high-resolution Nano-tactile sensors and state-of-the-art machine learning (DNN) is a strong approach to reproduce human fingertip sensation for various valuable applications. Figure 1