Impact Ionization and Hot-Electron Injection Derived Consistently from Boltzmann Transport

We develop a quantitative model of the impact-ionizationand hot-electron–injection processes in MOS devices from first principles. We begin by modeling hot-electron transport in the drain-to-channel depletion region using the spatially varying Boltzmann transport equation, and we analytically find a self consistent distribution function in a two step process. From the electron distribution function, we calculate the probabilities of impact ionization and hot-electron injection as functions of channel current, drain voltage, and floating-gate voltage. We compare our analytical model results to measurements in long-channel devices. The model simultaneously fits both the hot-electron- injection and impact-ionization data. These analytical results yield an energydependent impact-ionization collision rate that is consistent with numerically calculated collision rates reported in the literature

On-chip compensation of device-mismatch effects in analog VLSI neural networks

Device mismatch in VLSI degrades the accuracy of analog arithmetic circuits and lowers the learning performance of large-scale neural networks implemented in this technology. We show compact, low-power on-chip calibration techniques that compensate for device mismatch. Our techniques enable large-scale analog VLSI neural networks with learning performance on the order of 10 bits. We demonstrate our techniques on a 64-synapse linear perceptron learning with the Least-Mean-Squares (LMS) algorithm, and fabricated in a 0.35µm CMOS process.

Floating-Gate MOS Synapse Transistors

Our goal is to develop silicon learning systems. One impediment to achieving this goal has been the lack of a simple circuit element combining nonvolatile analog memory storage with locally computed memory updates. Existing circuits [63, 132] typically are large and complex; the nonvolatile floating-gate devices, such as EEPROM transistors. typically are optimized for binary-valued storage [17], and do not compute their own memory updates. Although floating-gate transistors can provide nonvolatile analog storage [1, 15], because writing the memory entails the difficult process of moving electrons through Si0_2, these devices have not seen wide use as memory elements in silicon learning systems

The matching of small capacitors for analog VLSI

The capacitor has become the dominant passive component for analog circuits designed in standard CMOS processes. Thus, capacitor matching is a primary factor in determining the precision of many analog circuit techniques. In this paper, we present experimental measurements of the mismatch between square capacitors ranging in size from 6 μm×6 μm to 20 μm×20 μm fabricated in a standard 2 μm double-poly CMOS process available through MOSIS. For a size of 6 μm×6 μm, we have found that those capacitors that fell within one standard deviation of the mean matched to better than 1%. For the 20 μm×20 μm size, we observed that those capacitors that fell within 1 standard deviation of the mean matched to about 0.2%. Finally, we observed the effect of nonidentical surrounds on capacitor matching

Adaptation of Current Signals with Floating-Gate Circuits

In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron injection to add or remove charge on a floating-gate of an auto-zeroing amplifier. We designed, fabricated and successfully tested a chip with the circuit. Test results show that the circuit reduces the offsets by more than an order of magnitude

Scaling pFET Hot-Electron Injection

This paper' elaborates on a previously introduced [l] analytical model for hot-electron injection in pchannel MOSFET's. Hot-electron injection is frequently exploited to remove stored charge in floating-gate circuits. As illustrated in We present data from devices fabricated on processes with minimum channel lengths of 2pm, l p m , 0.5pm, 0.35pm, 0.25pm, O.lSpm, and 0.13pm, modeling these devices analytically in each process. While we can derive the mean free length between phonon collisions, A, using the well known energy of optical phonons in silicon [4], we must provide a theoretical model and experiniental verification of the energy dependence of the mean free lengths between impact-ionization events in the conduction and valence bands, & ( E ) and &+(E)

A νMOS soft-maximum current mirror

In this paper, we describe a novel circuit consisting of N+1 MOS transistors and a single floating gate which computes a soft maximum of N current inputs and reflects the result in the output transistor. An intuitive description of the operation of the circuit is given. Data from a working two-input version of the circuit is presented and discussed. The circuit features a high output voltage swing and an interesting feedback mechanism which causes its output impedance to be comparable to that of a normal MOS transistor despite the fact that the output device is a floating-gate transistor

A floating-gate MOS learning array with locally computed weight updates

We have demonstrated on-chip learning in an array of floating-gate MOS synapse transistors. The array comprises one synapse transistor at each node, and normalization circuitry at the row boundaries. The array computes the inner product of a column input vector and a stored weight matrix. The weights are stored as floating-gate charge; they are nonvolatile, but can increase when we apply a row-learn signal. The input and learn signals are digital pulses; column input pulses that are coincident with row-learn pulses cause weight increases at selected synapses. The normalization circuitry forces row synapses to compete for floating-gate charge, bounding the weight values. The array simultaneously exhibits fast computation and slow adaptation: The inner product computes in 10 μs, whereas the weight normalization takes minutes to hours

Competitive Learning With Floating-Gate Circuits

Competitive learning is a general technique for training clustering and classification networks. We have developed an 11-transistor silicon circuit, that we term an automaximizing bump circuit, that uses silicon physics to naturally implement a similarity computation, local adaptation, simultaneous adaptation and computation and nonvolatile storage. This circuit is an ideal building block for constructing competitive-learning networks. We illustrate the adaptive nature of the automaximizing bump in two ways. First, we demonstrate a silicon competitive-learning circuit that clusters one-dimensional (1-D) data. We then illustrate a general architecture based on the automaximizing bump circuit; we show the effectiveness of this architecture, via software simulation, on a general clustering task. We corroborate our analysis with experimental data from circuits fabricated in a 0.35-µm CMOS process