Ultra Low Power Circuits for Internet of Things and Deep Learning Accelerator Design with In-Memory Computing

Collecting data from environment and converting gathered data into information is the key idea of Internet of Things (IoT). Miniaturized sensing devices enable the idea for many applications including health monitoring, industrial sensing, and so on. Sensing devices typically have small form factor and thus, low battery capacity, but at the same time, require long life time for continuous monitoring and least frequent battery replacement. This thesis introduces three analog circuit design techniques featuring ultra-low power consumption for such requirements: (1) An ultra-low power resistor-less current reference circuit, (2) A 110nW resistive frequency locked on-chip oscillator as a timing reference, (3) A resonant current-mode wireless power receiver and battery charger for implantable systems. Raw data can be efficiently transformed into useful information using deep learning. However deep learning requires tremendous amount of computation by its nature, and thus, an energy efficient deep learning hardware is highly demanded to fully utilize this algorithm in various applications. This thesis also presents a pulse-width based computation concept which utilizes in-memory computing of SRAM.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144173/1/myungjun_1.pd

A development of assistant surgical robot system based on surgical-operation-by-wire and hands-on-throttle-and-stick

BACKGROUND: Robot-assisted laparoscopic surgery offers several advantages compared with open surgery and conventional minimally invasive surgery. However, one issue that needs to be resolved is a collision between the robot arm and the assistant instrument. This is mostly caused by miscommunication between the surgeon and the assistant. To resolve this limitation, an assistant surgical robot system that can be simultaneously manipulated via a wireless controller is proposed to allow the surgeon to control the assistant instrument. METHODS: The system comprises two novel master interfaces (NMIs), a surgical instrument with a gripper actuated by a micromotor, and 6-axis robot arm. Two NMIs are attached to master tool manipulators of da Vinci research kit (dVRK) to control the proposed system simultaneously with patient side manipulators of dVRK. The developments of the surgical instrument and NMI are based on surgical-operation-by-wire concept and hands-on-throttle-and-stick concept from the earlier research, respectively. Tests for checking the accuracy, latency, and power consumption of the NMI are performed. The gripping force, reaction time, and durability are assessed to validate the surgical instrument. The workspace is calculated for estimating the clinical applicability. A simple peg task using the fundamentals of laparoscopic surgery board and an in vitro test are executed with three novice volunteers. RESULTS: The NMI was operated for 185 min and reflected the surgeon’s decision successfully with a mean latency of 132 ms. The gripping force of the surgical instrument was comparable to that of conventional systems and was consistent even after 1000 times of gripping motion. The reaction time was 0.4 s. The workspace was calculated to be 8397.4 cm(3). Recruited volunteers were able to execute the simple peg task within the cut-off time and successfully performed the in vitro test without any collision. CONCLUSIONS: Various experiments were conducted and it is verified that the proposed assistant surgical robot system enables collision-free and simultaneous operation of the dVRK’s robot arm and the proposed assistant robot arm. The workspace is appropriate for the performance of various kinds of surgeries. Therefore, the proposed system is expected to provide higher safety and effectiveness for the current surgical robot system

Investigation of the Probe-Factor Deconvolution Methods for Dynamic ESD Fields Measurements

Accurate measurements of electromagnetic fields are essential to analyze the radiated noise due to unwanted electrostatic discharge (ESD) events at electronic devices. Usually, to know the radiated noise by ESD events, the voltages induced at field probes are measured, and the fields are obtained from the voltage by de-convolving the probe factor. In this paper, the two probe-factor deconvolution methods are investigated and compared in the measurements of the fields induced by system-level ESD events

Measurement and Modeling of System-level ESD Noise Voltages in Real Mobile Products

Electromagnetic noise source of an integrated circuit (IC) can be approximately modeled with three dipole moments. To improve the previous dipole moments extraction method using 3 TEM cell measurements data, a new dipole moments extraction technique using 24 measurements data in a GTEM cell is proposed herein. The nonlinear least square (NLS) method is applied to accurately extract the three dipole moments including the relative phases of each dipole moment. The near- magnetic field patterns calculated using the improved dipole moments extraction method are compared with those using the previous method

Measurement and Analysis of Statistical IC Operation Errors in a Memory Module Due to System-Level ESD Noise

Voltage noise and operation errors in an integrated circuit (IC) due to electrostatic discharge (ESD) events were measured, validated, and analyzed in this paper. A simplified structure of a laptop personal computer and an IC with a D-type flip-flop were designed and manufactured for the experimental tests. Every signal input to the IC was simultaneously measured during the ESD tests, and validated with the simulated results using a full-wave solver and a simple circuit model. Next, SPICE simulations were conducted using the measured voltages with ESD tests. The output waveforms and the statistical occurrence ratios of the operation failures found from the SPICE simulations were compared with measured values. Furthermore, the effects of decoupling capacitors on the IC operation failures due to ESD were investigated

Autonomous Microsystems for Downhole Applications: Design Challenges, Current State, and Initial Test Results

This paper describes two platforms for autonomous sensing microsystems that are intended for deployment in chemically corrosive environments at elevated temperatures and pressures. Following the deployment period, the microsystems are retrieved, recharged, and interrogated wirelessly at close proximity. The first platform is the Michigan Micro Mote for High Temperature (M3HT), a chip stack 2.9 × 1.1 × 1.5 mm3 in size. It uses RF communications to support pre-deployment and post-retrieval functions, and it uses customized electronics to achieve ultralow power consumption, permitting the use of a chip-scale battery. The second platform is the Environmental Logging Microsystem (ELM). This system, which is 6.5 × 6.3 × 4.5 mm3 in size, uses the smallest suitable off-the-shelf electronic and battery components that are compatible with assembly on a flexible printed circuit board. Data are stored in non-volatile memory, permitting retrieval even after total power loss. Pre-deployment and post-retrieval functions are supported by optical communication. Two types of encapsulation methods are used to withstand high pressure and corrosive environments: an epoxy filled volume is used for the M3HT, and a hollow stainless-steel shell with a sapphire lid is used for both the M3HT and ELM. The encapsulated systems were successfully tested at temperature and pressure reaching 150 °C and 10,000 psi, in environments of concentrated brine, oil, and cement slurry. At elevated temperatures, the limited lifetimes of available batteries constrain the active deployment period to several hours

Application of a Perception Neuron^® System in Simulation-Based Surgical Training

While multiple studies show that simulation methods help in educating surgical trainees, few studies have focused on developing systems that help trainees to adopt the most effective body motions. This is the first study to use a Perception Neuron® system to evaluate the relationship between body motions and simulation scores. Ten medical students participated in this study. All completed two standard tasks with da Vinci Skills Simulator (dVSS) and five standard tasks with thyroidectomy training model. This was repeated. Thyroidectomy training was conducted while participants wore a perception neuron. Motion capture (MC) score that indicated how long the tasks took to complete and each participant’s economy-of-motion that was used was calculated. Correlations between the three scores were assessed by Pearson’s correlation analyses. The 20 trials were categorized as low, moderate, and high overall-proficiency by summing the training model, dVSS, and MC scores. The difference between the low and high overall-proficiency trials in terms of economy-of-motion of the left or right hand was assessed by two-tailed t-test. Relative to cycle 1, the training model, dVSS, and MC scores all increased significantly in cycle 2. Three scores correlated significantly with each other. Six, eight, and six trials were classified as low, moderate, and high overall-proficiency, respectively. Low- and high-scoring trials differed significantly in terms of right (dominant) hand economy-of-motion (675.2 mm and 369.4 mm, respectively) (p = 0.043). Perception Neuron® system can be applied to simulation-based training of surgical trainees. The motion analysis score is related to the traditional scoring system