Paul E. Hasler

A single-transistor silicon synapse

We have developed a new floating-gate silicon MOS transistor for analog learning applications. The memory storage is nonvolatile; hot-electron injection and electron tunneling permit bidirectional memory updates. Because these updates depend on both the stored memory value and the transistor terminal voltages, the synapse can implement a learning function. We have derived a memory-update rule from the physics of the tunneling and injection processes, and have investigated synapse learning in a prototype array. Unlike conventional EEPROM devices, the synapse allows simultaneous memory reading and writing. Synapse transistor arrays can therefore compute both the array output, and local memory updates, in parallel. The synapse is small, and typically is operated at subthreshold current levels; it will permit the development of dense, low-power silicon learning systems.

A CMOS programmable analog memory-cell array using floating-gate circuits

The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off-chip. Moving parameter storage on-chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip nonvolatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and hot-electron injection to program values. We have fabricated two versions of this design: one with an nFET injection mechanism and one with a pFET injection mechanism. With these designs, we achieve greater than 13-bit output precision with a 39-dB power-supply rejection ratio and no crosstalk between memory cells.

Compressive sensing on a CMOS separable transform image sensor

This paper demonstrates a computational image sensor capable of implementing compressive sensing operations. Instead of sensing raw pixel data, this image sensor projects the image onto a separable 2-D basis set and measures the corresponding expansion coefficients. The inner products are computed in the analog domain using a computational focal plane and an analog vector-matrix multiplier (VMM). This is more than mere postprocessing, as the processing circuity is integrated as part of the sensing circuity itself. We implement compressive imaging on the sensor by using pseudorandom vectors called noiselets for the measurement basis. This choice allows us to reconstruct the image from only a small percentage of the transform coefficients. This effectively compresses the image without any digital computation and reduces the throughput of the analog-to-digital converter (ADC). The reduction in throughput has the potential to reduce power consumption and increase the frame rate. The general architecture and a detailed circuit implementation of the image sensor are discussed. We also present experimental results that demonstrate the advantages of using the sensor for compressive imaging rather than more traditional coded imaging strategies.

Translinear circuits using subthreshold floating-gate MOS transistors

We describe a family of current-mode circuits with multiple inputs and multiple outputs whose output currents are products and/or quotients of powers of the input currents. These circuits are made up of multipleinput floating-gate MOS (FGMOS) transistors operating in the subthreshold regime. The powers are set by capacitor ratios; hence, they can be quite accurate. We analyze the general family of such circuits and present experimental data from several members that we fabricated in a standard 2μm double-poly p-well process through MOSIS.

An autozeroing floating-gate amplifier

We have developed a bandpass floating-gate amplifier that uses tunneling and pFET hot-electron injection to set its dc operating point adaptively. Because the hot-electron injection is an inherent part of the pFETs behavior, we obtain this adaptation with no additional circuitry. Because the gate currents are small, the circuit exhibits a high-pass characteristic with a cutoff frequency less than 1 Hz. The high-frequency cutoff is controlled electronically, as is done in continuous-time filters. We have derived analytical models that completely characterize the amplifier and that are in good agreement with experimental data for a wide range of operating conditions and input waveforms. This autozeroing floating-gate amplifier demonstrates how to use continuous-time floating-gate adaptation in amplifier design.

Large-scale field-programmable analog arrays for analog signal processing

Field-programmable analog arrays (FPAAs) provide a method for rapidly prototyping analog systems. Currently available commercial and academic FPAAs are typically based on operational amplifiers (or other similar analog primitives) with only a few computational elements per chip. While their specific architectures vary, their small sizes and often restrictive interconnect designs leave current FPAAs limited in functionality and flexibility. For FPAAs to enter the realm of large-scale reconfigurable devices such as modern field-programmable gate arrays (FPGAs), new technologies must be explored to provide area-efficient accurately programmable analog circuitry that can be easily integrated into a larger digital/mixed-signal system. Recent advances in the area of floating-gate transistors have led to a core technology that exhibits many of these qualities, and current research promises a digitally controllable analog technology that can be directly mated to commercial FPGAs. By leveraging these advances, a new generation of FPAAs is introduced in this paper that will dramatically advance the current state of the art in terms of size, functionality, and flexibility. FPAAs have been fabricated using floating-gate transistors as the sole programmable element, and the results of characterization and system-level experiments on the most recent FPAA are shown.

A Floating-Gate-Based Field-Programmable Analog Array

A field-programmable analog array (FPAA) with 32 computational analog blocks (CABs) and occupying 3 × 3 mm2 in 0.35-μm CMOS is presented. Each CAB has a wide variety of subcircuits ranging in granularity from multipliers and programmable offset wide-linear-range Gm blocks to nMOS and pMOS transistors. The programmable interconnects and circuit elements in the CAB are implemented using floating-gate (FG) transistors, the total number of which exceeds fifty thousand. Using FG devices eliminates the need for SRAM to store configuration bits since the switch stores its own configuration. This system exhibits significant performance enhancements over its predecessor in terms of achievable dynamic range (> 9 b of FG voltage) and speed (≈ 20 gates/s) of accurate FG current programming and isolation between ON and OFF switches. An improved routing fabric has been designed that includes nearest neighbor connections to minimize the penalty on bandwidth due to routing parasitic. A maximum bandwidth of 57 MHz through the switch matrix and around 5 MHz for a first-order low-pass filter is achievable on this chip, the limitation being a “program” mode switch that will be rectified in the next chip. Programming performance improved drastically by implementing the entire algorithm on-chip with an SPI digital interface. Measured results of the individual subcircuits and two system examples including an AM receiver and a speech processor are presented.

Floating Gate Synapses With Spike-Time-Dependent Plasticity

This paper describes a single transistor floating-gate synapse device that can be used to store a weight in a nonvolatile manner, compute a biological EPSP, and demonstrate biological learning rules such as Long-Term Potentiation, LTD, and spike-time dependent plasticity. We also describe a highly scalable architecture of a matrix of synapses to implement the described learning rules. Parameters for weight update in the 0.35 um process have been extracted and can be used to predict the change in weight based on time difference between pre- and post-synaptic spike times.

A high-resolution non-volatile analog memory cell

A 3-transistor non-volatile analog storage cell with 14 bits effective resolution and rail-to-rail buffered voltage output is presented. The memory, which consists of charge stored on a MOS transistor floating gate, is written by means of hot-electron injection and erased by means of gate oxide tunneling. The circuit allows simultaneous memory reading and writing; by writing the memory under feedback control, errors due to oxide mismatch or trapping can be nearly eliminated, Small size and low power consumption make the cell especially attractive for use in analog neural networks. The cell is fabricated in a 2 /spl mu/m n-well silicon Bi-CMOS process available from MOSIS,.

Compressive Sensing on a CMOS Separable-Transform Image Sensor

This paper demonstrates a computational image sensor capable of implementing compressive sensing operations. Instead of sensing raw pixel data, this image sensor projects the image onto a separable 2-D basis set and measures the corresponding expansion coefficients. The inner products are computed in the analog domain using a computational focal plane and an analog vector-matrix multiplier (VMM). This is more than mere postprocessing, as the processing circuity is integrated as part of the sensing circuity itself. We implement compressive imaging on the sensor by using pseudorandom vectors called noiselets for the measurement basis. This choice allows us to reconstruct the image from only a small percentage of the transform coefficients. This effectively compresses the image without any digital computation and reduces the throughput of the analog-to-digital converter (ADC). The reduction in throughput has the potential to reduce power consumption and increase the frame rate. The general architecture and a detailed circuit implementation of the image sensor are discussed. We also present experimental results that demonstrate the advantages of using the sensor for compressive imaging rather than more traditional coded imaging strategies.