A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

Variation Resilient Adaptive Controller for Subthreshold Circuits

Subthreshold logic is showing good promise as a viable ultra-low-power circuit design technique for power-limited applications. For this design technique to gain widespread adoption, one of the most pressing concerns is how to improve the robustness of subthreshold logic to process and temperature variations. We propose a variation resilient adaptive controller for subthreshold circuits with the following novel features: new sensor based on time-to-digital converter for capturing the variations accurately as digital signatures, and an all-digital DC-DC converter incorporating the sensor capable of generating an operating operating Vdd from 0V to 1.2V with a resolution of 18.75mV, suitable for subthreshold circuit operation. The benefits of the proposed controller is reflected with energy improvement of up to 55% compared to when no controller is employed. The detailed implementation and validation of the proposed controller is discussed

A 10-bit Charge-Redistribution ADC Consuming 1.9 μW at 1 MS/s

This paper presents a 10 bit successive approximation ADC in 65 nm CMOS that benefits from technology scaling. It meets extremely low power requirements by using a charge-redistribution DAC that uses step-wise charging, a dynamic two-stage comparator and a delay-line-based controller. The ADC requires no external reference current and uses only one external supply voltage of 1.0 V to 1.3 V. Its supply current is proportional to the sample rate (only dynamic power consumption). The ADC uses a chip area of approximately 115--225 μm2. At a sample rate of 1 MS/s and a supply voltage of 1.0 V, the 10 bit ADC consumes 1.9 μW and achieves an energy efficiency of 4.4 fJ/conversion-step

Smart-Pixel Cellular Neural Networks in Analog Current-Mode CMOS Technology

This paper presents a systematic approach to design CMOS chips with concurrent picture acquisition and processing capabilities. These chips consist of regular arrangements of elementary units, called smart pixels. Light detection is made with vertical CMOS-BJT’s connected in a Darlington structure. Pixel smartness is achieved by exploiting the Cellular Neural Network paradigm [1], [2], incorporating at each pixel location an analog computing cell which interacts with those of nearby pixels. We propose a current-mode implementation technique and give measurements from two 16 x 16 prototypes in a single-poly double-metal CMOS n-well 1.6-µm technology. In addition to the sensory and processing circuitry, both chips incorporate light-adaptation circuitry for automatic contrast adjustment. They obtain smart-pixel densities up to 89 units/mm2, with a power consumption down to 105 µW/unit and image processing times below 2 µs

Integrated phased array systems in silicon

Silicon offers a new set of possibilities and challenges for RF, microwave, and millimeter-wave applications. While the high cutoff frequencies of the SiGe heterojunction bipolar transistors and the ever-shrinking feature sizes of MOSFETs hold a lot of promise, new design techniques need to be devised to deal with the realities of these technologies, such as low breakdown voltages, lossy substrates, low-Q passives, long interconnect parasitics, and high-frequency coupling issues. As an example of complete system integration in silicon, this paper presents the first fully integrated 24-GHz eight-element phased array receiver in 0.18-μm silicon-germanium and the first fully integrated 24-GHz four-element phased array transmitter with integrated power amplifiers in 0.18-μm CMOS. The transmitter and receiver are capable of beam forming and can be used for communication, ranging, positioning, and sensing applications

Mask Programmable CMOS Transistor Arrays for Wideband RF Integrated Circuits

A mask programmable technology to implement RF and microwave integrated circuits using an array of standard 90-nm CMOS transistors is presented. Using this technology, three wideband amplifiers with more than 15-dB forward transmission gain operating in different frequency bands inside a 4-22-GHz range are implemented. The amplifiers achieve high gain-bandwidth products (79-96 GHz) despite their standard multistage designs. These amplifiers are based on an identical transistor array interconnected with application specific coplanar waveguide (CPW) transmission lines and on-chip capacitors and resistors. CPW lines are implemented using a one-metal-layer post-processing technology over a thick Parylene-N (15 mum ) dielectric layer that enables very low loss lines (~0.6 dB/mm at 20 GHz) and high-performance CMOS amplifiers. The proposed integration approach has the potential for implementing cost-efficient and high-performance RF and microwave circuits with a short turnaround time

ACE16K: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs

Today, with 0.18-μm technologies mature and stable enough for mixed-signal design with a large variety of CMOS compatible optical sensors available and with 0.09-μm technologies knocking at the door of designers, we can face the design of integrated systems, instead of just integrated circuits. In fact, significant progress has been made in the last few years toward the realization of vision systems on chips (VSoCs). Such VSoCs are eventually targeted to integrate within a semiconductor substrate the functions of optical sensing, image processing in space and time, high-level processing, and the control of actuators. The consecutive generations of ACE chips define a roadmap toward flexible VSoCs. These chips consist of arrays of mixed-signal processing elements (PEs) which operate in accordance with single instruction multiple data (SIMD) computing architectures and exhibit the functional features of CNN Universal Machines. They have been conceived to cover the early stages of the visual processing path in a fully-parallel manner, and hence more efficiently than DSP-based systems. Across the different generations, different improvements and modifications have been made looking to converge with the newest discoveries of neurobiologists regarding the behavior of natural retinas. This paper presents considerations pertaining to the design of a member of the third generation of ACE chips, namely to the so-called ACE16k chip. This chip, designed in a 0.35-μm standard CMOS technology, contains about 3.75 million transistors and exhibits peak computing figures of 330 GOPS, 3.6 GOPS/mm2 and 82.5 GOPS/W. Each PE in the array contains a reconfigurable computing kernel capable of calculating linear convolutions on 3×3 neighborhoods in less than 1.5 μs, imagewise Boolean combinations in less than 200 ns, imagewise arithmetic operations in about 5 μs, and CNN-like temporal evolutions with a time constant of about 0.5 μs. Unfortunately, the many ideas underlying the design of this chip cannot be covered in a single paper; hence, this paper is focused on, first, placing the ACE16k in the ACE chip roadmap and, then, discussing the most significant modifications of ACE16K versus its predecessors in the family.LOCUST IST2001—38 097VISTA TIC2003—09 817 - C02—01Office of Naval Research N000 140 210 88

Using Building Blocks to Design Analog Neuro-Fuzzy Controllers

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level—a prerequisite for increasing processor complexity and operation speed. Examples include a three-input, four-rule controller chip in 1.5-μm CMOS, single-poly, double-metal technology